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| Section | Objectives |
|---|---|
| Plan and manage Azure AI solutions | - Plan security and compliance requirements - Monitor and optimize AI solutions - Select appropriate Azure AI services |
| Implement Azure AI solutions | - Implement computer vision solutions - Implement generative AI solutions using Azure OpenAI - Implement natural language processing solutions - Implement knowledge mining with Azure AI Search |
| Implement and monitor AI workloads | - Monitor performance and troubleshoot issues - Deploy AI models and services |
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NEW QUESTION # 131
You are reviewing secret access patterns used by an AI application that retrieves credentials from Key Vault.
You need to evaluate the security impact of each implementation approach.
What is the outcome of each approach? To answer, move the appropriate outcomes to the correct implementations. You may use each outcome once, more than once, or not at all. You may need to move the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 132
Your team wants to track token usage, latency, and error rates for an Azure OpenAI-backed application in production, with alerting on anomalies. What should you configure?
Answer: C
Explanation:
Azure Monitor, configured via diagnostic settings on the Azure OpenAI resource, collects logs and metrics such as token consumption, latency, and errors, and supports alert rules for anomaly detection in production.
NEW QUESTION # 133
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear on the review screen.
You plan to deploy a container to an Azure App Service API app named api1. You host the source code for api1 in a GitHub repository. The container uses the API key at runtime to connect to a backend service.
The container must be able to retrieve the API key at runtime without exposing it in the source repository or Git commit history.
You need to ensure that the API key remains outside of Git commit history and is available to the container at runtime.
Solution: Store the API key as a GitHub repository secret.
Does the solution meet the goal?
Answer: B
Explanation:
Correct:
* Store the API key in Azure Key Vault and reference it from an App Service application setting.
Storing the API key in Azure Key Vault and referencing it via App Service application settings is the recommended, secure approach. This strategy completely removes sensitive credentials from your GitHub repository and Git commit history while injecting them safely into your container environment at runtime.
Incorrect:
* Embed the API key as a hardcoded environment variable in the Dockerfile.
* Store the API key as a GitHub repository secret.
Reference:
https://www.qservicesit.com/full-stack-applications-on-azure
NEW QUESTION # 134
You configure ACR Tasks to automate image builds.
Container images must rebuild when:
Application updates occur.
Base image updates occur, such as when the underlying OS image is updated.
Regular scheduled rebuilds are required.
You need to configure ACR Tasks to support automated image rebuilds.
Which three triggers should you configure? Each correct answer presents part of the solution. Choose three.
NOTE: Each correct selection is worth one point.
Answer: A,C,D
NEW QUESTION # 135
You need to detect whether an uploaded image contains adult or violent content before allowing it to be posted to a public forum. What should you use?
Answer: B
Explanation:
Azure AI Content Safety's image moderation capability is purpose-built to detect categories such as sexual, violent, self-harm, and hate content in images, returning severity scores you can act on.
NEW QUESTION # 136
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