- A
Translating customer support emails from Spanish to English
Why wrong: Email translation is an NLP workload — anomaly detection identifies statistical outliers, not language conversion.
- B
Automatically identifying fraudulent credit card transactions that deviate from a customer's normal patterns
Fraud detection is anomaly detection — identifying transactions that statistically deviate from established normal behaviour patterns.
- C
Generating product descriptions from a list of specifications
Why wrong: Content generation is a generative AI workload — anomaly detection finds outliers in data, not creates new content.
- D
Classifying customer reviews as positive or negative
Why wrong: Review sentiment classification is NLP — anomaly detection finds unusual patterns, not categories.
AI-900 Practice Question: Describe Artificial Intelligence workloads and considerations
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.
Which of the following is an example of 'anomaly detection' as an AI workload?
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
Automatically identifying fraudulent credit card transactions that deviate from a customer's normal patterns
Anomaly detection identifies data points that deviate significantly from the norm. In this case, fraudulent credit card transactions are detected because they do not match the customer's typical spending patterns, which is a classic use case for anomaly detection in AI workloads.
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.
- ✗
Translating customer support emails from Spanish to English
Why it's wrong here
Email translation is an NLP workload — anomaly detection identifies statistical outliers, not language conversion.
- ✓
Automatically identifying fraudulent credit card transactions that deviate from a customer's normal patterns
Why this is correct
Fraud detection is anomaly detection — identifying transactions that statistically deviate from established normal behaviour patterns.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Generating product descriptions from a list of specifications
Why it's wrong here
Content generation is a generative AI workload — anomaly detection finds outliers in data, not creates new content.
- ✗
Classifying customer reviews as positive or negative
Why it's wrong here
Review sentiment classification is NLP — anomaly detection finds unusual patterns, not categories.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse anomaly detection with classification (Option D) because both involve identifying unusual items, but classification requires labeled training data for known categories, whereas anomaly detection focuses on deviations from a learned norm without predefined labels for anomalies.
Detailed technical explanation
How to think about this question
Anomaly detection often uses unsupervised learning algorithms like Isolation Forest, One-Class SVM, or autoencoders to model normal behavior and flag outliers. In fraud detection, these models analyze transaction features such as amount, location, and time, and can adapt to evolving patterns without requiring labeled fraud data for every new attack vector.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
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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: Automatically identifying fraudulent credit card transactions that deviate from a customer's normal patterns — Anomaly detection identifies data points that deviate significantly from the norm. In this case, fraudulent credit card transactions are detected because they do not match the customer's typical spending patterns, which is a classic use case for anomaly detection in AI workloads.
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
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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.
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