AI-900 Practice Question: Describe Artificial Intelligence workloads and considerations
What is 'fraud detection' as an AI workload and what type of ML technique does it typically use?
⚠ Common exam trap
It's easy for candidates to confuse data security techniques (encryption, digital signatures) or data preparation steps (synthetic data generation) with the core AI workload of detecting fraud through anomaly detection and classification.
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 anomaly detection and classification models to identify fraudulent transactions in real time
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.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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Generating synthetic fraudulent data to train security awareness training content
Why it's wrong here
This option describes creating security awareness training materials with synthetic data, which is a content-generation task, not an AI fraud detection system. Although synthetic data can be used to train fraud models, the stated purpose here is training people, not analyzing live payment streams for suspicious activity. Fraud detection requires continuously scoring real transactions against learned patterns.
- ✓
Using anomaly detection and classification models to identify fraudulent transactions in real time
Why this is correct
Fraud detection in this scenario is a real-time AI workload that combines anomaly detection to flag transactions deviating from a user's normal behavior and classification models to assign a probability that a transaction is fraudulent or legitimate. These models operate on live transaction streams, scoring each event quickly enough to block or flag transactions before settlement. This is a core example of AI applied to financial services.
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Verifying digital signatures on financial documents to confirm their authenticity
Why it's wrong here
Digital signature verification uses public-key cryptography to confirm that a document was signed by a specific private key and has not been altered, which is an authentication and integrity mechanism rather than machine learning. It does not model transaction behavior or detect unusual spending patterns across accounts. AI-based fraud detection identifies anomalies in transaction behavior, whereas signature verification merely proves who signed a given file.
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Encrypting financial data to prevent fraudsters from intercepting it
Why it's wrong here
Encryption protects financial data by transforming it into ciphertext so that intercepted messages are unreadable, but it is a cryptographic security control, not an analytical AI system. It does not inspect transaction patterns or learn behavioral baselines, so it cannot decide whether a particular charge is part of a fraud ring. AI fraud detection works after data is received and analyzes the transaction itself for suspicious characteristics.
Go deeper
Related to this question
Learn chapter
Regression and Classification
Key term
Classification
Classification is a supervised machine learning technique used to predict a category or class label for new data based on patterns learned from labeled training data.
Key term
Regression
Regression is a type of machine learning algorithm that predicts a continuous numeric output based on input data, used to model relationships between variables.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.