- A
Analysing customer complaints to identify the root cause of service dissatisfaction
Why wrong: Root cause analysis of complaints is analytics/NLP — churn prediction uses ML to forecast which customers will leave.
- B
Using supervised classification to predict which customers are likely to cancel or become inactive
Churn prediction trains on labelled historical data (churned/retained) — enabling proactive retention targeting of high-risk customers.
- C
Detecting when a customer has already churned based on their last login date
Why wrong: Detecting past churn is rule-based logic — churn prediction uses ML to identify at-risk customers before they leave.
- D
Using NLP to understand why customers write negative reviews before leaving
Why wrong: Negative review analysis is sentiment + topic modelling — churn prediction uses behavioural and transactional features, not text.
What Is Customer Churn Prediction in AI?
This AI-900 practice question tests your understanding of describe artificial intelligence workloads and considerations. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
What is 'customer churn prediction' as an AI workload and what ML type does it use?
Quick Answer
The answer is supervised classification, because customer churn prediction in AI is a workload that uses historical customer data—such as usage patterns, support interactions, and billing history—to train a model that assigns a binary label (churn or not churn) to new customers. This makes it a supervised classification task, where the model learns from labeled examples to predict which customers are likely to cancel or become inactive. On the Microsoft Azure AI Fundamentals AI-900 exam, this concept tests your understanding of how supervised learning applies to real-world business problems, often appearing in scenario-based questions that ask you to match the workload to the correct machine learning type. A common trap is confusing it with regression or unsupervised clustering, but remember: churn prediction always involves a clear target label (churn vs. no churn), so it’s classification, not prediction of a numeric value. Memory tip: “Churn classification = binary label decision.”
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Using supervised classification to predict which customers are likely to cancel or become inactive
Customer churn prediction is a supervised machine learning workload where historical customer data (e.g., usage patterns, support interactions, billing history) is used to train a classification model. The model learns to assign a binary label (churn or not churn) to new customers, making it a supervised classification task. This directly matches option B, which correctly identifies the use of supervised classification to predict likely churners.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Analysing customer complaints to identify the root cause of service dissatisfaction
Why it's wrong here
Root cause analysis of complaints is analytics/NLP — churn prediction uses ML to forecast which customers will leave.
- ✓
Using supervised classification to predict which customers are likely to cancel or become inactive
Why this is correct
Churn prediction trains on labelled historical data (churned/retained) — enabling proactive retention targeting of high-risk customers.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Detecting when a customer has already churned based on their last login date
Why it's wrong here
Detecting past churn is rule-based logic — churn prediction uses ML to identify at-risk customers before they leave.
- ✗
Using NLP to understand why customers write negative reviews before leaving
Why it's wrong here
Negative review analysis is sentiment + topic modelling — churn prediction uses behavioural and transactional features, not text.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse descriptive analytics (analyzing why churn happened) with predictive analytics (forecasting who will churn), leading them to pick option A or D, which describe post-hoc analysis rather than supervised classification.
Detailed technical explanation
How to think about this question
Under the hood, churn prediction models often use algorithms like logistic regression, random forest, or gradient boosting (e.g., XGBoost) trained on labeled historical data where 'churn' is defined by a business rule (e.g., no login for 90 days). A subtle behavior is class imbalance—churn events are often rare (e.g., 5% of customers), requiring techniques like SMOTE oversampling or cost-sensitive learning to avoid a model that always predicts 'not churn'. In a real-world SaaS scenario, the model might ingest features like days since last login, support ticket count, and payment failure history to output a churn probability score used for proactive retention campaigns.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
- →
Describe Artificial Intelligence workloads and considerations — study guide chapter
Learn the concepts, then practise the questions
- →
Describe Artificial Intelligence workloads and considerations practice questions
Targeted practice on this topic area only
- →
All AI-900 questions
1,020 questions across all exam domains
- →
Microsoft Azure AI Fundamentals AI-900 study guide
Full concept coverage aligned to exam objectives
- →
AI-900 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related AI-900 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Describe Artificial Intelligence workloads and considerations practice questions
Practise AI-900 questions linked to Describe Artificial Intelligence workloads and considerations.
Describe fundamental principles of machine learning on Azure practice questions
Practise AI-900 questions linked to Describe fundamental principles of machine learning on Azure.
Describe features of computer vision workloads on Azure practice questions
Practise AI-900 questions linked to Describe features of computer vision workloads on Azure.
Describe features of Natural Language Processing workloads on Azure practice questions
Practise AI-900 questions linked to Describe features of Natural Language Processing workloads on Azure.
Describe features of generative AI workloads on Azure practice questions
Practise AI-900 questions linked to Describe features of generative AI workloads on Azure.
AI-900 fundamentals practice questions
Practise AI-900 questions linked to AI-900 fundamentals.
AI-900 scenario practice questions
Practise AI-900 questions linked to AI-900 scenario.
AI-900 troubleshooting practice questions
Practise AI-900 questions linked to AI-900 troubleshooting.
Practice this exam
Start a free AI-900 practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Questions learners often ask
What does this AI-900 question test?
Describe Artificial Intelligence workloads and considerations — This question tests Describe Artificial Intelligence workloads and considerations — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Using supervised classification to predict which customers are likely to cancel or become inactive — Customer churn prediction is a supervised machine learning workload where historical customer data (e.g., usage patterns, support interactions, billing history) is used to train a classification model. The model learns to assign a binary label (churn or not churn) to new customers, making it a supervised classification task. This directly matches option B, which correctly identifies the use of supervised classification to predict likely churners.
What should I do if I get this AI-900 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Keep practising
More AI-900 practice questions
- A company deploys an AI system to screen job applications. The system is a complex neural network that learns patterns f…
- What is 'model versioning' and why is it essential in MLOps?
- What is 'AI transparency' in Microsoft's Responsible AI principles?
- A company uses Azure OpenAI Service to generate marketing copy. They notice that sometimes the generated text contains r…
- A data scientist is training a regression model to predict house prices using features like square footage, number of be…
- A company uses Azure OpenAI Service to generate marketing copy. They want to ensure that the generated text does not con…
Last reviewed: Jun 11, 2026
This AI-900 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-900 exam.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.