Tag: AI

AI, AI-900, Artificial Intelligence (AI), Machine Learning (ML), Microsoft Certification January 31, 2026

Practice Questions: Identify Clustering Machine Learning Scenarios (AI-900 Exam Prep)

Practice Exam Questions

Question 1

A retail company wants to group customers based on purchasing behavior without defining categories in advance.

Which machine learning technique should be used?

A. Regression
B. Classification
C. Clustering
D. Anomaly detection

Correct Answer: C

Explanation:
The goal is to group unlabeled data and discover natural segments, which is clustering.

Question 2

An organization analyzes large volumes of web traffic data to identify patterns in user behavior.

Which machine learning approach is most appropriate?

A. Classification
B. Regression
C. Clustering
D. Forecasting

Correct Answer: C

Explanation:
Identifying patterns and similarities in unlabeled data is a clustering scenario.

Question 3

Which scenario is best suited for clustering?

A. Predicting monthly revenue
B. Determining whether a transaction is fraudulent
C. Segmenting customers into behavior-based groups
D. Estimating delivery time

Correct Answer: C

Explanation:
Customer segmentation without predefined labels is a classic clustering use case.

Question 4

A company wants to organize products into groups based on similarity without predefined categories.

What type of machine learning technique is being used?

A. Regression
B. Classification
C. Clustering
D. Recommendation

Correct Answer: C

Explanation:
Grouping items based on similarity without labels is clustering.

Question 5

Which characteristic most strongly indicates a clustering scenario?

A. Numeric output values
B. Predefined labels
C. Labeled training data
D. Unlabeled data

Correct Answer: D

Explanation:
Clustering uses unlabeled data to discover structure and patterns.

Question 6

An AI system groups support tickets by similarity to identify common issues, without predefined issue types.

Which machine learning approach is being used?

A. Classification
B. Regression
C. Clustering
D. Natural language processing

Correct Answer: C

Explanation:
The system groups tickets without predefined labels, which indicates clustering.

Question 7

Which output best represents a clustering result?

A. Approved / Rejected
B. 4.7 hours
C. Cluster A, Cluster B, Cluster C
D. High risk

Correct Answer: C

Explanation:
Clusters represent group assignments, not numeric values or labels.

Question 8

A data scientist wants to explore a dataset to discover natural groupings before defining categories.

Which technique should be used?

A. Classification
B. Regression
C. Clustering
D. Forecasting

Correct Answer: C

Explanation:
Clustering is used for exploratory analysis to find natural groupings.

Question 9

Which statement best describes clustering?

A. It predicts numeric values
B. It assigns predefined labels
C. It groups similar data points
D. It detects unusual events

Correct Answer: C

Explanation:
Clustering groups data points based on similarity without predefined labels.

Question 10

On the AI-900 exam, which keyword most strongly signals a clustering scenario?

A. Estimate
B. Categorize
C. Group
D. Measure

Correct Answer: C

Explanation:
Group indicates organizing unlabeled data into clusters, which is clustering.

Exam-Day Tip

If a machine learning related question mentions …

No labels
Discover patterns
Group or segment data

… the correct answer is likely to be related to Clustering.

Go to the AI-900 Exam Prep Hub main page.

AI, AI-900, Artificial Intelligence (AI), Machine Learning (ML), Microsoft Certification January 31, 2026

Identify Clustering Machine Learning Scenarios (AI-900 Exam Prep)

Where This Fits in the Exam

Exam Domain: Describe fundamental principles of machine learning on Azure (15–20%)
Sub-Domain: Identify common machine learning techniques
Topic: Identify clustering machine learning scenarios

On the AI-900 exam, clustering questions test whether you can recognize when grouping unlabeled data is the goal, not how to build or tune clustering models.

What Is Clustering in Machine Learning?

Clustering is a type of unsupervised machine learning used to group similar data points together based on patterns in the data.

No labeled training data is provided
The algorithm discovers structure on its own
The output is a group or cluster, not a predefined label

Key exam rule:
If the data has no labels and the goal is to discover natural groupings, the scenario is clustering.

Characteristics of Clustering Scenarios

A clustering workload typically includes:

Large amounts of unlabeled data
Multiple features describing each data point
No predefined categories
A goal of discovering similarity or structure

Clustering answers questions like:

Which items are similar?
How can this data be segmented?
What patterns exist in this dataset?

Common Clustering Use Cases

Customer Segmentation

Grouping customers by purchasing behavior
Identifying customer personas
Segmenting users for marketing campaigns

Data Exploration

Discovering patterns in large datasets
Identifying natural groupings in usage data
Understanding customer behavior trends

Image and Document Grouping

Grouping images by visual similarity
Organizing documents by topic
Detecting patterns in text collections

All of these involve grouping without predefined labels, which is the hallmark of clustering.

Clustering vs Other Machine Learning Techniques

This distinction is very important for AI-900.

Technique	Labeled Data	Output	Example
Regression	Yes	Numeric value	Predicting house price
Classification	Yes	Category or label	Approving a loan
Clustering	No	Group or cluster	Customer segmentation

Exam tip:
If the scenario mentions no labels, discover, or group, think Clustering.

Example Exam Scenarios

Scenario 1

A retailer wants to group customers based on shopping habits without defining categories in advance.

Labeled data: No
ML Technique: Clustering

Scenario 2

An organization analyzes sensor data to identify natural groupings of usage patterns.

Goal: Discover patterns
ML Technique: Clustering

Scenario 3

A company wants to organize products into groups based on similarity.

Predefined categories: None
ML Technique: Clustering

Azure Context for AI-900

On the AI-900 exam, clustering scenarios are often framed using Azure Machine Learning concepts such as:

Analyzing unlabeled datasets
Discovering patterns in data
Segmenting data for insights

You are not expected to:

Choose clustering algorithms
Configure Azure services
Write code

The focus is on recognizing when clustering is appropriate.

Common Exam Traps and Misconceptions

❌ Predicting a value → Regression
❌ Assigning predefined labels → Classification
❌ Detecting fraud → Classification or anomaly detection
✅ Grouping unlabeled data → Clustering

Key Takeaways for the Exam

Clustering is unsupervised learning
No labeled training data is required
The goal is to group similar data
Outputs are clusters, not predictions
Keywords: group, segment, organize, discover patterns

Identify Clustering Machine Learning Scenarios

AI-900: Microsoft Azure AI Fundamentals

Where This Fits in the Exam

Exam Domain: Describe fundamental principles of machine learning on Azure (15–20%)
Sub-Domain: Identify common machine learning techniques
Topic: Identify clustering machine learning scenarios

On the AI-900 exam, clustering questions test whether you can recognize when grouping unlabeled data is the goal, not how to build or tune clustering models.

What Is Clustering in Machine Learning?

Clustering is a type of unsupervised machine learning used to group similar data points together based on patterns in the data.

No labeled training data is provided
The algorithm discovers structure on its own
The output is a group or cluster, not a predefined label

Key exam rule:
If the data has no labels and the goal is to discover natural groupings, the scenario is clustering.

Characteristics of Clustering Scenarios

A clustering workload typically includes:

Large amounts of unlabeled data
Multiple features describing each data point
No predefined categories
A goal of discovering similarity or structure

Clustering answers questions like:

Which items are similar?
How can this data be segmented?
What patterns exist in this dataset?

Common Clustering Use Cases

Customer Segmentation

Grouping customers by purchasing behavior
Identifying customer personas
Segmenting users for marketing campaigns

Data Exploration

Discovering patterns in large datasets
Identifying natural groupings in usage data
Understanding customer behavior trends

Image and Document Grouping

Grouping images by visual similarity
Organizing documents by topic
Detecting patterns in text collections

All of these involve grouping without predefined labels, which is the hallmark of clustering.

Clustering vs Other Machine Learning Techniques

This distinction is very important for AI-900.

Technique	Labeled Data	Output	Example
Regression	Yes	Numeric value	Predicting house price
Classification	Yes	Category or label	Approving a loan
Clustering	No	Group or cluster	Customer segmentation

Exam tip:
If the scenario mentions no labels, discover, or group, think Clustering.

Example Exam Scenarios

Scenario 1

A retailer wants to group customers based on shopping habits without defining categories in advance.

Labeled data: No
ML Technique: Clustering

Scenario 2

An organization analyzes sensor data to identify natural groupings of usage patterns.

Goal: Discover patterns
ML Technique: Clustering

Scenario 3

A company wants to organize products into groups based on similarity.

Predefined categories: None
ML Technique: Clustering

Azure Context for AI-900

On the AI-900 exam, clustering scenarios are often framed using Azure Machine Learning concepts such as:

Analyzing unlabeled datasets
Discovering patterns in data
Segmenting data for insights

You are not expected to:

Choose clustering algorithms
Configure Azure services
Write code

The focus is on recognizing when clustering is appropriate.

Common Exam Traps and Misconceptions

❌ Predicting a value → Regression
❌ Assigning predefined labels → Classification
❌ Detecting fraud → Classification or anomaly detection
✅ Grouping unlabeled data → Clustering

Key Takeaways for the Exam

Clustering is unsupervised learning
No labeled training data is required
The goal is to group similar data
Outputs are clusters, not predictions
Keywords: group, segment, organize, discover patterns

Go to the Practice Exam Questions for this topic.

Go to the AI-900 Exam Prep Hub main page.

AI, AI-900, Artificial Intelligence (AI), Deep Learning, Machine Learning (ML), Microsoft Certification January 31, 2026

Practice Questions: Identify features of deep learning techniques (AI-900 Exam Prep)

Practice Questions

Question 1

Which characteristic best distinguishes deep learning from traditional machine learning techniques?

A. Deep learning always produces more accurate results
B. Deep learning uses rule-based logic
C. Deep learning uses neural networks with multiple layers
D. Deep learning does not require training data

Correct Answer: C

Explanation:
Deep learning is defined by the use of multi-layer (deep) neural networks, which allows the model to learn complex patterns. Accuracy is not guaranteed, and deep learning still requires training data.

Question 2

A data scientist is building a system to identify objects in photographs without manually defining features such as edges or shapes. Which approach best supports this requirement?

A. Linear regression
B. Decision trees
C. Deep learning
D. Rule-based classification

Correct Answer: C

Explanation:
Deep learning models automatically extract features from raw data, making them ideal for image recognition scenarios where manual feature engineering is difficult.

Question 3

Which type of data is deep learning particularly well suited to process?

A. Highly structured tabular data only
B. Unstructured data such as images and text
C. Small datasets with few attributes
D. Pre-aggregated numerical summaries

Correct Answer: B

Explanation:
Deep learning excels with unstructured data like images, audio, video, and natural language text — a key exam concept.

Question 4

Which scenario is the best example of a deep learning workload?

A. Predicting house prices using historical averages
B. Grouping customers by age and income
C. Translating spoken language into text
D. Calculating monthly sales totals

Correct Answer: C

Explanation:
Speech-to-text translation relies on deep neural networks trained on large datasets and is a classic deep learning use case.

Question 5

Why do deep learning models typically require large amounts of training data?

A. They rely on predefined rules
B. They use many layers with numerous parameters
C. They only work with structured data
D. They do not support feature reuse

Correct Answer: B

Explanation:
Deep learning models contain many parameters across multiple layers, which requires large datasets to train effectively and avoid overfitting.

Question 6

Which statement accurately describes feature engineering in deep learning?

A. Features must always be manually selected
B. Features are randomly generated
C. Features are automatically learned during training
D. Feature engineering is not possible

Correct Answer: C

Explanation:
A defining feature of deep learning is automatic feature extraction, reducing the need for manual feature engineering.

Question 7

Which Azure workload is most likely to use deep learning techniques?

A. Calculating averages in a SQL database
B. Performing rule-based fraud detection
C. Detecting faces in images
D. Sorting records by date

Correct Answer: C

Explanation:
Computer vision tasks such as face detection rely heavily on deep learning models.

Question 8

Compared to traditional machine learning models, deep learning models generally require:

A. Less computational power
B. No training data
C. More computational resources
D. Fewer model parameters

Correct Answer: C

Explanation:
Deep learning models are computationally intensive, often requiring GPUs and longer training times.

Question 9

Which statement is true about deep learning and structured data?

A. Deep learning cannot process structured data
B. Deep learning is always the best choice for structured data
C. Traditional ML is often sufficient for structured data
D. Structured data requires neural networks

Correct Answer: C

Explanation:
For many structured data problems, traditional machine learning techniques may be simpler and more efficient than deep learning.

Question 10

A model uses an input layer, multiple hidden layers, and an output layer. What type of technique does this describe?

A. Clustering
B. Regression
C. Deep learning
D. Rule-based inference

Correct Answer: C

Explanation:
This layered structure is characteristic of deep neural networks, which form the foundation of deep learning techniques.

Exam Tips for This Topic

Look for keywords like images, speech, text, neural networks, and automatic feature extraction
Avoid choosing deep learning for simple, structured, low-data scenarios
Remember: deep learning ≠ better in all cases

Go to the AI-900 Exam Prep Hub main page.

AI, AI-900, Artificial Intelligence (AI), Deep Learning, Machine Learning (ML), Microsoft Certification January 31, 2026

Identify Features of Deep Learning Techniques (AI-900 Exam Prep)

Where This Fits in the Exam

Exam Domain: Describe fundamental principles of machine learning on Azure (15–20%)
Sub-Domain: Identify common machine learning techniques
Topic: Identify features of deep learning techniques

On the AI-900 exam, deep learning questions focus on what makes deep learning distinct, when it is used, and what types of problems it solves well.

What Is Deep Learning?

Deep learning is a subset of machine learning that uses artificial neural networks with multiple layers (deep neural networks) to learn complex patterns in data.

Inspired by how the human brain works
Uses many layers to extract increasingly abstract features
Particularly effective with large and complex datasets

Key exam idea:
Deep learning uses multi-layer neural networks to automatically learn features from data.

Key Features of Deep Learning Techniques

Multi-Layer Neural Networks

Deep learning models consist of:

An input layer
One or more hidden layers
An output layer

Each layer learns progressively more complex representations of the data.

This “depth” is what differentiates deep learning from traditional machine learning models.

Automatic Feature Extraction

Traditional machine learning often requires manual feature engineering.

Deep learning:

Automatically learns relevant features
Reduces the need for human-designed features
Is well-suited for unstructured data

This is a high-frequency exam point.

Works Well with Unstructured Data

Deep learning excels at handling:

Images
Audio
Video
Natural language text

These data types are difficult for traditional ML models but ideal for deep neural networks.

Requires Large Amounts of Data

Deep learning models typically:

Perform better with large datasets
Require significant training data
Benefit from increased data volume and variety

On the exam, deep learning is often associated with big data scenarios.

High Computational Requirements

Deep learning models:

Require more processing power
Often use GPUs for training
Take longer to train than simpler models

You don’t need hardware details for AI-900 — just recognize that deep learning is computationally intensive.

Common Deep Learning Use Cases

Computer Vision

Image classification
Facial recognition
Object detection

Natural Language Processing

Language translation
Sentiment analysis
Text generation

Speech Recognition

Voice assistants
Speech-to-text systems

These scenarios frequently appear in AI-900 questions tied to deep learning.

Deep Learning vs Traditional Machine Learning

This comparison is commonly tested.

Aspect	Traditional ML	Deep Learning
Feature engineering	Manual	Automatic
Model complexity	Simpler models	Multi-layer neural networks
Data requirements	Smaller datasets	Large datasets
Best for	Structured data	Unstructured data
Compute needs	Lower	Higher

Azure Context for AI-900

In Azure, deep learning is commonly associated with:

Azure Machine Learning
AI services built on deep neural networks
Vision, speech, and language workloads

You are not expected to:

Build neural networks
Choose architectures
Write training code

Focus on identifying features and use cases.

Common Exam Traps and Misconceptions

❌ Deep learning is required for all ML problems
❌ Deep learning works best with small datasets
❌ Deep learning requires manual feature selection
✅ Deep learning excels at complex, unstructured data tasks

Key Takeaways for the Exam

Deep learning uses multi-layer neural networks
It automatically learns features from data
It works best with large datasets
It is ideal for images, text, audio, and video
It requires more computational resources than traditional ML

Go to the Practice Exam Questions for this topic.

Go to the AI-900 Exam Prep Hub main page.

AI, AI-900, Artificial Intelligence (AI), Deep Learning, Microsoft Certification January 31, 2026

Practice Exam Questions: Identify Features of the Transformer Architecture (AI-900 Exam Prep)

Practice Exam Questions

Question 1

What is the primary purpose of the self-attention mechanism in a Transformer model?

A. To reduce the size of the training dataset
B. To allow the model to focus on relevant parts of the input sequence
C. To replace the need for training data
D. To process words strictly in order

Correct Answer: B

Explanation:
Self-attention enables a Transformer to determine which words in a sentence are most relevant to one another, improving context understanding. It does not enforce strict order or reduce dataset size.

Question 2

Which feature allows Transformers to be trained more efficiently than recurrent neural networks (RNNs)?

A. Sequential word processing
B. Parallel processing of input data
C. Manual feature engineering
D. Rule-based language models

Correct Answer: B

Explanation:
Transformers process entire sequences in parallel, unlike RNNs that process tokens sequentially. This makes Transformers faster and more scalable.

Question 3

A key reason Transformers require positional encoding is because they:

A. Use convolutional layers
B. Process all input tokens at the same time
C. Rely on labeled data only
D. Perform unsupervised learning

Correct Answer: B

Explanation:
Because Transformers process words in parallel, positional encoding is needed to preserve information about word order in a sentence.

Question 4

Which type of AI workload most commonly uses Transformer-based models?

A. Time-series forecasting
B. Natural language processing
C. Image compression
D. Robotics control systems

Correct Answer: B

Explanation:
Transformers are primarily used for NLP tasks such as translation, summarization, and conversational AI.

Question 5

Which statement best describes the encoder–decoder architecture used in many Transformer models?

A. Both components generate output text
B. The encoder understands input, and the decoder generates output
C. The decoder trains the encoder
D. Both components store training data

Correct Answer: B

Explanation:
The encoder processes and understands the input sequence, while the decoder generates the output sequence based on that understanding.

Question 6

Why are Transformers better at handling long-range dependencies in text compared to earlier models?

A. They use fewer parameters
B. They rely on handcrafted grammar rules
C. They use attention to relate all words in a sequence
D. They process words one at a time

Correct Answer: C

Explanation:
Self-attention allows Transformers to evaluate relationships between all words in a sentence, regardless of distance.

Question 7

Which Azure scenario is most likely to involve a Transformer-based model?

A. Predicting tomorrow’s stock price
B. Detecting network hardware failures
C. Translating text between languages
D. Calculating average sales per region

Correct Answer: C

Explanation:
Language translation is a classic NLP task that relies heavily on Transformer architectures.

Question 8

What is a major advantage of Transformers over traditional sequence models?

A. They require no training data
B. They eliminate bias automatically
C. They improve scalability and performance
D. They work only with structured data

Correct Answer: C

Explanation:
Transformers scale efficiently due to parallel processing and attention mechanisms, improving performance on large datasets.

Question 9

Which statement about Transformers is TRUE?

A. They are rule-based AI systems
B. They process data strictly sequentially
C. They are a type of deep learning model
D. They are limited to image recognition

Correct Answer: C

Explanation:
Transformers are deep learning architectures commonly used for NLP tasks.

Question 10

Which feature enables a Transformer model to understand the context of a word based on surrounding words?

A. Positional encoding
B. Tokenization
C. Self-attention
D. Data labeling

Correct Answer: C

Explanation:
Self-attention allows the model to weigh the importance of surrounding words when interpreting meaning and context.

Quick Exam Tip

If you see keywords like:

attention
context
parallel processing
language understanding
Azure OpenAI

You’re almost certainly dealing with a Transformer-based model.

Go to the AI-900 Exam Prep Hub main page.

AI, AI-900, Artificial Intelligence (AI), Machine Learning (ML), Microsoft Certification January 31, 2026

Describe How Training and Validation Datasets Are Used in Machine Learning (AI-900 Exam Prep)

This section of the AI-900: Microsoft Azure AI Fundamentals exam focuses on understanding how machine learning models learn from data and how their performance is evaluated. Specifically, it covers the role of training datasets and validation datasets, which are core concepts in supervised machine learning.

This topic appears under: Describe fundamental principles of machine learning on Azure (15–20%) → Describe core machine learning concepts

You are not expected to build or tune models for AI-900, but you must be able to describe the purpose of training and validation datasets and how they differ.

Why Datasets Are Split in Machine Learning

In machine learning, using the same data to both train and evaluate a model can lead to misleading results. To avoid this, datasets are commonly split into separate subsets, each with a distinct purpose.

At a minimum, most machine learning workflows use:

A training dataset
A validation dataset

These datasets help ensure that a model can generalize to new, unseen data.

Training Dataset

A training dataset is the portion of data used to teach the machine learning model how to make predictions.

Key Characteristics of Training Data

Contains both features and labels (in supervised learning)
Used to identify patterns and relationships in the data
Typically makes up the largest portion of the dataset

What Happens During Training

The model makes predictions using the features
Predictions are compared to the known labels
The model adjusts its internal parameters to reduce errors

In Azure Machine Learning, this is the phase where the model “learns” from historical data.

Validation Dataset

A validation dataset is used to evaluate how well the model performs on unseen data during the training process.

Key Characteristics of Validation Data

Separate from the training dataset
Contains features and labels
Used to assess model accuracy and generalization

Why Validation Data Is Important

Helps detect overfitting (when a model memorizes training data)
Provides an unbiased evaluation of model performance
Supports decisions about model selection or improvement

For AI-900, the key idea is that validation data is not used to train the model, only to evaluate it.

Training vs Validation: Key Differences

Aspect	Training Dataset	Validation Dataset
Primary purpose	Teach the model	Evaluate the model
Used to adjust model parameters	Yes	No
Seen by the model during learning	Yes	No
Helps detect overfitting	Indirectly	Yes

Understanding this distinction is essential for AI-900 exam questions.

Common Data Split Ratios

While AI-900 does not test exact percentages, common industry practices include:

70% training / 30% validation
80% training / 20% validation

The exact split depends on dataset size and use case, but the concept is what matters for the exam.

Example Scenario

A company is building a model to predict whether customers will cancel a subscription.

Training dataset:
- Used to teach the model using historical customer behavior and known outcomes
Validation dataset:
- Used to test how accurately the model predicts cancellations for customers it has not seen before

This approach helps ensure the model performs well in real-world scenarios.

Overfitting and Generalization

One of the main reasons for using a validation dataset is to avoid overfitting.

Overfitting occurs when a model performs well on training data but poorly on new data
Validation data helps confirm that the model can generalize beyond the training set

For AI-900, you only need to recognize this relationship, not the mathematical details.

Azure Context for AI-900

In Azure Machine Learning:

Training data is used to train machine learning models
Validation data is used to evaluate model performance during development
This separation supports reliable and responsible AI solutions

Exam Tips for AI-900

If the question mentions learning or adjusting the model, think training dataset
If the question mentions evaluation or performance on unseen data, think validation dataset
Validation data is not used to teach the model
AI-900 focuses on understanding why datasets are separated

Key Takeaways

Training datasets are used to teach machine learning models
Validation datasets are used to evaluate model performance
Separating datasets helps prevent overfitting
Understanding these roles is a core AI-900 exam skill

Go to the Practice Exam Questions for this topic.

Go to the AI-900 Exam Prep Hub main page.

AI, AI-900, Artificial Intelligence (AI), Machine Learning (ML), Microsoft Certification January 31, 2026

Practice Questions: Describe Capabilities of Automated Machine Learning (AI-900 Exam Prep)

Practice Exam Questions

Question 1

What is the primary purpose of Automated Machine Learning (AutoML) in Azure?

A. To replace data scientists
B. To automatically label data
C. To select and optimize machine learning models
D. To deploy models without evaluation

Correct Answer: C

Explanation:
AutoML automatically selects algorithms and tunes parameters to identify the best-performing model for a given dataset.

Question 2

Which machine learning scenarios are supported by Azure Automated Machine Learning?

A. Clustering only
B. Regression and classification
C. Reinforcement learning
D. Rule-based automation

Correct Answer: B

Explanation:
AutoML supports supervised learning scenarios such as regression and classification, which are core to AI-900.

Question 3

How does AutoML reduce the need for deep machine learning expertise?

A. By eliminating the need for training data
B. By automatically selecting models and hyperparameters
C. By generating business requirements
D. By replacing human oversight

Correct Answer: B

Explanation:
AutoML handles model selection and hyperparameter tuning automatically, reducing manual effort and expertise requirements.

Question 4

Which task is handled automatically by Azure AutoML?

A. Defining business objectives
B. Cleaning poor-quality data
C. Hyperparameter tuning
D. Approving model deployment

Correct Answer: C

Explanation:
AutoML automatically adjusts hyperparameters to improve model performance.

Question 5

A team wants to quickly build a sales forecasting model with minimal manual configuration.
Which Azure capability should they use?

A. Azure Cognitive Services
B. Azure Bot Service
C. Automated Machine Learning
D. Azure Logic Apps

Correct Answer: C

Explanation:
AutoML is designed to quickly build supervised ML models, including time-series forecasting.

Question 6

Which statement about Automated Machine Learning is TRUE?

A. AutoML guarantees perfect model accuracy
B. AutoML removes the need for human review
C. AutoML compares multiple models automatically
D. AutoML works only with unlabeled data

Correct Answer: C

Explanation:
AutoML evaluates and compares multiple models to identify the best-performing option.

Question 7

Which Azure service provides Automated Machine Learning capabilities?

A. Azure Functions
B. Azure Machine Learning
C. Azure App Service
D. Azure Synapse Analytics

Correct Answer: B

Explanation:
Automated Machine Learning is a feature within Azure Machine Learning.

Question 8

What is a key benefit of using AutoML?

A. Manual feature engineering
B. Faster model development
C. Elimination of data preparation
D. Guaranteed regulatory compliance

Correct Answer: B

Explanation:
AutoML speeds up model development by automating model selection, tuning, and evaluation.

Question 9

Which of the following is NOT a capability of Automated Machine Learning?

A. Automatic model evaluation
B. Automatic algorithm selection
C. Automatic business decision-making
D. Hyperparameter tuning

Correct Answer: C

Explanation:
AutoML supports model creation and evaluation but does not make business decisions.

Question 10

Why is Automated Machine Learning especially useful for beginners?

A. It removes the need for labeled data
B. It eliminates model deployment steps
C. It simplifies model creation and experimentation
D. It replaces Azure Machine Learning

Correct Answer: C

Explanation:
AutoML simplifies experimentation by automating many steps involved in building machine learning models.

Exam Strategy Tip

On AI-900, think of AutoML as a productivity accelerator:

You provide the data and goal
AutoML handles model selection, tuning, and evaluation
Humans still review and deploy the model

If a question mentions automatic selection, minimal configuration, or quick model building, the answer is might be related to Automated Machine Learning.

Go to the AI-900 Exam Prep Hub main page.

AI, AI-900, Artificial Intelligence (AI), Microsoft Certification January 31, 2026

Practice Questions: Identify Features of Image Classification Solutions (AI-900 Exam Prep)

Practice Questions

Question 1

A company wants to automatically categorize uploaded photos as landscape, food, or people. The location of objects in the image is not required. Which computer vision solution should be used?

A. Object detection
B. Image segmentation
C. Image classification
D. Facial recognition

Correct Answer: C

Explanation:
Image classification assigns one or more labels to an entire image without identifying object locations.

Question 2

Which output is typically returned by an image classification model?

A. Bounding boxes and coordinates
B. Pixel-level masks
C. Labels with confidence scores
D. Audio transcripts

Correct Answer: C

Explanation:
Image classification returns labels that describe the image, usually with confidence or probability scores.

Question 3

Which scenario is the best fit for image classification?

A. Counting the number of people in an image
B. Identifying where objects appear in an image
C. Determining whether an image contains a cat or a dog
D. Tracking a moving object in a video

Correct Answer: C

Explanation:
Image classification is ideal when determining what is in the image, not where it appears.

Question 4

Which Azure service allows you to train a custom image classification model using labeled images?

A. Azure AI Vision
B. Azure OpenAI
C. Azure AI Custom Vision
D. Azure Cognitive Search

Correct Answer: C

Explanation:
Azure AI Custom Vision enables training custom image classification models using user-provided labeled datasets.

Question 5

What is a key difference between image classification and object detection?

A. Image classification requires training; object detection does not
B. Image classification identifies object locations
C. Object detection assigns labels only
D. Image classification analyzes the entire image

Correct Answer: D

Explanation:
Image classification evaluates the whole image and assigns labels, while object detection also locates objects using bounding boxes.

Question 6

Which Azure service provides prebuilt image classification capabilities without requiring model training?

A. Azure AI Custom Vision
B. Azure AI Vision
C. Azure Machine Learning
D. Azure Blob Storage

Correct Answer: B

Explanation:
Azure AI Vision offers prebuilt computer vision models that can classify images without custom training.

Question 7

An image classification solution returns a confidence score of 0.95 for the label Animal. What does this indicate?

A. The model has been retrained
B. The label is incorrect
C. The model is highly confident in the prediction
D. The image contains multiple objects

Correct Answer: C

Explanation:
Confidence scores indicate how certain the model is about its prediction.

Question 8

Which requirement would make image classification insufficient as a solution?

A. Categorizing images by content
B. Identifying whether images contain people
C. Locating objects within an image
D. Tagging images with labels

Correct Answer: C

Explanation:
Image classification does not provide spatial location data. Object detection would be required instead.

Question 9

Which type of machine learning model is most commonly used for image classification?

A. Decision trees
B. Linear regression
C. Convolutional neural networks
D. K-means clustering

Correct Answer: C

Explanation:
Convolutional neural networks (CNNs) are widely used for image classification due to their effectiveness with visual data.

Question 10

Which phrase in an exam question is the strongest indicator that image classification is the correct solution?

A. “Identify and count objects”
B. “Detect faces and emotions”
C. “Assign a category to an image”
D. “Draw bounding boxes”

Correct Answer: C

Explanation:
Keywords such as classify, label, or categorize strongly indicate image classification.

Final AI-900 Exam Reminders

Image classification = labels, not locations
Prebuilt models → Azure AI Vision
Custom labels → Azure AI Custom Vision
Watch for exam “traps” involving bounding boxes

Go to the AI-900 Exam Prep Hub main page.

AI, AI-900, Artificial Intelligence (AI), Microsoft Certification, Natural Language Processing (NLP) January 31, 2026

Practice Questions: Identify Features and Uses for Key Phrase Extraction (AI-900 Exam Prep)

Practice Questions

Question 1

A company wants to automatically identify the main topics discussed in thousands of customer reviews without determining whether the reviews are positive or negative.

Which NLP capability should be used?

A. Sentiment analysis
B. Language detection
C. Key phrase extraction
D. Entity recognition

Correct Answer: C

Explanation:
Key phrase extraction identifies important topics and concepts in text without analyzing emotional tone, making it ideal for summarizing review content.

Question 2

Which output is most likely returned by a key phrase extraction service?

A. A sentiment score between –1 and 1
B. A list of important words or short phrases
C. A detected language code
D. A classification label

Correct Answer: B

Explanation:
Key phrase extraction returns a list of relevant words or phrases that summarize the main ideas of the text.

Question 3

Which Azure service provides key phrase extraction using prebuilt models?

A. Azure Machine Learning
B. Azure AI Vision
C. Azure AI Language
D. Azure Cognitive Search

Correct Answer: C

Explanation:
Key phrase extraction is part of Azure AI Language, which offers prebuilt NLP models accessible via APIs.

Question 4

A support team wants to automatically tag incoming support tickets with topics such as billing, login issues, or performance.

Which NLP capability should they use?

A. Named entity recognition
B. Key phrase extraction
C. Sentiment analysis
D. Speech-to-text

Correct Answer: B

Explanation:
Key phrase extraction identifies important topics in unstructured text, making it suitable for tagging and categorization.

Question 5

Which scenario is NOT a typical use of key phrase extraction?

A. Summarizing the main topics of documents
B. Improving document search and indexing
C. Detecting the emotional tone of text
D. Identifying trending discussion topics

Correct Answer: C

Explanation:
Detecting emotional tone is handled by sentiment analysis, not key phrase extraction.

Question 6

Which statement best describes key phrase extraction for the AI-900 exam?

A. It requires labeled training data
B. It extracts names and dates only
C. It uses pretrained models on unstructured text
D. It classifies text into predefined categories

Correct Answer: C

Explanation:
Key phrase extraction uses pretrained NLP models and works directly on unstructured text without training.

Question 7

A multinational company wants to extract key topics from documents written in multiple languages.

Which feature of Azure AI Language supports this requirement?

A. Custom model training
B. Multi-language support
C. Facial recognition
D. Object detection

Correct Answer: B

Explanation:
Azure AI Language supports multiple languages for key phrase extraction, enabling global text analysis.

Question 8

Which NLP capability focuses on identifying specific items such as names, locations, and dates?

A. Key phrase extraction
B. Sentiment analysis
C. Language detection
D. Entity recognition

Correct Answer: D

Explanation:
Entity recognition extracts specific entities, while key phrase extraction focuses on main topics and concepts.

Question 9

A business wants to quickly understand what large volumes of text are about, without reading every document.

Which benefit of key phrase extraction addresses this need?

A. Emotion detection
B. Automatic topic identification
C. Speech recognition
D. Image analysis

Correct Answer: B

Explanation:
Key phrase extraction automatically identifies important topics, allowing rapid understanding of large text collections.

Question 10

Which responsible AI consideration is most relevant when using key phrase extraction?

A. Identity verification
B. Avoiding misinterpretation of extracted phrases
C. Biometric data protection
D. Facial bias detection

Correct Answer: B

Explanation:
Key phrase extraction outputs are contextual summaries, so users must avoid treating them as definitive conclusions.

Exam Tip Recap 🔑

Often paired with search, tagging, and trend analysis

Key phrase extraction = What is this text about?

It does not analyze sentiment

Uses prebuilt models in Azure AI Language

Go to the AI-900 Exam Prep Hub main page.

AI, AI-900, Artificial Intelligence (AI), Microsoft Certification, Natural Language Processing (NLP) January 31, 2026

Identify Features and Uses for Key Phrase Extraction (AI-900 Exam Prep)

Overview

Key phrase extraction is a Natural Language Processing (NLP) capability that identifies the main topics or important terms within unstructured text. In the context of the AI-900: Microsoft Azure AI Fundamentals exam, you are expected to understand what key phrase extraction does, when to use it, and how it differs from other NLP workloads.

In Azure, key phrase extraction is provided through Azure AI Language using prebuilt models, requiring no custom training.

What Is Key Phrase Extraction?

Key phrase extraction answers the question:

“What is this text mainly about?”

It analyzes text and returns a list of relevant words or short phrases that summarize the core ideas.

Example:

“Azure AI provides cloud-based artificial intelligence services for developers.”

Extracted key phrases might include:

Azure AI
artificial intelligence services
cloud-based
developers

Core Features of Key Phrase Extraction

1. Automatic Topic Identification

The service automatically identifies:

Important concepts
Repeated or emphasized terms
Meaningful noun phrases

This helps users quickly understand large volumes of text.

2. Works with Unstructured Text

Key phrase extraction can be applied to:

Customer reviews
Support tickets
Emails
Social media posts
Articles and documents

No formatting or labeling is required.

3. Prebuilt NLP Models

For AI-900 purposes:

No model training is required
No labeled datasets are needed
The service is accessed via API calls or SDKs

This makes it ideal for rapid implementation.

4. Multi-Language Support

Azure AI Language supports multiple languages for key phrase extraction, making it suitable for global applications.

Common Use Cases

Summarizing Customer Feedback

Organizations can extract key phrases from thousands of customer comments to identify:

Common complaints
Popular features
Emerging issues

Search and Indexing

Key phrases can be used to:

Improve document search
Tag content automatically
Enhance content discoverability

Trend and Topic Analysis

By aggregating extracted phrases, businesses can:

Identify trending topics
Monitor brand mentions
Analyze public sentiment themes

Key Phrase Extraction vs Other NLP Workloads

NLP Capability	Primary Purpose
Key phrase extraction	Identify main topics in text
Sentiment analysis	Determine emotional tone
Language detection	Identify the language used
Entity recognition	Extract specific entities (names, dates, locations)

Understanding these distinctions is critical for AI-900 exam questions.

Typical AI-900 Exam Scenarios

You may see questions describing:

Analyzing large amounts of feedback text
Automatically tagging documents
Identifying main discussion points without understanding emotion

Correct answers will reference:

Key phrase extraction
Azure AI Language
Prebuilt NLP models

Responsible AI Considerations

Although key phrase extraction does not directly analyze people, responsible usage still includes:

Avoiding misinterpretation of extracted phrases
Understanding that output is contextual, not definitive
Using extracted phrases as decision support, not final judgment

Key Takeaways for the AI-900 Exam

Key phrase extraction identifies important topics, not sentiment
It works on unstructured text
It uses pretrained models in Azure AI Language
It complements other NLP workloads rather than replacing them

A strong grasp of when to use key phrase extraction will help you confidently answer AI-900 questions related to Natural Language Processing workloads.

Go to the Practice Exam Questions for this topic.

Go to the AI-900 Exam Prep Hub main page.