Tag: Classification

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.