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| Section | Objectives |
|---|---|
| Secure, monitor, troubleshoot Azure solutions | - Operate AI cloud solutions
|
| Develop AI solutions by using Azure data management services | - Work with Azure data platforms for AI workloads
|
| Connect to and consume Azure services | - Integrate Azure services
|
| Develop containerized solutions on Azure | - Implement containerized applications
|
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NEW QUESTION # 81
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 an App Service application setting configured through the Azure Portal.
Does the solution meet the goal?
Answer: A
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.
* Store the API key as an App Service application setting configured through the Azure Portal.
Reference:
https://www.qservicesit.com/full-stack-applications-on-azure
NEW QUESTION # 82
You are using Azure Monitor Application Insights to collect dependency data.
You must be able to:
- Correlate failed requests with dependency calls during the last hour.
- Calculate the average dependency duration per operation.
You need to construct the KOL query by using the minimum number of statements.
Which three operators should you use? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: C,D,E
Explanation:
To satisfy both requirements with the minimum number of statements using the Kusto Query Language (KQL), you should use the following KQL operators:
where: Filters data by time range (ago(1h)) and request status (success == false).
join: Correlates the requests and dependencies tables using a unique identifier (usually operation_Id).
summarize: Computes the average (avg()) dependency duration grouped by the specific operation (operation_Name).
Reference:
https://learn.microsoft.com/en-us/azure/azure-monitor/app/application-insights-faq
NEW QUESTION # 83
You are developing an AI search API that caches semantic search results in Redis.
Search results must remain cached for 10 minutes. If the underlying data changes, cached entries must NOT be returned.
You need to implement a cache aside strategy to ensure data consistency.
Which two actions should you perform? Each correct answer presents part of the solution. Choose two NOTE: Each correct selection is worth one point.
Answer: B,D
Explanation:
Detailed Explanation: The cache-aside design needs both bounded lifetime and explicit invalidation. A ten- minute TTL ensures cached search results do not persist indefinitely, while deleting related keys when source data changes prevents known-stale entries from being returned during that ten-minute window. Sliding expiration would extend lifetime based on access and can preserve stale data. Keyspace notifications report Redis key events; they do not replace application logic that invalidates entries when the authoritative source changes.
Study Guide Alignment: AI data-management workloads: Cosmos DB, PostgreSQL, caching, vector storage, vector retrieval, consistency, and connection optimization.
Official Microsoft Learn References: AI-200 Study Guide | Azure Managed Redis documentation
NEW QUESTION # 84
You provisioned an Azure Cosmos DB for NoSQL account named account1 with the default consistency level.
You plan to configure the consistency level on a per request basis. You plan to request Consistent Prefix consistency on a per-request basis.
You need to identify the resulting consistency level for read and write operations.
Which levels result from this configuration? To answer, select the appropriate options in the answer a rea.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Verified answer: Read operations: Consistent Prefix. Write operations: Session (the account default remains effective for writes).
Detailed Explanation: Azure Cosmos DB allows a client or request to override consistency for reads.
Microsoft explicitly notes that such an override applies only to reads; it does not change how writes are committed and replicated under the account's configured consistency. Because a new account uses Session consistency by default, requesting Consistent Prefix affects the reads while the account continues to use its Session consistency behavior for writes. This distinction is the key point tested by the hotspot.
Study Guide Alignment: AI data-management workloads: Cosmos DB, PostgreSQL, caching, vector storage, vector retrieval, consistency, and connection optimization.
Official Microsoft Learn References: AI-200 Study Guide | Manage Cosmos DB consistency levels | Cosmos DB consistency level choices
NEW QUESTION # 85
You are designing a solution that will use two Azure Functions apps: App1 and App2. App1 is Windows based and will be deployed as code. App2 is Linux based and will be deployed as a container image.
Estimates show that the duration of the request processing for both apps will range from 1 to 10 minutes.
You plan to implement App1 and App2 by using the hosting plan to satisfy the following requirements:
* Request processing can complete within the estimated time range.
* The autoscaling behavior is event driven.
* The upper scaling limit is maximized.
You need to create the hosting plan for the implementation.
Which hosting plan should you create? To answer, move the appropriate hosting plans to the correct apps.
You may use each hosting plan once, more than once, or not at all. You may..
Answer:
Explanation:
Explanation:
pp1: Consumption. App2: Premium.
Detailed Explanation: For App1, the Windows code-based Consumption plan satisfies event-driven autoscaling, allows execution up to the stated ten-minute range, and has the highest scaling ceiling among the listed event-driven choices for this workload. App2 is deployed as a Linux container image; among Premium, Dedicated, and Consumption, Premium is the event-driven plan that supports Linux containerized Functions and permits long-running execution. Dedicated hosting is not the requested event-driven autoscale model. The original Premium/Premium answer therefore did not maximize App1's upper scaling limit.
Study Guide Alignment: Azure service integration: Service Bus, Event Grid, Azure Functions triggers
/bindings, and event-driven processing.
Official Microsoft Learn References: AI-200 Study Guide | Azure Functions scale and hosting
NEW QUESTION # 86
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