A company has a generative AI application that is experiencing intermittent failures. They need to identify the root cause, but they have not implemented tracing. They want to add tracing without disrupting the existing application. Which approach should they take?

Deploy a new version of the application with tracing enabled

Use Azure Log Analytics to query existing logs

Use Application Insights SDK to add custom telemetry to the application code

Enable Azure Monitor for the entire subscription

A team is using a generative AI model to generate responses in a customer service chatbot. The responses are often too verbose and include irrelevant details. They want to make the responses more concise and focused. They have a set of example responses that are considered ideal. What is the most effective way to achieve this?

Fine-tune the model on a large dataset of customer service conversations

Increase the max tokens to allow longer responses and set a high frequency penalty

Use a system message that says 'Be concise' and set the temperature to 1.0

Add the ideal responses as few-shot examples and set a low temperature to keep the model focused

A manufacturing company needs to run a generative AI model on edge devices in factories with intermittent internet connectivity. They want to package the model for easy deployment and updates. Which approach should they use?

Store the model in Azure Blob Storage and download it on demand

Deploy the model to Azure Kubernetes Service (AKS)

Deploy the model as a container using Azure IoT Edge

Use Azure Functions to run the model in the cloud

A team has deployed a generative AI model that generates customer service responses. They have collected a large amount of user feedback indicating that the model sometimes provides incorrect information. They want to systematically analyze the failures and then improve the model. They have limited compute resources for fine-tuning. What is the most effective approach?

Immediately fine-tune the model on all the feedback data to correct the errors

Use a larger base model and deploy it without fine-tuning

Analyze the feedback to identify common error patterns, then use prompt engineering to address those patterns before considering fine-tuning

Increase the temperature to get more diverse responses and then select the best ones

A developer is using a generative AI model to generate code snippets. The output is often repetitive and includes unnecessary comments. They want to reduce repetition while keeping the code functional. Which parameter adjustments should they make?

Decrease the frequency penalty and increase the temperature

Increase the max tokens and set the top-p to 0.5

Set the presence penalty to 0 and increase the temperature

Increase the frequency penalty and decrease the temperature

Microsoft Certified:Azure AI Engineer Associate

Domain 2Objective 3

Optimize and Operationalize a Generative AI Solution AI-102 Practice Questions (Page 2)

Part of the Implement generative AI solutions domain, which accounts for 15-20% of the AI-102 exam. Microsoft does not publish an official question count, but from its 100-minute exam (~40–65 total, ~6–13 in this domain), expect 2–4 from this objective — we provide 47 practice questions to prepare you well beyond it. (estimate)

47questions here

10free pages

9concepts

15-20%of the exam

Questions 6–10

6expert · hard
A company has a generative AI application that is experiencing intermittent failures. They need to identify the root cause, but they have not implemented tracing. They want to add tracing without disrupting the existing application. Which approach should they take?
Select an answer first
7expert · hard
A team is using a generative AI model to generate responses in a customer service chatbot. The responses are often too verbose and include irrelevant details. They want to make the responses more concise and focused. They have a set of example responses that are considered ideal. What is the most effective way to achieve this?
Select an answer first
8application · medium
A manufacturing company needs to run a generative AI model on edge devices in factories with intermittent internet connectivity. They want to package the model for easy deployment and updates. Which approach should they use?
Select an answer first
9expert · hard
A team has deployed a generative AI model that generates customer service responses. They have collected a large amount of user feedback indicating that the model sometimes provides incorrect information. They want to systematically analyze the failures and then improve the model. They have limited compute resources for fine-tuning. What is the most effective approach?
Select an answer first
10application · medium
A developer is using a generative AI model to generate code snippets. The output is often repetitive and includes unnecessary comments. They want to reduce repetition while keeping the code functional. Which parameter adjustments should they make?
Select an answer first

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Optimize and Operationalize a Generative AI Solution AI-102 Practice Questions (Page 2)

Questions 6–10

A company has a generative AI application that is experiencing intermittent failures. They need to identify the root cause, but they have not implemented tracing. They want to add tracing without disrupting the existing application. Which approach should they take?

A manufacturing company needs to run a generative AI model on edge devices in factories with intermittent internet connectivity. They want to package the model for easy deployment and updates. Which approach should they use?

A developer is using a generative AI model to generate code snippets. The output is often repetitive and includes unnecessary comments. They want to reduce repetition while keeping the code functional. Which parameter adjustments should they make?

Keep studying — Implement generative AI solutions