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AI-102 · topic practice

Implement generative AI solutions practice questions

Practise Microsoft Azure AI Engineer Associate AI-102 Implement generative AI solutions practice questions — original exam-style scenarios with answer choices, explanations, and analysis of common mistakes.

Courseiva uses original exam-style practice questions designed for learning and revision. The goal is to understand the concepts, recognise exam patterns, and improve through explanations — not memorise copied exam dumps.

Reviewed byJohnson Ajibi· MSc IT Security

20 questionsDomain: Implement generative AI solutions

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What the exam tests

What to know about Implement generative AI solutions

Implement generative AI solutions questions test whether you can apply the concept in context, not just recognise a definition.

How the topic appears in realistic exam-style scenarios.

Which detail in the question changes the correct answer.

How to eliminate plausible but wrong options.

How to connect the question back to the wider exam objective.

Watch out for

Common Implement generative AI solutions exam traps

▸Answering from memory before reading the full scenario.
▸Missing a constraint such as cost, availability, security, scope or command context.
▸Choosing a broad answer when the question asks for the most specific fix.
▸Ignoring why the wrong options are tempting.

Practice set

Implement generative AI solutions questions

20 questions · select your answer, then reveal the explanation

Question 1hardmulti select

Read the full Implement generative AI solutions explanation →

You need to monitor and audit usage of Azure OpenAI Service to ensure compliance with company policies. Which TWO actions should you take?

Trap 1: Use Azure Monitor metrics to track token consumption.

Metrics are aggregated and not suitable for audit.

Trap 2: Apply Azure Policy to restrict model deployments.

Policy enforces rules but does not audit usage.

Trap 3: Connect Azure OpenAI to Microsoft Purview for data lineage.

Purview is for data cataloging, not monitoring.

Study all Implement generative AI solutions common traps →

A
Configure content filtering and review the filtered content logs.
Content filtering logs show when content is blocked.
B
Enable diagnostic settings to stream logs to a Log Analytics workspace.
Diagnostic logs capture detailed request and response data.
C
Use Azure Monitor metrics to track token consumption.
Why wrong: Metrics are aggregated and not suitable for audit.
D
Apply Azure Policy to restrict model deployments.
Why wrong: Policy enforces rules but does not audit usage.
E
Connect Azure OpenAI to Microsoft Purview for data lineage.
Why wrong: Purview is for data cataloging, not monitoring.

Implement generative AI solutions practice questions

What to know about Implement generative AI solutions

Common Implement generative AI solutions exam traps

Implement generative AI solutions questions

You need to monitor and audit usage of Azure OpenAI Service to ensure compliance with company policies. Which TWO actions should you take?

You are deploying a generative AI application using Azure OpenAI Service. The application must generate responses in multiple languages while maintaining high accuracy. You need to minimize token usage. Which approach should you recommend?

Which THREE components are required to build a custom copilot using Microsoft Copilot Studio that can answer questions from a SharePoint document library?

You are developing a generative AI solution that uses Azure OpenAI Service. You need to control the creativity of the generated responses. Which parameter should you adjust?

You are designing a generative AI solution that uses Azure OpenAI Service. The solution must not generate responses that include personally identifiable information (PII). Which TWO configurations should you implement? (Choose two.)

Your organization is deploying a generative AI solution using Azure AI Foundry. The solution must comply with responsible AI principles, including fairness and transparency. Which combination of tools should you use to assess and mitigate bias in the model?

A company wants to generate personalized product descriptions for its e-commerce site using Azure OpenAI. They need to ensure the model's output adheres to brand guidelines and does not generate prohibited content. Which approach should they use?

A healthcare startup is developing a chatbot that uses Azure OpenAI to answer patient questions. They need to ensure that the chatbot only uses information from their verified medical database and does not generate unsupported medical advice. What is the best approach?

A developer wants to deploy a custom generative AI model using Azure Machine Learning. Which compute target should they choose for low-latency real-time inference?

A company uses Azure OpenAI to generate code snippets. They notice that the model sometimes produces code that uses deprecated APIs. They want to minimize this without retraining the model. What should they do?

A financial services firm wants to use Azure OpenAI to generate investment advice summaries. They must ensure that the model does not produce any advice that could be interpreted as personalized financial advice. What is the most effective strategy?

A developer wants to use Azure OpenAI to generate text from a prompt. Which parameter controls the diversity of the generated output?

A company is using Azure OpenAI to generate customer support responses. They want to ensure the model does not use any personally identifiable information (PII) in its outputs. What should they implement?

A research lab wants to use Azure OpenAI to generate synthetic data for training a model. They need to generate a large volume of data quickly and cost-effectively. Which approach should they use?

Which TWO actions should you take to reduce the cost of using Azure OpenAI for a chatbot that handles high traffic?

Which THREE factors should you consider when selecting a model for a generative AI solution on Azure?

Which TWO Azure services can be used together with Azure OpenAI to implement a Retrieval-Augmented Generation (RAG) solution?

A company is building a chatbot using Azure OpenAI Service to answer customer queries. The chatbot must not generate harmful or offensive content. Which Azure AI service should be integrated to filter inappropriate content?

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