You need to build a chatbot that can generate natural, human-like responses and maintain context across multiple user interactions. Which Azure service should you use?
A. Azure AI Language B. Azure AI Speech C. Azure OpenAI Service D. Azure AI Vision
Correct Answer: C
Explanation: Azure OpenAI Service provides large language models capable of multi-turn conversational AI. Azure AI Language supports traditional NLP tasks but not advanced generative conversations.
Question 2
Which feature of Azure OpenAI Service enables semantic search by representing text as numerical vectors?
A. Prompt engineering B. Text completion C. Embeddings D. Tokenization
Correct Answer: C
Explanation: Embeddings convert text into vectors that capture semantic meaning, enabling similarity search and retrieval-augmented generation (RAG).
Question 3
An organization wants to generate summaries of long internal documents while ensuring their data is not used to train public models. Which service meets this requirement?
A. Open-source LLM hosted on a VM B. Azure AI Language C. Azure OpenAI Service D. Azure Cognitive Search
Correct Answer: C
Explanation: Azure OpenAI ensures customer data isolation and does not use customer data to retrain models, making it suitable for enterprise and regulated environments.
Question 4
Which type of workload is Azure OpenAI Service primarily designed to support?
A. Predictive analytics B. Generative AI C. Rule-based automation D. Image preprocessing
Correct Answer: B
Explanation: Azure OpenAI focuses on generative AI workloads, including text generation, conversational AI, code generation, and embeddings.
Question 5
A developer wants to build an AI assistant that can explain code, generate new code snippets, and translate code between programming languages. Which Azure service should be used?
A. Azure AI Language B. Azure Machine Learning C. Azure OpenAI Service D. Azure AI Vision
Correct Answer: C
Explanation: Azure OpenAI supports code-capable large language models designed for code generation, explanation, and translation.
Question 6
Which Azure OpenAI capability is MOST useful for building retrieval-augmented generation (RAG) solutions?
A. Chat completion B. Embeddings C. Image generation D. Speech synthesis
Correct Answer: B
Explanation: RAG solutions rely on embeddings to retrieve relevant content based on semantic similarity before generating responses.
Question 7
Which security feature is a key benefit of using Azure OpenAI Service instead of public OpenAI endpoints?
A. Anonymous access B. Built-in image labeling C. Azure Active Directory integration D. Automatic data labeling
Correct Answer: C
Explanation: Azure OpenAI integrates with Azure Active Directory and RBAC, providing enterprise-grade authentication and access control.
Question 8
A solution requires generating marketing copy, summarizing customer feedback, and answering user questions in natural language. Which Azure service best supports all these requirements?
A. Azure AI Language B. Azure OpenAI Service C. Azure AI Vision D. Azure AI Search
Correct Answer: B
Explanation: Azure OpenAI excels at generating and transforming text using large language models, covering all described scenarios.
Question 9
Which statement BEST describes how Azure OpenAI Service handles customer data?
A. Customer data is used to retrain models globally B. Customer data is publicly accessible C. Customer data is isolated and not used for model training D. Customer data is stored permanently without controls
Correct Answer: C
Explanation: Azure OpenAI ensures data isolation and does not use customer prompts or responses to retrain foundation models.
Question 10
When should you choose Azure OpenAI Service instead of Azure AI Language?
A. When performing key phrase extraction B. When detecting named entities C. When generating original text or conversational responses D. When identifying sentiment polarity
Correct Answer: C
Explanation: Azure AI Language is designed for traditional NLP tasks, while Azure OpenAI is used for generative AI tasks such as text generation and conversational AI.
Final Exam Tip
If the scenario involves creating new content, chatting naturally, generating code, or semantic understanding at scale, the correct answer is likely related to Azure OpenAI Service.
The Azure OpenAI Service provides access to powerful OpenAI large language models (LLMs)—such as GPT models—directly within the Microsoft Azure cloud environment. It enables organizations to build generative AI applications while benefiting from Azure’s security, compliance, governance, and enterprise integration capabilities.
For the AI-900 exam, Azure OpenAI is positioned as Microsoft’s primary service for generative AI workloads, especially those involving text, code, and conversational AI.
What Is Azure OpenAI Service?
Azure OpenAI Service allows developers to deploy, customize, and consume OpenAI models using Azure-native tooling, APIs, and security controls.
Key characteristics:
Hosted and managed by Microsoft Azure
Provides enterprise-grade security and compliance
Uses REST APIs and SDKs
Integrates seamlessly with other Azure services
👉 On the exam, Azure OpenAI is the correct answer when a scenario describes generative AI powered by large language models.
Core Capabilities of Azure OpenAI Service
1. Access to Large Language Models (LLMs)
Azure OpenAI provides access to advanced models such as:
GPT models for text generation and understanding
Chat models for conversational AI
Embedding models for semantic search and retrieval
Code-focused models for programming assistance
These models can:
Generate human-like text
Answer questions
Summarize content
Write code
Explain concepts
Generate creative content
2. Text and Content Generation
Azure OpenAI can generate:
Articles, emails, and reports
Chatbot responses
Marketing copy
Knowledge base answers
Product descriptions
Exam tip: If the question mentions writing, summarizing, or generating text, Azure OpenAI is likely the answer.
3. Conversational AI (Chatbots)
Azure OpenAI supports natural, multi-turn conversations, making it ideal for:
Customer support chatbots
Virtual assistants
Internal helpdesk bots
AI copilots
These chatbots:
Maintain conversation context
Generate natural responses
Can be grounded in enterprise data
4. Code Generation and Assistance
Azure OpenAI can:
Generate code snippets
Explain existing code
Translate code between languages
Assist with debugging
This makes it valuable for developer productivity tools and AI-assisted coding scenarios.
5. Embeddings and Semantic Search
Azure OpenAI can create vector embeddings that represent the meaning of text.
Use cases include:
Semantic search
Document similarity
Recommendation systems
Retrieval-augmented generation (RAG)
Exam tip: If the scenario mentions searching based on meaning rather than keywords, think embeddings + Azure OpenAI.
6. Enterprise Security and Compliance
One of the most important exam points:
Azure OpenAI provides:
Data isolation
No training on customer data
Azure Active Directory integration
Role-Based Access Control (RBAC)
Compliance with Microsoft standards
This makes it suitable for regulated industries.
7. Integration with Azure Services
Azure OpenAI integrates with:
Azure AI Foundry
Azure AI Search
Azure Machine Learning
Azure App Service
Azure Functions
Azure Logic Apps
This allows organizations to build end-to-end generative AI solutions within Azure.
Common Use Cases Tested on AI-900
You should associate Azure OpenAI with:
Chatbots and conversational agents
Text generation and summarization
AI copilots
Semantic search
Code generation
Enterprise generative AI solutions
Azure OpenAI vs Other Azure AI Services (Exam Perspective)
Service
Primary Focus
Azure OpenAI
Generative AI using large language models
Azure AI Language
Traditional NLP (sentiment, entities, key phrases)
Azure AI Vision
Image analysis and OCR
Azure AI Speech
Speech-to-text and text-to-speech
Azure AI Foundry
End-to-end generative AI app lifecycle
Key Exam Takeaways
For AI-900, remember:
Azure OpenAI = Generative AI
Best for text, chat, code, and embeddings
Enterprise-ready with security and compliance
Uses pre-trained OpenAI models
Integrates with the broader Azure ecosystem
One-Line Exam Rule
If the question describes generating new content using large language models in Azure, the answer is likely related to Azure OpenAI Service.
What is the primary purpose of the Azure AI Foundry model catalog?
A. To store training datasets for Azure Machine Learning B. To centrally discover, compare, and deploy AI models C. To monitor AI model performance in production D. To automatically fine-tune all deployed models
✅ Correct Answer:B
Explanation: The Azure AI Foundry model catalog is a centralized repository that allows users to discover, evaluate, compare, and deploy AI models from Microsoft and partner providers. It is not primarily used for dataset storage or monitoring.
Question 2
Which types of models are available in the Azure AI Foundry model catalog?
A. Only Microsoft-built models B. Only open-source community models C. Models from Microsoft and multiple third-party providers D. Only models trained within Azure Machine Learning
✅ Correct Answer:C
Explanation: The model catalog includes models from Microsoft, OpenAI, Meta, Anthropic, Cohere, and other partners, giving users access to a diverse range of generative and AI models.
Question 3
Which feature helps users compare models within the Azure AI Foundry model catalog?
A. Azure Cost Management B. Model leaderboards and benchmarking C. AutoML pipelines D. Feature engineering tools
✅ Correct Answer:B
Explanation: The model catalog includes leaderboards and benchmark metrics, allowing users to compare models based on performance characteristics and suitability for specific tasks.
Question 4
What information is typically included in a model card in the Azure AI Foundry model catalog?
A. Only pricing details B. Only deployment scripts C. Metadata such as capabilities, limitations, and licensing D. Only training dataset information
✅ Correct Answer:C
Explanation: Model cards provide descriptive metadata, including model purpose, supported tasks, licensing terms, and usage considerations, helping users make informed decisions.
Question 5
Which deployment option allows you to consume a model without managing infrastructure?
A. Managed compute B. Dedicated virtual machines C. Serverless API deployment D. On-premises deployment
✅ Correct Answer:C
Explanation: Serverless API deployment (Models-as-a-Service) allows users to call models via APIs without managing underlying infrastructure, making it ideal for rapid development and scalability.
Question 6
What is a key benefit of having search and filtering in the model catalog?
A. It automatically selects the best model B. It restricts models to one provider C. It helps users quickly find models that match specific needs D. It enforces Responsible AI policies
✅ Correct Answer:C
Explanation: Search and filtering features allow users to narrow down models based on capabilities, provider, task type, and deployment options, speeding up model selection.
Question 7
Which AI workload is the Azure AI Foundry model catalog most closely associated with?
A. Traditional rule-based automation B. Predictive analytics dashboards C. Generative AI solutions D. Network security monitoring
✅ Correct Answer:C
Explanation: The model catalog is a core capability supporting generative AI workloads, such as text generation, chat, summarization, and multimodal applications.
Question 8
Why might an organization choose managed compute instead of a serverless API deployment?
A. To avoid version control B. To reduce accuracy C. To gain more control over performance and resources D. To eliminate licensing requirements
✅ Correct Answer:C
Explanation: Managed compute provides greater control over performance, scaling, and resource allocation, which can be important for predictable workloads or specialized use cases.
Question 9
Which scenario best illustrates the use of the Azure AI Foundry model catalog?
A. Writing SQL queries for data analysis B. Comparing multiple large language models before deployment C. Creating Power BI dashboards D. Training image classification models from scratch
✅ Correct Answer:B
Explanation: The model catalog is designed to help users evaluate and compare models before deploying them into generative AI applications.
Question 10
For the AI-900 exam, which statement best describes the Azure AI Foundry model catalog?
A. A low-level training engine for custom neural networks B. A centralized hub for discovering and deploying AI models C. A compliance auditing tool D. A replacement for Azure Machine Learning
✅ Correct Answer:B
Explanation: For AI-900, the key takeaway is that the model catalog acts as a central hub that simplifies model discovery, comparison, and deployment within Azure’s generative AI ecosystem.
🔑 Exam Tip
If an AI-900 question mentions:
Choosing between multiple generative models
Evaluating model performance or benchmarks
Using models from different providers in Azure
👉 The correct answer is very likely related to the Azure AI Foundry model catalog.
The Azure AI Foundry model catalog (also known as Microsoft Foundry Models) is a centralized, searchable repository of AI models that developers and organizations can use to build generative AI solutions on Azure. It contains hundreds to thousands of models from multiple providers — including Microsoft, OpenAI, Anthropic, Meta, Cohere, DeepSeek, NVIDIA, and more — and provides tools to explore, compare, and deploy them for various AI workloads.
The model catalog is a key feature of Azure AI Foundry because it lets teams discover and evaluate the right models for specific tasks before integrating them into applications.
Key Capabilities of the Model Catalog
🌐 1. Wide and Diverse Model Selection
The catalog includes a broad set of models, such as:
Large language models (LLMs) for text generation and chat
Domain-specific models for legal, medical, or industry tasks
Multimodal models that handle text + images
Reasoning and specialized task models These models come from multiple providers including Microsoft, OpenAI, Anthropic, Meta, Mistral AI, and more.
This diversity ensures that developers can find models that fit a wide range of use cases, from simple text completion to advanced multi-agent workflows.
🔍 2. Search and Filtering Tools
The model catalog provides tools to help you find the right model by:
Keyword search
Provider and collection filters
Filtering by capabilities (e.g., reasoning, tool calling)
Deployment type (e.g., serverless API vs managed compute)
Inference and fine-tune task types
Industry or domain tags
These filters make it easier to match models to specific AI workloads.
📊 3. Comparison and Benchmarking
The catalog includes features like:
Model performance leaderboards
Benchmark metrics for selected models
Side-by-side comparison tools
This lets organizations evaluate and compare models based on real-world performance metrics before deployment.
This is especially useful when choosing between models for accuracy, cost, or task suitability.
📄 4. Model Cards with Metadata
Each model in the catalog has a model card that provides:
Quick facts about the model
A description
Version and supported data types
Licenses and legal information
Benchmark results (if available)
Deployment status and options
Model cards help users understand model capabilities, constraints, and appropriate use cases.
🚀 5. Multiple Deployment Options
Models in the Foundry catalog can be deployed using:
Serverless API: A “Models as a Service” approach where the model is hosted and managed by Azure, and you pay per API call
Managed compute: Dedicated virtual machines for predictable performance and long-running applications
This gives teams flexibility in choosing cost and performance trade-offs.
⚙️ 6. Integration and Customization
The model catalog isn’t just for discovery — it also supports:
Fine-tuning of models based on your data
Custom deployments within your enterprise environment
Integration with other Azure tools and services, like Azure AI Foundry deployment workflows and AI development tooling
This makes the catalog a foundational piece of end-to-end generative AI development on Azure.
Model Categories in the Catalog
The model catalog is organized into key categories such as:
Models sold directly by Azure: Models hosted and supported by Microsoft with enterprise-grade integration, support, and compliant terms.
Partner and community models: Models developed by external organizations like OpenAI, Anthropic, Meta, or Cohere. These often extend capabilities or offer domain-specific strengths.
This structure helps teams select between fully supported enterprise models and innovative third-party models.
Scenarios Where You Would Use the Model Catalog
The Azure AI Foundry model catalog is especially useful when:
Exploring models for text generation, chat, summarization, or reasoning
Comparing multiple models for accuracy vs cost
Deploying models in different formats (serverless API vs compute)
Integrating models from multiple providers in a single AI pipeline
It is a central discovery and evaluation hub for generative AI on Azure.
How This Relates to AI-900
For the AI-900 exam, you should understand:
The model catalog is a core capability of Azure AI Foundry
It allows discovering, comparing, and deploying models
It supports multiple model providers
It offers deployment options and metadata to guide selection
If a question mentions finding the right generative model for a use case, evaluating model performance, or using a variety of models in Azure, then the Azure AI Foundry model catalog is likely being described.
Summary (Exam Highlights)
Azure AI Foundry model catalog provides discoverability for thousands of AI models.
Models can be filtered, compared, and evaluated.
Catalog entries include useful metadata (model cards) and benchmarking.
Models come from Microsoft and partner providers like OpenAI, Anthropic, Meta, etc.
Deployment options vary between serverless APIs and managed compute.
Practice Exam 1 – 60 Questions (with Answer key at the end)
Note: The exam is separated into topic sections to help with context and preparation, but the real exam will not be like that.
SECTION 1: Describe Artificial Intelligence workloads and considerations (Questions 1–10)
Question 1 (Single choice)
Which scenario is the best example of an AI workload?
A. A rules-based system that routes emails based on keywords B. A dashboard that displays historical sales data C. A system that predicts customer churn based on historical behavior D. A script that automatically renames files
Question 2 (Multi-select – Choose TWO)
Which characteristics are commonly associated with AI solutions?
A. Deterministic outputs B. Ability to improve with experience C. Dependence on labeled or unlabeled data D. Use of static business rules
Question 3 (Scenario – Single choice)
A company wants to automatically approve or reject loan applications based on past decisions and applicant attributes. Which AI workload type does this represent?
A. Computer vision B. Anomaly detection C. Classification D. Natural language processing
Question 4 (Matching)
Match each AI workload to its correct description:
AI Workload
Description
1. Classification
A. Identify unusual patterns
2. Regression
B. Assign items to categories
3. Clustering
C. Group similar items without labels
4. Anomaly detection
D. Predict numeric values
Question 5 (Single choice)
Which factor is most important when evaluating the ethical impact of an AI solution?
A. Processing speed B. Model size C. Potential bias in training data D. Storage cost
Question 6 (Scenario – Single choice)
An AI system used for hiring consistently favors one demographic group. Which Responsible AI principle is most directly violated?
A. Reliability B. Transparency C. Fairness D. Privacy
Question 7 (Multi-select – Choose TWO)
Which scenarios would typically require human oversight when deploying AI solutions?
A. Medical diagnosis recommendations B. Image resizing C. Credit approval decisions D. Log file compression
Question 8 (Fill in the blank)
The ability for users to understand how an AI model makes decisions relates to the principle of __________.
Question 9 (Single choice)
Which workload is best suited for predicting future sales revenue?
A. Classification B. Regression C. Clustering D. Object detection
Question 10 (Scenario – Single choice)
A system groups customers into segments without predefined labels. Which AI approach is being used?
A. Supervised learning B. Reinforcement learning C. Unsupervised learning D. Deep learning
SECTION 2: Describe fundamental principles of machine learning on Azure (Questions 11–20)
Question 11 (Single choice)
Which Azure service is primarily used to build, train, and deploy machine learning models?
A. Azure AI Vision B. Azure Machine Learning C. Azure OpenAI D. Azure Cognitive Search
Question 12 (Multi-select – Choose TWO)
Which elements are required to train a supervised machine learning model?
A. Labeled data B. Feature engineering C. Pretrained transformers D. Inference endpoints
Question 13 (Scenario – Single choice)
You want to predict house prices based on size, location, and age. Which type of machine learning model should you use?
A. Classification B. Regression C. Clustering D. Anomaly detection
Question 14 (Single choice)
Which term describes input variables used by a machine learning model?
Below are 60 questions. The questions are broken up into topic sections to help with context and preparation. The real exam is not like that.
Section 1: Describe Artificial Intelligence workloads and considerations (Q1–Q10)
Q1. A city wants to automatically adjust traffic light timing based on real‑time vehicle congestion detected from sensors. Which type of AI workload is MOST appropriate?
A. Classification
B. Anomaly detection
C. Prediction and optimization
D. Computer vision
Q2. (Multi‑select) Which characteristics distinguish AI solutions from traditional software? (Choose two.)
A. Deterministic logic paths
B. Ability to learn from data
C. Adaptation over time
D. Manual rule updates only
Q3. An application analyzes medical images to identify whether a tumor is benign or malignant. Which AI workload is this?
A. Regression
B. Clustering
C. Classification
D. Forecasting
Q4. (Matching) Match the Responsible AI principle to its description.
Principle
Description
1. Reliability & Safety
A. Protects personal and sensitive data
2. Privacy & Security
B. Ensures consistent and dependable performance
3. Transparency
C. Explains how decisions are made
Q5. Why is explainability especially important in AI systems used for healthcare decisions?
A. It improves system performance
B. It reduces infrastructure costs
C. It builds trust and supports accountability
D. It eliminates the need for human oversight
Q6. An AI model performs well in testing but fails frequently in real‑world use. Which Responsible AI principle is MOST impacted?
A. Fairness
B. Transparency
C. Reliability & safety
D. Inclusiveness
Q7. (Multi‑select) Which scenarios require human‑in‑the‑loop decision making? (Choose two.)
A. Automated photo tagging
B. Credit approval systems
C. Medical diagnosis support
D. Spam email filtering
Q8. Fill in the blank: An AI system that ensures users understand why a specific output was generated is demonstrating __________.
Q9. A retailer predicts next month’s total revenue using historical sales data. What AI workload does this represent?
A. Classification
B. Regression
C. Clustering
D. Anomaly detection
Q10. Which concern arises when an AI system unintentionally favors one demographic group over others?
A. Reliability
B. Bias
C. Security
D. Performance
Section 2: Describe fundamental principles of machine learning on Azure (Q11–Q20)
Q11. Which Azure service is designed to build, train, and deploy machine learning models at scale?
A. Azure AI Vision
B. Azure Machine Learning
C. Azure OpenAI
D. Azure AI Language
Q12. (Multi‑select) Which components are required for supervised learning? (Choose two.)
A. Labeled data
B. Features
C. Unlabeled datasets
D. Prompt templates
Q13. A model predicts the number of support tickets expected per day. Which ML task is this?
A. Classification
B. Regression
C. Clustering
D. Ranking
Q14. In machine learning, what is a feature?
A. The predicted output
B. An input variable
C. A training algorithm
D. A deployment endpoint
Q15. (Matching) Match the learning type to the scenario.
Learning Type
Scenario
1. Supervised
A. Grouping customers by behavior
2. Unsupervised
B. Predicting house prices
3. Reinforcement
C. Training a robot using rewards
Q16. Which problem occurs when a model memorizes training data but performs poorly on new data?
A. Underfitting
B. Overfitting
C. Bias
D. Drift
Q17. Which metric is MOST commonly used to evaluate classification models?
A. RMSE
B. Accuracy
C. MAE
D. R²
Q18. Why is data split into training and test sets?
A. To reduce storage requirements
B. To improve inference speed
C. To evaluate generalization
D. To eliminate bias
Q19. Which Azure ML capability allows building models without writing code?
A. Jupyter notebooks
B. Azure ML designer
C. REST endpoints
D. Pipelines
Q20. Fill in the blank: Using a trained model to make predictions on new data is called __________.
Section 3: Describe features of computer vision workloads on Azure (Q21–Q30)
Q21. Which Azure service provides image analysis, OCR, and object detection?
A. Azure AI Language
B. Azure AI Vision
C. Azure AI Speech
D. Azure Machine Learning
Q22. A solution identifies people and vehicles in security footage and draws bounding boxes around them. What vision capability is required?
A. Image classification
B. Face recognition
C. Object detection
D. OCR
Q23. (Multi‑select) Which tasks are computer vision workloads? (Choose two.)
A. Image tagging
B. Sentiment analysis
C. OCR
D. Language translation
Q24. Extracting printed text from scanned invoices is an example of:
A. Object detection
B. OCR
C. Image segmentation
D. Face analysis
Q25. Which capability identifies the emotional attributes of a face in an image?
A. OCR
B. Face analysis
C. Image classification
D. Object detection
Q26. (Matching) Match the vision task to the description.
Task
Description
1. Image classification
A. Detects text in images
2. OCR
B. Assigns a label to an entire image
3. Object detection
C. Locates objects with bounding boxes
Q27. Which scenario is NOT a computer vision workload?
A. Counting people in a store
B. Detecting defects in products
C. Converting speech to text
D. Reading license plates
Q28. (Multi‑select) What are common concerns with facial recognition systems? (Choose two.)
A. Privacy
B. Bias
C. Cost optimization
D. Network latency
Q29. Which Azure service supports OCR for handwritten text?
A. Azure Machine Learning
B. Azure AI Vision
C. Azure OpenAI
D. Azure AI Speech
Q30. Fill in the blank: Identifying the location and category of multiple objects in an image is called __________.
Section 4: Describe features of NLP workloads on Azure (Q31–Q40)
Q31. Which Azure service provides sentiment analysis, entity recognition, and key phrase extraction?
A. Azure AI Vision
B. Azure AI Language
C. Azure OpenAI
D. Azure AI Speech
Q32. An application determines whether customer feedback is positive, negative, or neutral. What NLP task is this?
A. Translation
B. Entity recognition
C. Sentiment analysis
D. Language modeling
Q33. (Multi‑select) Which tasks fall under NLP workloads? (Choose two.)
A. Key phrase extraction
B. Named entity recognition
C. Image tagging
D. Object detection
Q34. What is tokenization in NLP?
A. Translating text
B. Breaking text into smaller units
C. Assigning sentiment scores
D. Detecting entities
Q35. Identifying names of people, places, and organizations in text is known as:
A. Translation
B. Sentiment analysis
C. Entity recognition
D. Language detection
Q36. (Matching) Match the NLP task to the scenario.
Task
Scenario
1. Translation
A. Detects emotional tone
2. Sentiment analysis
B. Converts text between languages
3. Key phrase extraction
C. Summarizes main topics
Q37. Which Azure service converts spoken language into text?
A. Azure AI Vision
B. Azure AI Language
C. Azure AI Speech
D. Azure OpenAI
Q38. (Multi‑select) Which use cases are appropriate for speech synthesis? (Choose two.)
A. Voice assistants
B. Image labeling
C. Accessibility tools
D. Object detection
Q39. Fill in the blank: Detecting the language of a document is a __________ task.
Q40. Which Azure service supports both speech‑to‑text and text‑to‑speech?
A. Azure AI Vision
B. Azure AI Language
C. Azure AI Speech
D. Azure Machine Learning
Section 5: Describe features of generative AI workloads on Azure (Q41–Q60)
Q41. What distinguishes generative AI from predictive ML?
A. It only classifies data
B. It creates new content
C. It requires no training data
D. It cannot use text input
Q42. Large language models are primarily trained on:
A. Structured tables only
B. Images
C. Massive text datasets
D. Sensor data
Q43. (Multi‑select) Which are common generative AI use cases? (Choose two.)
A. Text summarization
B. Image generation
C. Fraud detection
D. Forecasting
Q44. Which Azure service provides access to GPT‑based models?
A. Azure AI Language
B. Azure Machine Learning
C. Azure OpenAI
D. Azure AI Vision
Q45. A chatbot that answers questions using natural language is an example of:
A. Computer vision
B. Predictive ML
C. Generative AI
D. Rule‑based automation
Q46. (Matching) Match the concept to its description.
Concept
Description
1. Prompt
A. AI‑generated incorrect content
2. Hallucination
B. Input provided to a model
3. Grounding
C. Using trusted data sources
Q47. What is prompt engineering?
A. Training new models
B. Designing effective inputs
C. Deploying endpoints
D. Cleaning datasets
Q48. (Multi‑select) Which Responsible AI considerations apply to generative AI? (Choose two.)
A. Content safety
B. Bias mitigation
C. Image resolution
D. Compute scaling
Q49. Which technique helps reduce hallucinations by referencing verified information?
A. Fine‑tuning
B. Grounding
C. Tokenization
D. Sampling
Q50. Fill in the blank: When a generative AI model produces confident but incorrect outputs, it is known as __________.
Q51. Which Azure platform helps manage, evaluate, and deploy generative AI solutions responsibly?
A. Azure Machine Learning
B. Azure AI Foundry
C. Azure AI Vision
D. Azure AI Language
Q52. What capability does the Azure AI Foundry model catalog provide?
A. Access to prebuilt and foundation models
B. Image labeling
C. Speech transcription
D. Data storage
Q53. (Multi‑select) Which actions support responsible generative AI deployment? (Choose two.)
A. Human review
B. Content filtering
C. Unlimited model access
D. Ignoring bias metrics
Q54 (Scenario-Based | Single Select)
A marketing team wants to generate short product descriptions automatically based on a few bullet points provided by users. The solution should generate natural-sounding text and allow control over tone (for example, professional or casual).
Which AI approach is most appropriate?
A. Image classification B. Predictive regression modeling C. Generative AI using a large language model D. Rule-based text templating
Q55 (Scenario-Based | Multi-Select)
You are designing a generative AI solution using Azure OpenAI Service for internal employees. The solution will generate responses to HR-related questions.
Which Responsible AI considerations should be addressed? (Select all that apply)
A. Data privacy and protection B. Model transparency C. Bias and fairness D. Object detection accuracy E. Content safety and filtering
Q56 (Matching)
Match each Azure service or capability to its primary use case.
Azure Service / Capability
Use Case
1. Azure OpenAI Service
A. Analyze sentiment in customer feedback
2. Azure AI Language
B. Generate natural language text from prompts
3. Azure AI Vision
C. Detect objects and extract image features
4. Azure AI Speech
D. Convert spoken language into text
Q57 (Scenario-Based | Single Select)
A developer wants to experiment with different foundation models, compare their performance, and select a model to deploy for a generative AI chatbot.
Which Azure capability best supports this requirement?
A. Azure Machine Learning pipelines B. Azure AI Foundry model catalog C. Azure AI Vision Studio D. Azure AI Speech Studio
Q58 (Fill in the Blank)
In a generative AI solution, the text or instructions provided by the user to guide the model’s output is called a __________.
Q59 (Scenario-Based | Multi-Select)
An organization plans to deploy a generative AI application that summarizes internal documents. The documents may contain sensitive employee data.
Which actions help reduce risk? (Select all that apply)
A. Apply role-based access control (RBAC) B. Use data encryption at rest and in transit C. Disable content filtering to improve creativity D. Limit model access to approved users E. Log and monitor prompt and response usage
Q60 (Scenario-Based | Single Select)
You are evaluating whether a business problem is best solved using generative AI rather than traditional machine learning.
Which scenario is the best candidate for generative AI?
A. Predicting next month’s sales total B. Classifying emails as spam or not spam C. Generating a draft response to a customer support request D. Detecting fraudulent credit card transactions
Practice Exam 2 – Answer Key
(It is recommended that you review the answers after attempting the exam)
Describe AI Workloads & Considerations (Q1–Q10)
Question 1
Correct Answer: C Explanation: AI workloads focus on enabling machines to perceive, learn, reason, and act. Automation alone does not imply AI.
Question 2
Correct Answer: B Explanation: Image classification is a computer vision AI workload, not a traditional automation or rules-based system.
Question 3
Correct Answer: A Explanation: Fairness ensures AI systems do not introduce or reinforce bias against groups of users.
Question 4
Correct Answers: A, C Explanation: Reliability and safety focus on consistency, error handling, and preventing harm. Performance tuning alone is not sufficient.
Question 5
Correct Answer: D Explanation: Accountability ensures humans remain responsible for AI decisions and outcomes.
Question 6
Correct Answer: B Explanation: Transparency requires that users understand how and why AI systems behave as they do.
Question 7
Correct Answers: A, D Explanation: Privacy and security focus on protecting data and controlling access.
Question 8
Correct Answer: C Explanation: Inclusiveness ensures AI systems are usable by people of different abilities and backgrounds.
Question 9
Correct Answer: B Explanation: AI workloads often require training on large datasets, unlike static rule-based systems.
Question 10
Correct Answer: A Explanation: Predictive outcomes based on patterns is a defining feature of AI workloads.
Machine Learning Principles (Q11–Q22)
Question 11
Correct Answer: B Explanation: Regression predicts continuous numeric values, such as sales or temperature.
Question 12
Correct Answer: A Explanation: Classification predicts discrete labels (spam vs. not spam).
Question 13
Correct Answer: C Explanation: Clustering groups unlabeled data based on similarity.
Question 14
Correct Answer: D Explanation: Features are input variables; labels are the outcomes the model learns to predict.
Question 15
Correct Answer: B Explanation: Training data teaches the model; validation data evaluates performance.
Question 16
Correct Answer: A Explanation: Automated ML automatically selects algorithms and tunes hyperparameters.
Question 17
Correct Answer: C Explanation: Azure Machine Learning provides compute, data management, and model lifecycle tools.
Question 18
Correct Answer: B Explanation: Model deployment makes trained models available as web services or endpoints.
Question 19
Correct Answer: D Explanation: Deep learning uses multi-layer neural networks to learn complex patterns.
Question 20
Correct Answer: A Explanation: Transformers use attention mechanisms to process sequences efficiently.
Question 21
Correct Answer: B Explanation: Validation datasets help detect overfitting.
Question 22
Correct Answer: C Explanation: Azure ML supports versioning, monitoring, and retraining.
Computer Vision Workloads (Q23–Q32)
Question 23
Correct Answer: A Explanation: Image classification assigns labels to images.
Question 24
Correct Answer: B Explanation: Object detection identifies objects and their locations.
Question 25
Correct Answer: C Explanation: OCR extracts printed or handwritten text from images.
Question 26
Correct Answer: D Explanation: Facial detection identifies faces; analysis can infer attributes.
Question 27
Correct Answer: A Explanation: Azure AI Vision provides image analysis, OCR, and object detection.
Question 28
Correct Answer: B Explanation: Face detection identifies faces without identifying individuals.
Question 29
Correct Answer: C Explanation: OCR is ideal for digitizing scanned documents.
Question 30
Correct Answer: D Explanation: Computer vision solutions analyze visual content.
Question 31
Correct Answer: A Explanation: Bounding boxes are used in object detection.
Question 32
Correct Answer: B Explanation: Vision Studio allows testing models without writing code.
NLP Workloads (Q33–Q43)
Question 33
Correct Answer: C Explanation: Key phrase extraction identifies important terms in text.
Question 34
Correct Answer: A Explanation: Entity recognition identifies names, locations, organizations, etc.
Question 35
Correct Answer: B Explanation: Sentiment analysis determines emotional tone.
Question 36
Correct Answer: D Explanation: Language models predict the next token in a sequence.
Question 37
Correct Answer: A Explanation: Speech recognition converts spoken language into text.
Question 38
Correct Answer: C Explanation: Text-to-speech generates spoken output from text.
Question 39
Correct Answer: B Explanation: Translation converts text between languages.
Question 40
Correct Answer: A Explanation: Azure AI Language provides NLP capabilities.
Question 41
Correct Answer: C Explanation: Azure AI Speech handles speech-to-text and text-to-speech.
Question 42
Correct Answer: D Explanation: NLP workloads process and analyze human language.
Question 43
Correct Answer: B Explanation: Tokenization breaks text into smaller units.
Generative AI Workloads (Q44–Q60)
Question 44
Correct Answer: C Explanation: Generative AI creates new content rather than predicting labels.
Question 45
Correct Answer: A Explanation: Large Language Models are trained on massive text datasets.
Question 46
Correct Answer: B Explanation: Azure OpenAI provides access to generative models.
Question 47
Correct Answer: D Explanation: Prompt engineering shapes model output.
Question 48
Correct Answer: A Explanation: Generative AI is ideal for summarization and content creation.
Question 49
Correct Answer: C Explanation: Responsible AI mitigates hallucinations and bias.
Question 50
Correct Answer: B Explanation: Content filtering prevents unsafe outputs.
Question 51
Correct Answer: A Explanation: Azure AI Foundry centralizes model experimentation and deployment.
Question 52
Correct Answer: D Explanation: Model catalogs allow model discovery and comparison.
Question 53
Correct Answer: B Explanation: Generative AI is best for open-ended responses.
Question 54
Correct Answer: C Explanation: LLMs generate natural language with tone control.
Question 55
Correct Answers: A, B, C, E Explanation: Privacy, fairness, transparency, and content safety are critical.
Question 56
Correct Matches: 1 → B 2 → A 3 → C 4 → D
Question 57
Correct Answer: B Explanation: Azure AI Foundry model catalog supports model comparison.
Question 58
Correct Answer:Prompt Explanation: Prompts guide model behavior.
Question 59
Correct Answers: A, B, D, E Explanation: Security controls and monitoring reduce risk.
Question 60
Correct Answer: C Explanation: Generative AI excels at creating human-like text responses.
Below are some commonly asked questions about the PL-300: Microsoft Power BI Data Analyst certification exam. Upon successfully passing this exam, you earn the Microsoft Certified: Power BI Data Analyst Associate certification.
What is the PL-300 certification exam?
The PL-300: Microsoft Power BI Data Analyst exam validates your ability to prepare, model, visualize, analyze, and secure data using Microsoft Power BI.
Candidates who pass the exam demonstrate proficiency in:
Connecting to and transforming data from multiple sources
Designing and building efficient data models
Creating compelling and insightful reports and dashboards
Applying DAX calculations and measures
Implementing security, governance, and deployment best practices in Power BI
This certification is designed for professionals who work with data and use Power BI to deliver business insights. Upon successfully passing this exam, candidates earn the Microsoft Certified: Power BI Data Analyst Associate certification.
Is the PL-300 certification exam worth it?
The short answer is yes.
Preparing for the PL-300 exam provides significant value, even beyond the certification itself. The study process exposes you to Power BI features, patterns, and best practices that you may not encounter in day-to-day work. This often results in:
Stronger data modeling and DAX skills
Better-performing and more maintainable Power BI solutions
Increased confidence when designing analytics solutions
Greater credibility with stakeholders, employers, and clients
For many professionals, the exam also serves as a structured learning path that fills in knowledge gaps and reinforces real-world experience.
How many questions are on the PL-300 exam?
The PL-300 exam typically contains between 40 and 60 questions.
The questions may appear in several formats, including:
Single-choice and multiple-choice questions
Multi-select questions
Drag-and-drop or matching questions
Case studies with multiple questions
The exact number and format can vary slightly from exam to exam.
How hard is the PL-300 exam?
The PL-300 exam is considered moderately to highly challenging, especially for candidates without hands-on Power BI experience.
The difficulty comes from:
The breadth of topics covered
Scenario-based questions that test applied knowledge
Time pressure during the exam
However, the challenge is also what gives the certification its value. With proper preparation and practice, the exam is very achievable.
As of January 1, 2026, the standard exam pricing is:
United States: $165 USD
Australia: $140 USD
Canada: $140 USD
India: $4,865 INR
China: $83 USD
United Kingdom: £106 GBP
Other countries: Pricing varies based on country and region
Microsoft occasionally offers discounts, student pricing, or exam vouchers, so it is worth checking the official Microsoft certification site before scheduling your exam.
How do I prepare for the Microsoft PL-300 certification exam?
The most important advice is do not rush to sit the exam. Take time to cover all topic areas thoroughly before taking the exam.
Practice building Power BI reports end-to-end using real or sample data.
Strengthen weak areas such as DAX, data modeling, or security.
Take practice exams to validate your readiness. Microsoft Learn’s PL-300 practice exam is available here; and there are 2 practice exams available on The Data Community’s PL-300 Exam Prep Hub.
Scenario-based questions that test understanding rather than memorization
Legitimate practice materials help you build real skills that are valuable beyond the exam itself.
How long should I study for the PL-300 exam?
Study time varies depending on your background and experience.
General guidelines:
Experienced Power BI users: 4–6 weeks of focused preparation
Moderate experience: 6–8 weeks of focused preparation
Beginners or limited experience: 8–12 weeks or more of focused preparation
Rather than focusing on time alone, because it will vary broadly based on several factors, aim to fully understand all exam topics and perform well on practice exams before scheduling the test.
Where can I find training or a course for the PL-300 exam?
Training options include:
Microsoft Learn: Free, official learning path
Online learning platforms: Udemy, Coursera, and similar providers
YouTube: Free playlists and walkthroughs covering PL-300 topics
Subscription platforms: Datacamp and others offering Power BI courses
Microsoft partners: Instructor-led and enterprise-focused training
A combination of structured learning and hands-on practice tends to work best.
What skills should I have before taking the PL-300 exam?
Before attempting the exam, you should be comfortable with:
Basic data concepts (tables, relationships, measures)
Power BI Desktop and Power BI Service
Power Query for data transformation
DAX fundamentals
Basic understanding of data modeling and analytics concepts
You do not need to be an expert in all areas, but hands-on familiarity is important.
What score do I need to pass the PL-300 exam?
Microsoft exams are scored on a scale of 1–1000, and a score of 700 or higher is required to pass.
The score is scaled, meaning it is based on question difficulty rather than a simple percentage of correct answers.
How long is the PL-300 exam?
You are given approximately 120 minutes to complete the exam, including time to review instructions and case studies.
Time management is very important, especially for scenario-based questions.
How long is the PL-300 certification valid?
The Microsoft Certified: Power BI Data Analyst Associate certification is valid for one year.
To maintain your certification, you must complete a free online renewal assessment before the expiration date.
Is PL-300 suitable for beginners?
PL-300 is beginner-friendly in structure but assumes some hands-on experience.
Beginners can absolutely pass the exam, but they should expect to spend additional time practicing with Power BI and learning foundational concepts.
What roles benefit most from the PL-300 certification?
The PL-300 certification is especially valuable for:
Welcome to the one-stop hub with information for preparing for the PL-300: Microsoft Power BI Data Analyst certification exam. Upon successful completion of the exam, you earn the Microsoft Certified: Power BI Data Analyst Associate certification.
This hub provides information directly here (topic-by-topic), links to a number of external resources, tips for preparing for the exam, practice tests, and section questions to help you prepare. Bookmark this page and use it as a guide to ensure that you are fully covering all relevant topics for the PL-300 exam and making use of as many of the resources available as possible.
Skills tested at a glance (as specified in the official study guide)
Prepare the data (25–30%)
Model the data (25–30%)
Visualize and analyze the data (25–30%)
Manage and secure Power BI (15–20%)
Click on each hyperlinked topic below to go to the preparation content and practice questions for that topic. And there are also 2 practice exams provided below.
Link to the free, comprehensive, self-paced course on Microsoft Learn – Design and manage analytics solutions using Power BI It contains 5 Learning Paths, each with multiple Modules, and each module has multiple Units. It will take some time to do it, but we recommend that you complete this entire course, including the exercises/labs.
Schedule time to learn, study, perform labs, and do practice exams and questions
Schedule the exam based on when you think you will be ready; scheduling the exam gives you a target and drives you to keep working on it; but keep in mind that it can be rescheduled based on the rules of the provider.
Use the various resources above and below to learn
Take the free Microsoft Learn practice test, any other available practice tests, and do the practice questions in each section and the two practice tests available on this hub.
Good luck to you passing the PL-300: Microsoft Power BI Data Analyst certification exam and earning the Microsoft Certified: Power BI Data Analyst Associate certification!
PL-300: Microsoft Power BI Data Analyst practice exam
Total Questions: 60 Time Recommendation: 120 minutes
Note: We have sectioned the questions to help you prepare, but the real exam will have questions from the sections appearing randomly. The answers are at the end, and we recommend only looking at the answers after you have attempted the questions.
Exam Structure & Weighting (60 Questions)
Domain
%
Questions
Prepare the data
~27%
16
Model the data
~27%
16
Visualize and analyze the data
~27%
16
Manage and secure Power BI
~19%
12
Total
100%
60
SECTION 1: Prepare the Data (Questions 1–16)
1. (Single choice) You connect to a CSV file containing sales data. The file is updated daily with additional rows. What should you do to ensure Power BI always imports only new records?
A. Use Import mode B. Enable Incremental Refresh C. Use DirectQuery D. Create a calculated table
2. (Scenario – Multi-select) You are cleaning customer data in Power Query. You need to:
Remove rows where CustomerID is null
Replace empty strings in Country with “Unknown”
Which two steps should you use? (Select two)
A. Filter rows B. Replace values C. Conditional column D. Remove errors
3. (Fill in the blank) The Power Query feature used to profile data by showing column distribution, quality, and profile is called __________.
4. (Single choice) You want to reduce model size by removing unused columns before loading data. Where should this be done?
A. In DAX B. In Power BI Service C. In Power Query Editor D. In the Data view
5. (Scenario – Single choice) A dataset contains numeric values stored as text. What is the best approach to fix this?
A. Convert data type in the report view B. Create a calculated column C. Change data type in Power Query D. Use FORMAT() in DAX
6. (Multi-select) Which transformations are considered query folding–friendly? (Select two)
A. Filtering rows B. Adding an Index column C. Merging queries D. Custom M function logic
7. (Single choice) What does query folding primarily help with?
A. Improving report aesthetics B. Reducing dataset size C. Pushing transformations to the source system D. Enabling DirectQuery
8. (Scenario – Single choice) You want to append monthly Excel files from a folder automatically. What connector should you use?
A. Excel Workbook B. SharePoint Folder C. Folder D. Web
9. (Matching) Match the Power Query feature to its purpose:
Feature
Purpose
A. Merge Queries
1. Stack tables vertically
B. Append Queries
2. Combine tables horizontally
C. Group By
3. Aggregate rows
10. (Single choice) Which data source supports DirectQuery?
A. Excel B. CSV C. SQL Server D. JSON
11. (Scenario – Multi-select) You want to reduce refresh time. Which actions help? (Select two)
A. Remove unused columns B. Increase report page count C. Apply filters early D. Use calculated columns
12. (Single choice) What does enabling “Enable load” = Off do?
A. Deletes the query B. Prevents data refresh C. Prevents data from loading into the model D. Disables query folding
13. (Single choice) Which transformation breaks query folding most often?
A. Filtering B. Sorting C. Custom column with M code D. Renaming columns
14. (Fill in the blank) The language used by Power Query is called __________.
15. (Scenario – Single choice) You need to standardize country names across multiple sources. What is the best approach?
A. DAX LOOKUPVALUE B. Power Query Replace Values C. Calculated table D. Visual-level filter
16. (Single choice) What is the main benefit of disabling Auto Date/Time?
A. Faster report rendering B. Better compression and simpler models C. Enables time intelligence D. Required for DirectQuery
SECTION 2: Model the Data (Questions 17–32)
17. (Single choice) What is the recommended cardinality between a fact table and a dimension table?
A. Many-to-many B. One-to-one C. One-to-many D. Many-to-one
18. (Scenario – Single choice) You have Sales and Customers tables. Each sale belongs to one customer. How should the relationship be defined?
A. Many-to-many B. One-to-many from Customers to Sales C. One-to-one D. Inactive
19. (Multi-select) Which actions improve model performance? (Select two)
A. Reduce column cardinality B. Use bi-directional filters everywhere C. Star schema design D. Hide fact table columns
20. (Fill in the blank) A __________ table contains descriptive attributes used for slicing and filtering.
21. (Scenario – Single choice) When should you use a calculated column instead of a measure?
A. When performing aggregations B. When results must be stored per row C. When using slicers D. When reducing model size
22. (Single choice) Which DAX function safely handles divide-by-zero errors?
A. DIV B. IFERROR C. DIVIDE D. CALCULATE
23. (Scenario – Single choice) You need a dynamic calculation that responds to filters. What should you use?
A. Calculated column B. Calculated table C. Measure D. Static column
24. (Matching) Match the DAX concept to its description:
Concept
Description
A. Row context
1. Filters applied by visuals
B. Filter context
2. Iteration over rows
C. Context transition
3. Row → filter conversion
25. (Single choice) What does CALCULATE primarily do?
A. Creates relationships B. Changes filter context C. Adds rows to tables D. Improves compression
26. (Multi-select) Which are valid time intelligence functions? (Select two)
A. TOTALYTD B. SAMEPERIODLASTYEAR C. SUMX D. VALUES
27. (Scenario – Single choice) You need Year-over-Year growth. What prerequisite must be met?
A. Auto Date/Time enabled B. Continuous date column C. Marked Date table D. Calculated column
28. (Single choice) What does marking a table as a Date table do?
A. Improves visuals B. Enables time intelligence accuracy C. Reduces refresh time D. Enables RLS
29. (Multi-select) Which DAX functions are iterators? (Select two)
A. SUMX B. AVERAGEX C. SUM D. COUNT
30. (Scenario – Single choice) You need to model a many-to-many relationship. What is the recommended solution?
A. Bi-directional filters B. Bridge table C. Calculated column D. Duplicate keys
31. (Single choice) What is the main drawback of bi-directional relationships?
A. Slower refresh B. Increased ambiguity and performance cost C. Larger dataset size D. Disabled measures
32. (Fill in the blank) The recommended schema design in Power BI is the __________ schema.
SECTION 3: Visualize and Analyze the Data (Questions 33–48)
33. (Single choice) Which visual best shows trends over time?
A. Bar chart B. Table C. Line chart D. Card
34. (Scenario – Single choice) You want users to explore details by clicking on a value in a chart. What feature should you use?
A. Drillthrough B. Tooltip C. Drill-down D. Bookmark
35. (Multi-select) Which visuals support drill-down? (Select two)
A. Matrix B. Card C. Bar chart D. KPI
36. (Fill in the blank) A page that shows detailed information for a selected data point is called a __________ page.
37. (Single choice) Which feature allows navigation between predefined report states?
A. Filters B. Slicers C. Bookmarks D. Tooltips
38. (Scenario – Single choice) You want to highlight values above a threshold. What should you use?
A. Conditional formatting B. Custom visual C. Calculated column D. Page filter
39. (Multi-select) Which elements can be used as slicers? (Select two)
A. Numeric columns B. Measures C. Date columns D. Calculated tables
40. (Single choice) What does a tooltip page provide?
A. Navigation B. Additional context on hover C. Data refresh D. Security
41. (Scenario – Single choice) You want visuals on one page to affect another page. What should you use?
A. Drill-down B. Sync slicers C. RLS D. Visual interactions
42. (Single choice) Which feature allows exporting summarized data only?
A. Export underlying data B. Export summarized data C. Analyze in Excel D. Paginated reports
43. (Multi-select) Which actions improve report performance? (Select two)
A. Limit visuals per page B. Use high-cardinality slicers C. Use measures instead of columns D. Disable interactions
44. (Single choice) What is the purpose of a KPI visual?
A. Show raw data B. Compare actuals to targets C. Display trends D. Filter visuals
45. (Scenario – Single choice) You need a visual that supports hierarchical navigation. What should you choose?
A. Card B. Line chart C. Matrix D. Gauge
46. (Fill in the blank) The feature that allows users to ask natural language questions is called __________.
47. (Single choice) What determines visual interaction behavior?
A. Data model B. Report theme C. Edit interactions settings D. Dataset permissions
48. (Single choice) Which visual is best for comparing proportions?
A. Table B. Pie chart C. Scatter plot D. Line chart
SECTION 4: Manage and Secure Power BI (Questions 49–60)
49. (Single choice) What does Row-Level Security (RLS) control?
A. Visual visibility B. Data access by user C. Dataset refresh D. Workspace roles
50. (Scenario – Single choice) You need different users to see different regions’ data. What should you implement?
A. App audiences B. RLS roles C. Workspace permissions D. Object-level security
51. (Multi-select) Which roles can publish content? (Select two)
A. Viewer B. Contributor C. Member D. Admin
52. (Single choice) Where is RLS created?
A. Power BI Service only B. Power BI Desktop C. Azure Portal D. Excel
53. (Single choice) What is Object-Level Security (OLS) used for?
A. Hiding rows B. Hiding columns or tables C. Encrypting data D. Managing refresh
54. (Scenario – Single choice) You want users to consume reports without editing. Which workspace role is best?
A. Admin B. Member C. Contributor D. Viewer
55. (Fill in the blank) A packaged, read-only distribution of reports is called a Power BI __________.
56. (Single choice) Which feature controls dataset refresh schedules?
A. Gateway B. Dataset settings C. Workspace D. App
57. (Multi-select) Which authentication methods are supported by Power BI gateways? (Select two)
A. Windows B. OAuth C. Basic D. Anonymous
58. (Scenario – Single choice) You want on-premises SQL data to refresh in Power BI Service. What is required?
A. DirectQuery B. On-premises data gateway C. Azure SQL D. Incremental refresh
59. (Single choice) Who can manage workspace users?
A. Viewer B. Contributor C. Member D. Admin
60. (Single choice) What is the primary benefit of Power BI apps?
A. Faster refresh B. Centralized content distribution C. Improved DAX performance D. Reduced dataset size
ANSWER KEY WITH EXPLANATIONS
Below are correct answers and explanations, including why incorrect options are not correct. (Use this section after completing the exam.)
SECTION 1: Prepare the Data (1-16)
B – Incremental Refresh loads only new/changed data
A, B – Filter rows removes nulls; Replace Values handles empty strings
Data profiling
C – Remove columns before loading
C – Best practice is Power Query transformation
A, C – Folding-friendly operations
C – Pushes logic to the source
C – Folder connector handles multiple files
A-2, B-1, C-3
C – SQL Server supports DirectQuery
A, C – Reduce data early
C – Prevents model loading
C – Custom M breaks folding
M
B – Clean once at ingestion
B – Avoids hidden date tables
SECTION 2: Model the Data (17–32)
17. Correct Answer: C — One-to-many
Why correct: In a star schema, dimension tables have unique keys and fact tables contain repeated keys.
Why others are incorrect:
A/B/D create ambiguity or are rarely appropriate in analytical models.
18. Correct Answer: B — One-to-many from Customers to Sales
Why correct: One customer can have many sales, but each sale belongs to one customer.
Why others are incorrect:
Many-to-many and one-to-one do not reflect the business reality.
Inactive relationships are only used when multiple relationships exist.
19. Correct Answers: A, C
Why correct:
Reducing column cardinality improves compression.
Star schemas reduce relationship complexity and improve performance.
Why others are incorrect:
Bi-directional filters add overhead.
Hiding columns improves usability, not performance.
PL-300: Microsoft Power BI Data Analyst practice exam
Total Questions: 60 Time Recommendation: 120 minutes
Note: We have sectioned the questions to help you prepare, but the real exam will have questions from the sections appearing randomly. The answers are at the end, and we recommend only looking at the answers after you have attempted the questions.
SECTION 1: Prepare the Data (Questions 1–16)
1. (Scenario – Single choice) You are importing data from a SQL Server database. You want to ensure transformations are executed at the source whenever possible. What should you prioritize?
A. Using Import mode B. Maintaining query folding C. Creating calculated columns D. Disabling Auto Date/Time
2. (Multi-select) Which Power Query actions typically preserve query folding? (Select two)
A. Filtering rows B. Adding a custom column with complex M logic C. Removing columns D. Changing column order
3. (Fill in the blank) Power BI’s feature that automatically detects column data types during import is called __________.
4. (Scenario – Single choice) You need to combine two tables with the same columns but different rows. What should you use?
A. Merge Queries B. Append Queries C. Relationship D. Lookup column
5. (Single choice) Which data type is most memory-efficient for categorical values?
A. Text B. Whole Number C. Decimal Number D. DateTime
6. (Scenario – Multi-select) You are profiling a dataset and notice unexpected null values. Which tools help identify data quality issues? (Select two)
A. Column quality B. Column distribution C. Conditional columns D. Replace errors
7. (Single choice) Which connector allows ingestion of multiple files stored in a directory?
A. Excel Workbook B. SharePoint List C. Folder D. Web API
8. (Scenario – Single choice) You want to standardize values such as “USA”, “U.S.”, and “United States”. What is the most scalable solution?
A. DAX calculated column B. Replace Values in Power Query C. Visual-level filter D. Manual edits in Data view
9. (Matching) Match the transformation to its outcome:
Transformation
Outcome
A. Group By
1. Reduce row-level detail
B. Remove duplicates
2. Aggregate data
C. Filter rows
3. Exclude unwanted records
10. (Single choice) Which data source does NOT support DirectQuery?
A. Azure SQL Database B. SQL Server C. Excel workbook D. Azure Synapse Analytics
11. (Scenario – Single choice) A column contains numbers and text. You need to fix errors without removing rows. What is the best option?
A. Remove errors B. Replace errors C. Change data type D. Split column
12. (Multi-select) Which actions reduce dataset size? (Select two)
A. Removing unused columns B. Increasing column cardinality C. Disabling Auto Date/Time D. Using calculated tables
13. (Single choice) Which step most commonly breaks query folding?
A. Sorting rows B. Renaming columns C. Adding a custom M function D. Filtering
14. (Fill in the blank) Power Query transformations are written using the __________ language.
15. (Scenario – Single choice) You want to reuse a transformation across multiple queries. What should you create?
A. Calculated table B. Custom column C. Function D. Measure
16. (Single choice) Why is disabling Auto Date/Time considered a best practice?
A. It improves visual formatting B. It reduces hidden tables and model size C. It enables DirectQuery D. It improves gateway performance
SECTION 2: Model the Data (Questions 17–32)
17. (Single choice) Which schema design is recommended for Power BI models?
A. Snowflake B. Relational C. Star D. Hierarchical
18. (Scenario – Single choice) You have multiple fact tables sharing the same Date table. What relationship setup is recommended?
A. Many-to-many B. One-to-one C. One-to-many from Date D. Bi-directional
19. (Multi-select) Which actions improve DAX performance? (Select two)
A. Using variables B. Using volatile functions C. Reducing iterator usage D. Increasing column cardinality
20. (Fill in the blank) A table that stores transactional events is called a __________ table.
21. (Scenario – Single choice) You need a calculation that must be evaluated only once during refresh. What should you use?
A. Measure B. Calculated column C. Visual filter D. Slicer
22. (Single choice) Which function changes filter context?
A. SUM B. FILTER C. CALCULATE D. VALUES
23. (Scenario – Single choice) You need a metric that responds to slicers and cross-highlighting. What should you create?
A. Calculated table B. Calculated column C. Measure D. Static column
24. (Matching) Match the DAX concept to its definition:
Concept
Definition
A. Filter context
1. Row-by-row evaluation
B. Row context
2. Visual and slicer filters
C. Iterator
3. Loops through rows
25. (Single choice) Which DAX function safely handles division when the denominator is zero?
A. IF B. DIV C. DIVIDE D. CALCULATETABLE
26. (Multi-select) Which functions are considered time intelligence? (Select two)
A. DATEADD B. SAMEPERIODLASTYEAR C. SUMX D. FILTER
27. (Scenario – Single choice) Why should you mark a Date table?
A. To enable RLS B. To improve visual formatting C. To ensure correct time intelligence D. To reduce refresh duration
28. (Single choice) What is the purpose of a bridge table?
A. Speed up refresh B. Resolve many-to-many relationships C. Enable DirectQuery D. Create calculated measures
29. (Multi-select) Which are iterator functions? (Select two)
A. COUNT B. SUMX C. AVERAGEX D. DISTINCT
30. (Scenario – Single choice) You have two date relationships between the same tables. One is inactive. How do you use the inactive one?
A. USERELATIONSHIP B. CROSSFILTER C. RELATED D. LOOKUPVALUE
31. (Single choice) What is a key downside of calculated columns?
A. They cannot be filtered B. They increase model size C. They cannot use DAX D. They slow down visuals
32. (Fill in the blank) The recommended relationship direction in most models is __________.
SECTION 3: Visualize and Analyze the Data (Questions 33–48)
33. (Single choice) Which visual best compares values across categories?
A. Line chart B. Bar chart C. Scatter plot D. Area chart
34. (Scenario – Single choice) You want users to navigate to a detail page by right-clicking a visual. What should you configure?
A. Drill-down B. Drillthrough C. Bookmark D. Tooltip
35. (Multi-select) Which visuals support hierarchies? (Select two)
A. Matrix B. Card C. Bar chart D. Gauge
36. (Fill in the blank) A report page designed to show details for a selected value is called a __________ page.
37. (Single choice) Which feature allows toggling between different visual states?
A. Filters B. Bookmarks C. Themes D. Sync slicers
38. (Scenario – Single choice) You want values over target to appear green and under target red. What should you use?
A. KPI visual B. Conditional formatting C. Measure D. Theme
39. (Multi-select) Which fields can be used in a slicer? (Select two)
A. Measures B. Date columns C. Text columns D. Tooltips
40. (Single choice) What is the primary purpose of report tooltips?
A. Navigation B. Additional context on hover C. Filtering D. Security
41. (Scenario – Single choice) You want slicer selections on one page to apply to other pages. What should you use?
A. Drillthrough B. Visual interactions C. Sync slicers D. Bookmarks
42. (Single choice) Which export option respects RLS and aggregation?
A. Export underlying data B. Export summarized data C. Copy visual D. Analyze in Excel
43. (Multi-select) Which actions improve report performance? (Select two)
A. Reduce number of visuals B. Use complex custom visuals everywhere C. Prefer measures over columns D. Increase page interactions
44. (Single choice) What does a KPI visual compare?
A. Actual vs target B. Categories vs totals C. Trends over time D. Part-to-whole
45. (Scenario – Single choice) Which visual supports row and column grouping with totals?
A. Table B. Matrix C. Card D. Gauge
46. (Fill in the blank) The feature that allows users to ask questions using natural language is __________.
47. (Single choice) Where do you configure how visuals affect each other?
A. Model view B. Edit interactions C. Dataset settings D. Themes
48. (Single choice) Which visual is best for showing part-to-whole relationships?
A. Line chart B. Pie chart C. Scatter plot D. Table
SECTION 4: Manage and Secure Power BI (Questions 49–60)
49. (Single choice) Row-Level Security primarily restricts access to:
A. Reports B. Rows of data C. Dashboards D. Workspaces
50. (Scenario – Single choice) Different users must see different departments’ data using the same report. What should you implement?
A. App audiences B. RLS roles C. Workspace permissions D. Bookmarks
51. (Multi-select) Which workspace roles can publish content? (Select two)
A. Viewer B. Contributor C. Member D. Admin
52. (Single choice) Where are RLS roles defined?
A. Power BI Service B. Power BI Desktop C. Azure AD D. SQL Server
53. (Single choice) What does Object-Level Security control?
A. Row visibility B. Column or table visibility C. Dataset refresh D. Report access
54. (Scenario – Single choice) Which role should be assigned to users who only consume content?
A. Admin B. Member C. Contributor D. Viewer
55. (Fill in the blank) A curated, read-only package of Power BI content is called an __________.
56. (Single choice) Which component enables scheduled refresh for on-premises data?
A. DirectQuery B. Dataset C. Gateway D. Workspace
57. (Multi-select) Which authentication types are supported by on-premises data gateways? (Select two)
A. Windows B. OAuth C. Basic D. Anonymous
58. (Scenario – Single choice) You want to minimize refresh time for a very large dataset. What should you configure?
A. RLS B. Incremental refresh C. DirectQuery D. OLS
59. (Single choice) Who can manage users and permissions in a workspace?
A. Viewer B. Contributor C. Member D. Admin
60. (Single choice) What is a primary advantage of Power BI apps?
A. Faster DAX calculations B. Controlled content distribution C. Reduced data volume D. Improved gateway reliability
ANSWER KEY WITH EXPLANATIONS
Prepare the Data (1–16)
B — Query folding pushes transformations to the source
A, C — Filtering and removing columns fold well
Type detection
B — Append stacks rows
B — Whole numbers compress best
A, B — Profiling tools reveal quality issues
C — Folder connector ingests multiple files
B — Clean once at ingestion
A-2, B-1, C-3
C — Excel does not support DirectQuery
B — Replace errors preserves rows
A, C — Less data, fewer hidden tables
C — Custom M breaks folding
M
C — Functions promote reuse
B — Prevents unnecessary date tables
Model the Data (17–32)
C — Star schema is best practice
C — Date is a shared dimension
A, C — Variables and fewer iterators improve performance
Fact
B — Calculated columns are refresh-time only
C — CALCULATE modifies filters
C — Measures are dynamic
A-2, B-1, C-3
C — DIVIDE handles zero safely
A, B — Both are time intelligence
C — Required for correct time calcs
B — Bridge resolves many-to-many
B, C — Iterators loop rows
A — USERELATIONSHIP activates inactive relationships
B — Stored in memory
Single-direction
Visualize & Analyze (33–48)
B — Best for categorical comparison
B — Drillthrough navigates pages
A, C — Support hierarchies
Drillthrough
B — Bookmarks store states
B — Conditional formatting applies rules
B, C — Columns only
B — Context on hover
C — Sync slicers cross pages
B — Respects aggregation & security
A, C — Fewer visuals, measures preferred
A — Actual vs target
B — Matrix supports grouping
Q&A
B — Edit interactions
B — Part-to-whole
Manage & Secure (49–60)
B — RLS filters rows
B — Role-based filtering
B, D — Can publish
B — Defined in Desktop
B — Hides columns/tables
D — Viewer is read-only
App
C — Gateway enables refresh
A, C — Supported auth types
B — Incremental refresh
D — Admin manages users
B — Centralized, controlled distribution
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