Category: AI

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

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

Practice Exam Questions

Question 1

A real estate company wants to predict the selling price of a house based on its size, location, and age.

Which machine learning technique should be used?

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

Correct Answer: C

Explanation:
The output is a numeric value (price), which makes this a regression scenario.

Question 2

A business wants to estimate the number of hours it will take to complete a project based on historical project data.

Which type of machine learning is most appropriate?

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

Correct Answer: A

Explanation:
Estimating time in hours is predicting a numeric value, which is a regression task.

Question 3

Which scenario is best suited for regression?

A. Determining whether a transaction is fraudulent
B. Grouping customers based on purchasing behavior
C. Predicting monthly sales revenue
D. Assigning customers to loyalty tiers

Correct Answer: C

Explanation:
Monthly sales revenue is a continuous numeric value, making regression the correct choice.

Question 4

An AI model predicts tomorrow’s temperature based on historical weather data.

What type of machine learning problem is this?

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

Correct Answer: B

Explanation:
Temperature is a numeric measurement, so this is a regression problem.

Question 5

A company wants to predict how many units of a product will be sold next month.

Which machine learning technique should be used?

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

Correct Answer: A

Explanation:
The output is a quantity (number of units), which is best handled by regression.

Question 6

Which statement best describes a regression model?

A. It assigns data points to categories
B. It predicts continuous numeric values
C. It groups unlabeled data
D. It identifies unusual data points

Correct Answer: B

Explanation:
Regression models are used to predict numeric values, such as prices or quantities.

Question 7

An organization uses historical data to estimate the fuel consumption of delivery vehicles.

What type of machine learning scenario is this?

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

Correct Answer: C

Explanation:
Fuel consumption is a numeric measurement, making this a regression scenario.

Question 8

Which output value most strongly indicates a regression problem?

A. Approved / Rejected
B. High / Medium / Low
C. Fraud / Not Fraud
D. 245.7

Correct Answer: D

Explanation:
A precise numeric output (245.7) indicates a regression scenario.

Question 9

A model predicts delivery times in hours based on distance, traffic, and weather.

Which machine learning technique is being used?

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

Correct Answer: B

Explanation:
Delivery time in hours is a continuous numeric value, so regression is appropriate.

Question 10

On the AI-900 exam, which keyword most often signals a regression scenario?

A. Classify
B. Group
C. Detect
D. Estimate

Correct Answer: D

Explanation:
Words like estimate, predict, or forecast typically indicate regression problems.

Exam-Day Tip

If a machine learning related question asks “how much,” “how many,” or “how long”, the answer is typically Regression related.

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

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

Identify Regression 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 regression machine learning scenarios

On the AI-900 exam, regression questions are about recognizing when regression is the appropriate technique, not building or tuning models.

What Is Regression in Machine Learning?

Regression is a type of supervised machine learning used to predict a numerical (continuous) value.

  • The model learns from labeled training data
  • The output is a number, not a category
  • The goal is to predict how much, how many, or how long

Key exam rule:
If the output is a number, the scenario is almost always regression.

Characteristics of Regression Scenarios

A regression machine learning workload typically involves:

  • Historical data with known outcomes
  • One or more input features
  • A continuous numeric output
  • Predicting future values based on patterns in data

Examples of numeric outputs:

  • Price
  • Temperature
  • Revenue
  • Distance
  • Duration
  • Quantity

Common Regression Use Cases

Price and Cost Prediction

  • Predicting house prices
  • Estimating insurance premiums
  • Forecasting product costs

Forecasting and Trends

  • Predicting future sales revenue
  • Estimating energy consumption
  • Forecasting website traffic

Measurements and Quantities

  • Predicting delivery time
  • Estimating fuel efficiency
  • Calculating demand levels

All of these scenarios involve predicting a numeric value, making them regression problems.

Regression vs Other Machine Learning Techniques

Understanding the difference between regression and other ML techniques is critical for AI-900.

TechniqueOutput TypeExample
RegressionNumeric valuePredicting house price
ClassificationCategory or labelApproving or denying a loan
ClusteringGroup assignmentSegmenting customers
Anomaly detectionUnusual behaviorDetecting fraud

Exam tip:
“Yes/No”, “True/False”, or named labels → Classification
A number or measurement → Regression

Example Exam Scenarios

Scenario 1

A company wants to predict the monthly electricity usage of buildings based on historical data.

  • Output: Electricity usage (kWh)
  • ML Technique: Regression

Scenario 2

A real estate company wants to estimate the selling price of homes based on size, location, and age.

  • Output: Price
  • ML Technique: Regression

Scenario 3

A logistics company wants to estimate delivery time for packages.

  • Output: Time (hours or days)
  • ML Technique: Regression

Azure Context for AI-900

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

  • Training models using historical datasets
  • Predicting numeric outcomes
  • Evaluating prediction accuracy

You are not expected to:

  • Write code
  • Choose algorithms
  • Tune hyperparameters

Focus on recognition, not implementation.

Common Exam Traps and Misconceptions

  • ❌ Predicting categories like high / medium / low → Classification
  • ❌ Grouping similar items without labels → Clustering
  • ❌ Detecting rare events → Anomaly detection
  • ✅ Predicting a number → Regression

Key Takeaways for the Exam

  • Regression predicts numeric values
  • It is a supervised learning technique
  • Look for words like predict, estimate, forecast
  • Outputs are continuous values, not categories
  • Regression is commonly used for prices, quantities, and time

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

Additional Material: Regression vs Classification vs Clustering (AI-900 Exam Prep)

Here is some additional information to help you prepare for the AI-900 or can be used just to solidify your knowledge of these concepts.

Machine Learning Techniques Comparison Table

AspectRegressionClassificationClustering
Type of LearningSupervisedSupervisedUnsupervised
Primary GoalPredict a numeric valuePredict a category or labelGroup similar data points
Output TypeContinuous numberDiscrete categoryCluster/group assignment
Labeled Training DataYesYesNo
Key Question AnsweredHow much? How many? How long?Which category? Yes or No?Which items are similar?
Common KeywordsPredict, estimate, forecastClassify, assign, detectGroup, segment, organize
Typical Output ExamplesPrice, temperature, revenue, timeApproved/Rejected, Spam/Not spamCustomer segments, usage groups
Example ScenarioPredict house pricesDetect fraudulent transactionsSegment customers by behavior
AI-900 Exam FocusIdentifying numeric predictionsIdentifying label predictionsIdentifying pattern discovery
Common Exam TrapConfusing ranges with categoriesTreating Yes/No as numericAssuming labels exist

Quick Visual Memory Trick

  • Regression → 📈 Numbers on a line
  • Classification → 🏷️ Named buckets
  • Clustering → 🧩 Natural groupings

Side-by-Side Example

Imagine a retail company:

Business QuestionTechnique
“What will next month’s revenue be?”Regression
“Will this customer churn?”Classification
“Which customers behave similarly?”Clustering

Common AI-900 Exam Pitfalls to Avoid

  • ❌ High / Medium / Low → Classification, not regression
  • ❌ Yes / No → Classification, not regression
  • ❌ Grouping without predefined labels → Clustering
  • ❌ Predicting quantities → Regression

Exam-Day Decision Shortcut

Ask yourself one question:

“Is the output a number?”

  • Yes → Regression
  • No, it’s a label → Classification
  • No labels, just groups → Clustering

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

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

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

Practice Exam Questions

Question 1

A bank wants to determine whether a credit card transaction is fraudulent.

Which machine learning technique should be used?

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

Correct Answer: B

Explanation:
The output is Fraud / Not Fraud, which is a category. Predicting categories is a classification task.

Question 2

An organization wants to predict whether a customer will renew their subscription.

Which type of machine learning problem is this?

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

Correct Answer: B

Explanation:
The outcome is Yes / No, which makes this a binary classification scenario.

Question 3

Which of the following scenarios is best suited for classification?

A. Predicting the price of a product
B. Grouping customers based on behavior
C. Determining if an email is spam
D. Estimating delivery time

Correct Answer: C

Explanation:
Spam detection involves assigning emails to Spam or Not Spam categories, which is classification.

Question 4

An AI system categorizes customer support tickets into predefined issue types.

What type of machine learning technique is being used?

A. Regression
B. Classification
C. Clustering
D. Time-series forecasting

Correct Answer: B

Explanation:
The system assigns each ticket to a known category, which is classification.

Question 5

Which output value most clearly indicates a classification scenario?

A. 128.5
B. 4.2 hours
C. High risk
D. 99.7

Correct Answer: C

Explanation:
High risk is a label, not a numeric value, indicating classification.

Question 6

A model predicts whether a customer will default on a loan.

Which machine learning approach is most appropriate?

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

Correct Answer: B

Explanation:
Default / Not Default is a binary label, making this a classification problem.

Question 7

Which scenario represents multi-class classification?

A. Predicting house prices
B. Detecting unusual network traffic
C. Assigning images to animal types
D. Grouping products by sales patterns

Correct Answer: C

Explanation:
Assigning images to multiple animal types (cat, dog, bird) is multi-class classification.

Question 8

A healthcare system predicts whether a patient is at low, medium, or high risk.

Which type of machine learning is being used?

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

Correct Answer: B

Explanation:
Low / Medium / High are categories, not numeric values, so this is classification.

Question 9

Which statement best describes classification models?

A. They predict continuous numeric values
B. They group unlabeled data
C. They assign inputs to predefined categories
D. They detect rare anomalies

Correct Answer: C

Explanation:
Classification models assign data points to predefined labels or categories.

Question 10

On the AI-900 exam, which keyword most strongly indicates a classification scenario?

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

Correct Answer: C

Explanation:
Categorize indicates assigning labels, which is classification.

Exam-Day Tip

For machine learning related questions, if the question describes …

  • Yes / No decisions
  • Named labels
  • Risk levels or categories

… the correct answer is likely related to Classification.

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

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

Identify Classification 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 classification machine learning scenarios

On the AI-900 exam, classification questions test your ability to recognize when classification is the appropriate machine learning technique, not how to build models.

What Is Classification in Machine Learning?

Classification is a type of supervised machine learning used to predict a category, class, or label.

  • The model is trained on labeled data
  • The output is discrete, not numeric
  • The goal is to decide which category something belongs to

Key exam rule:
If the output is a label or category, the scenario is classification.

Characteristics of Classification Scenarios

A classification workload typically includes:

  • Historical data with known labels
  • Input features used to make predictions
  • A finite set of possible outcomes
  • Binary or multi-class results

Common classification outputs:

  • Yes / No
  • True / False
  • Approved / Rejected
  • Spam / Not Spam
  • High Risk / Low Risk

Binary vs Multi-Class Classification

Binary Classification

  • Only two possible outcomes
  • Examples:
    • Fraud / Not Fraud
    • Pass / Fail
    • Churn / No Churn

Multi-Class Classification

  • More than two categories
  • Examples:
    • Product category (electronics, clothing, food)
    • Support ticket priority (low, medium, high)
    • Image labels (cat, dog, bird)

Both are classification scenarios on the AI-900 exam.

Common Classification Use Cases

Decision-Based Predictions

  • Loan approval decisions
  • Insurance claim approval
  • Credit risk classification

Detection and Filtering

  • Spam email detection
  • Fraud detection
  • Content moderation

Categorization

  • Customer churn prediction
  • Sentiment categories (positive, neutral, negative)
  • Product classification

All of these involve choosing a label, not predicting a number.

Classification vs Other ML Techniques

Understanding how classification differs from regression and clustering is critical for AI-900.

TechniqueOutputExample
RegressionNumeric valuePredicting house price
ClassificationCategory or labelApproving a loan
ClusteringGroup assignmentCustomer segmentation

Exam tip:
If the answer choices include Yes/No, True/False, or named groups, think Classification.

Example Exam Scenarios

Scenario 1

A bank wants to determine whether a transaction is fraudulent.

  • Output: Fraud / Not Fraud
  • ML Technique: Classification

Scenario 2

A company wants to predict whether a customer will cancel their subscription.

  • Output: Cancel / Not Cancel
  • ML Technique: Classification

Scenario 3

An AI system categorizes customer support tickets into predefined issue types.

  • Output: Issue category
  • ML Technique: Classification

Azure Context for AI-900

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

  • Training models with labeled datasets
  • Predicting predefined categories
  • Evaluating model accuracy

You are not required to:

  • Select algorithms
  • Write code
  • Configure Azure services

Focus on recognizing the technique, not implementing it.

Common Exam Traps and Misconceptions

  • ❌ Predicting a numeric score → Regression
  • ❌ Grouping data without labels → Clustering
  • ❌ Predicting ranges like High / Medium / Low → Classification, not regression
  • ✅ Predicting labels or categories → Classification

Key Takeaways for the Exam

  • Classification predicts categories or labels
  • It is a supervised learning technique
  • Outputs are discrete, not numeric
  • Binary and multi-class scenarios are both classification
  • Look for keywords like classify, detect, assign, categorize

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

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

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.

TechniqueLabeled DataOutputExample
RegressionYesNumeric valuePredicting house price
ClassificationYesCategory or labelApproving a loan
ClusteringNoGroup or clusterCustomer 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.

TechniqueLabeled DataOutputExample
RegressionYesNumeric valuePredicting house price
ClassificationYesCategory or labelApproving a loan
ClusteringNoGroup or clusterCustomer 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

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

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.

AspectTraditional MLDeep Learning
Feature engineeringManualAutomatic
Model complexitySimpler modelsMulti-layer neural networks
Data requirementsSmaller datasetsLarge datasets
Best forStructured dataUnstructured data
Compute needsLowerHigher

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.

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

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.

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