Tag: Microsoft Certified: Azure AI Fundamentals

AI-900, Microsoft Certification

AI-900: Practice Exam 2 (60 questions with answers)

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.

PrincipleDescription
1. Reliability & SafetyA. Protects personal and sensitive data
2. Privacy & SecurityB. Ensures consistent and dependable performance
3. TransparencyC. 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 TypeScenario
1. SupervisedA. Grouping customers by behavior
2. UnsupervisedB. Predicting house prices
3. ReinforcementC. 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.

TaskDescription
1. Image classificationA. Detects text in images
2. OCRB. Assigns a label to an entire image
3. Object detectionC. 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.

TaskScenario
1. TranslationA. Detects emotional tone
2. Sentiment analysisB. Converts text between languages
3. Key phrase extractionC. 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.

ConceptDescription
1. PromptA. AI‑generated incorrect content
2. HallucinationB. Input provided to a model
3. GroundingC. 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 / CapabilityUse Case
1. Azure OpenAI ServiceA. Analyze sentiment in customer feedback
2. Azure AI LanguageB. Generate natural language text from prompts
3. Azure AI VisionC. Detect objects and extract image features
4. Azure AI SpeechD. 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.

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