- A
Image captioning to describe transaction screenshots
Why wrong: Image captioning generates descriptions from images — fraud detection analyzes transaction data patterns.
- B
Anomaly detection or classification to flag unusual transaction patterns
Fraud detection uses anomaly detection (unusual patterns) or classification (fraud/not fraud) trained on historical transaction data.
- C
Text generation to create transaction summaries
Why wrong: Generating summaries is generative AI — fraud detection identifies potentially fraudulent transactions from behavioral patterns.
- D
Object detection to verify identity documents
Why wrong: Document verification is a vision task — fraud detection analyzes transaction patterns in financial data.
Question 500 of 1,020
Fraud Detection: Using Anomaly Detection and Classification 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.
Which type of AI workload uses historical transaction data to identify potentially fraudulent transactions in real time?
Quick Answer
The correct answer is anomaly detection and classification, as these AI workloads are specifically designed to identify patterns that deviate from learned normal behavior. In fraud detection, historical transaction data trains a model to understand typical spending habits, and then in real time, the model flags any transaction that falls outside those established patterns as potentially fraudulent. On the Microsoft Azure AI-900 exam, this concept tests your understanding of how anomaly detection differs from other workloads like regression or natural language processing—a common trap is confusing it with simple binary classification, but anomaly detection focuses on outliers rather than predefined categories. A strong memory tip is to think of anomaly detection as the “spot the odd one out” technique, where the model learns what’s normal and screams when something doesn’t fit, making it perfect for real-time fraud flagging.
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
✓
Anomaly detection or classification to flag unusual transaction patterns
Option B is correct because anomaly detection and classification are AI techniques specifically designed to identify patterns that deviate from normal behavior. In fraud detection, historical transaction data is used to train a model that learns typical spending patterns, and then in real time, the model flags transactions that fall outside those learned patterns as potentially fraudulent.
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.
- ✗
Image captioning to describe transaction screenshots
Why it's wrong here
Image captioning generates descriptions from images — fraud detection analyzes transaction data patterns.
- ✓
Anomaly detection or classification to flag unusual transaction patterns
Why this is correct
Fraud detection uses anomaly detection (unusual patterns) or classification (fraud/not fraud) trained on historical transaction data.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Text generation to create transaction summaries
Why it's wrong here
Generating summaries is generative AI — fraud detection identifies potentially fraudulent transactions from behavioral patterns.
- ✗
Object detection to verify identity documents
Why it's wrong here
Document verification is a vision task — fraud detection analyzes transaction patterns in financial data.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse 'text generation' (Option C) with 'report generation' and mistakenly think summarizing transactions is the same as detecting fraud, when in fact fraud detection requires classification or anomaly detection, not natural language generation.
Detailed technical explanation
How to think about this question
Anomaly detection for fraud often uses algorithms like Isolation Forest, Local Outlier Factor, or autoencoders that learn the 'normal' distribution of features such as transaction amount, location, and time. In production, the model scores each new transaction against this learned distribution, and if the anomaly score exceeds a threshold (e.g., 3 standard deviations from the mean), the transaction is flagged for review. A real-world scenario is credit card fraud detection, where a model might flag a $10,000 purchase in a foreign country minutes after a $5 coffee purchase at home, because the velocity and amount deviate from the user's historical behavior.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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: Anomaly detection or classification to flag unusual transaction patterns — Option B is correct because anomaly detection and classification are AI techniques specifically designed to identify patterns that deviate from normal behavior. In fraud detection, historical transaction data is used to train a model that learns typical spending patterns, and then in real time, the model flags transactions that fall outside those learned patterns as potentially fraudulent.
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 →
Same concept, more angles
1 more ways this is tested on AI-900
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. What is 'fraud detection' as an AI workload and what type of ML technique does it typically use?
easy- A.Generating synthetic fraudulent data to train security awareness training content
- ✓ B.Using anomaly detection and classification models to identify fraudulent transactions in real time
- C.Verifying digital signatures on financial documents to confirm their authenticity
- D.Encrypting financial data to prevent fraudsters from intercepting it
Why B: Fraud detection is an AI workload that identifies suspicious or anomalous patterns in transaction data to flag potential fraud. It typically uses anomaly detection (to spot outliers deviating from normal behavior) and classification models (e.g., logistic regression, random forest, or neural networks) to label transactions as legitimate or fraudulent in real time, enabling rapid intervention.
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Last reviewed: Jun 11, 2026
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