Azure AI-102 Sample Questions with Explanations

These original sample questions are designed to help you check how the exam topics appear in decision-style prompts. They are not taken from the live exam.

Use these sample questions as a guided self-assessment for Microsoft Certified: Azure AI Engineer Associate (AI-102) topics such as Azure AI services, language and vision workloads, search-backed retrieval, bot integration, responsible AI controls, monitoring, and secure service access. The goal is to practice service-fit reasoning, not memorize isolated product names.

Where these questions fit in the AI-102 guide

The sample set below is part of the Microsoft AI-102 guide path:

• Microsoft certification hub
• Microsoft Azure guides
• AI-102 exam guide
• AI-102 cheat sheet
• AI-102 study plan
• AI-102 FAQ
• AI-102 resources
• AI-102 glossary

AI-102 implementation sample questions

Work through each prompt before opening the explanation. AI-102 questions usually reward the answer that matches the workload, secures the service boundary, and keeps the model or cognitive service measurable in production.

Question 1

Topic: Search-backed answer generation

A support application must answer questions from product manuals and policy documents. The answer must be grounded in current content, include citations, and avoid exposing documents from other departments. Which design is strongest?

• A. Fine-tune a model on all documents and rely on the system prompt to mention citations.
• B. Use retrieval over an indexed knowledge source with department metadata filters, then pass selected passages to the model for a cited response.
• C. Put the most common manual sections in the prompt and update the prompt every month.
• D. Let the model choose documents from shared storage based only on the user’s question.

Best answer: B

Explanation: The scenario needs retrieval, grounding, citations, and access-aware filtering. An indexed knowledge source with metadata filters keeps the source boundary outside the model and lets the application provide only authorized passages for answer generation.

Why the other choices are weaker:

• A makes freshness, citations, and department boundaries unreliable because the content is buried in model behavior.
• C is brittle and cannot handle a changing document corpus.
• D gives selection authority to the model without a clear authorization gate.

What this tests: Choosing retrieval and access filtering when a generative answer must be grounded and governed.

Related topics: Retrieval; Azure AI Search; Grounding; Access control

Question 2

Topic: Language extraction workflow

A claims team receives thousands of customer emails. They need to detect sentiment, extract key phrases, and route negative messages to a priority queue. The team does not need to train a custom natural-language model. What should the solution use first?

• A. A language service pipeline that performs sentiment analysis and key phrase extraction, then routes messages based on the returned scores and entities.
• B. A computer vision service because customer emails are unstructured documents.
• C. A custom speech model because the messages may later be read aloud by agents.
• D. A fine-tuned generative model for every product line before any routing rules are built.

Best answer: A

Explanation: The required tasks are text analytics tasks: sentiment and key phrase extraction. A managed language service is the direct service fit, and the routing rule can use structured results without training a custom model.

Why the other choices are weaker:

• B confuses text analysis with image analysis.
• C solves a possible later audio requirement, not the current email classification problem.
• D adds model-training complexity before the scenario shows a custom-model need.

What this tests: Matching language workloads to the simplest managed capability that returns usable structured signals.

Related topics: Language; Sentiment; Key phrases; Service fit

Question 3

Topic: Secure service credentials

A web app calls multiple Azure AI services. Security review found that API keys are stored in application settings and copied between environments. The team wants to reduce secret exposure and keep access revocable by environment. What is the best improvement?

• A. Use managed identity where supported, grant only required roles or permissions, and store unavoidable secrets in a managed secret store with environment-specific access.
• B. Put all keys in source control but restrict the repository to administrators.
• C. Use the same service key in development, test, and production so rotation is simpler.
• D. Base64-encode keys before adding them to application settings.

Best answer: A

Explanation: AI-102 implementation questions often combine service use with secure access. Managed identity removes stored credentials where supported, and a secret store gives rotation, access control, and environment separation when a key is still required.

Why the other choices are weaker:

• B still exposes long-lived keys in code history.
• C increases blast radius across environments.
• D is encoding, not security.

What this tests: Applying least privilege and secret-management patterns to Azure AI service integrations.

Related topics: Managed identity; Secrets; Least privilege; Security

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