Which prompt technique is commonly used to implement model reflection?

Asking the model to 'review your previous answer and identify any mistakes'

Setting a low temperature to make output deterministic

Providing a few examples of desired output

Using a stop sequence to end generation early

An organization has deployed an Azure OpenAI model in production. The operations team needs to track token consumption, latency, and error rates over time to identify cost trends and performance degradation. Which monitoring approach should they implement?

Enable Azure Monitor and Application Insights for the Azure OpenAI resource

Use Azure Log Analytics only to query model responses

Enable diagnostic settings to send logs to a storage account

Use Azure Advisor to monitor model performance

A company wants to deploy a generative AI model to edge devices in remote locations with intermittent connectivity. The model must be able to run offline, but the team also wants to update the model remotely when connectivity is available. They also need to monitor the model's performance on the edge devices. What is the best approach?

Use Azure IoT Edge with a containerized model and configure the IoT Edge device to pull updates from Azure Container Registry when connected

Use Azure Functions on the edge device with a local cache for offline inference

Deploy the model to Azure Container Instances and access it via a VPN

Deploy the model to Azure Stack Edge and manually update it via USB drives

A team has deployed a generative AI model that sometimes produces biased or incorrect answers. They want to systematically analyze the model's outputs to identify patterns of failure and then improve the model. What is the most effective first step?

Increase the temperature parameter to get more varied responses and then compare them

Deploy the model to a separate staging environment and run load tests

Fine-tune the model immediately with a small set of corrected examples

Collect a diverse set of prompts and manually review the model's responses to identify common error patterns

Microsoft Certified:Azure AI Engineer Associate

Domain 2Objective 3

Optimize and Operationalize a Generative AI Solution AI-102 Practice Questions (Page 7)

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 31–35

31foundation · easy
Which Azure OpenAI feature allows you to customize a base model with your own training data?
Select an answer first
32foundation · easy
Which prompt technique is commonly used to implement model reflection?
Select an answer first
33application · medium
An organization has deployed an Azure OpenAI model in production. The operations team needs to track token consumption, latency, and error rates over time to identify cost trends and performance degradation. Which monitoring approach should they implement?
Select an answer first
34expert · hard
A company wants to deploy a generative AI model to edge devices in remote locations with intermittent connectivity. The model must be able to run offline, but the team also wants to update the model remotely when connectivity is available. They also need to monitor the model's performance on the edge devices. What is the best approach?
Select an answer first
35application · medium
A team has deployed a generative AI model that sometimes produces biased or incorrect answers. They want to systematically analyze the model's outputs to identify patterns of failure and then improve the model. What is the most effective first step?
Select an answer first

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Optimize and Operationalize a Generative AI Solution AI-102 Practice Questions (Page 7)

Questions 31–35

Which Azure OpenAI feature allows you to customize a base model with your own training data?

Which prompt technique is commonly used to implement model reflection?

An organization has deployed an Azure OpenAI model in production. The operations team needs to track token consumption, latency, and error rates over time to identify cost trends and performance degradation. Which monitoring approach should they implement?

A team has deployed a generative AI model that sometimes produces biased or incorrect answers. They want to systematically analyze the model's outputs to identify patterns of failure and then improve the model. What is the most effective first step?

Keep studying — Implement generative AI solutions