Tag: Regression

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.