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| Section | Objectives |
|---|---|
| Topic 1: Connect to and consume Azure services | - Integrate Azure services
|
| Topic 2: Develop containerized solutions on Azure | - Implement containerized applications
|
| Topic 3: Develop AI solutions by using Azure data management services | - Work with Azure data platforms for AI workloads
|
| Topic 4: Secure, monitor, troubleshoot Azure solutions | - Operate AI cloud solutions
|
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NEW QUESTION # 45
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 # 46
You need to address the known issue resulting from vector similarity queries.
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,C
Explanation:
Detailed Explanation: The objective is to reduce RU consumption from vector similarity queries. Microsoft guidance identifies vector numeric precision and vector-index selection as major cost and performance levers.
Using a lower supported vector precision can reduce storage and processing cost, while quantizedFlat and DiskANN are designed to reduce latency and RU consumption compared with flat search for appropriate data sizes. Strong consistency would increase resource cost rather than solve vector-search efficiency. A regular composite index is not a substitute for the vector index. Option B should be read as reducing vector numeric precision, not changing a generic "indexing precision" setting.
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 | Vector search in Azure Cosmos DB | Optimize Cosmos DB vector search performance
NEW QUESTION # 47
You are investigating high latency in an AI search application that processes millions of requests daily.
Telemetry is stored in Azure Monitor Logs.
You must create a KQL query that correlates information from the AppRequests table and the AppDependencies table. The query must meet the following requirements:
* Include only data from the last 24 hours.
* Filter for failed requests only.
* Calculate the average duration of dependencies, grouped by operation.
The query must follow best practice to optimize the performance by minimizing the initial data scan.
You need to create the query.
In which order should perform the actions? To answer, move all actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation:
Verified answer: 1) Select the requests table; 2) apply the 24-hour time filter; 3) filter failed requests; 4) join the dependencies table; 5) summarize average dependency duration by operation.
Detailed Explanation: KQL performs best when high-selectivity filters are applied as early as possible, especially the time predicate that limits the amount of data scanned. Starting from AppRequests, restricting to the last 24 hours, and then filtering failures reduces the left-side dataset before the join. The dependency table is then correlated with those requests, and aggregation is performed last to calculate average dependency duration by operation. Joining or summarizing before the time and failure filters would process more data than necessary.
Study Guide Alignment: Security and operations: Key Vault, App Configuration, managed identity, OpenTelemetry, Azure Monitor, and KQL-based troubleshooting.
Official Microsoft Learn References: AI-200 Study Guide | Optimize log queries in Azure Monitor
NEW QUESTION # 48
You are developing an AI application that retrieves database credentials from Key Vault by using the Azure SDK for Python.
The application must use managed identity for authentication.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Verified answer: Yes; Yes; No.
Detailed Explanation: The get_secret call supplies an explicit version value, so it retrieves that particular version of dbPassword. DefaultAzureCredential can authenticate to Key Vault by using the Azure-hosted application's managed identity without embedding a client secret in the application. Because the code requests a fixed version, later secret rotation creates a newer version but subsequent executions of this exact code continue to request the specified old version. To follow the latest version automatically, the version parameter must be omitted.
Study Guide Alignment: Security and operations: Key Vault, App Configuration, managed identity, OpenTelemetry, Azure Monitor, and KQL-based troubleshooting.
Official Microsoft Learn References: AI-200 Study Guide | Managed identities for Azure resources | Use Key Vault references for App Service and Functions
NEW QUESTION # 49
You have an Event Grid subscription that triggers an Azure Function.
You need to prevent loss of events in case the endpoint returns an HTTP 400 response.
Which action should you perform?
Answer: B
Explanation:
Configuring a dead-letter destination is exactly the right step to take.
By default, when an endpoint returns an HTTP 400 (Bad Request) or HTTP 413 (Payload Too Large) response, Azure Event Grid immediately stops delivery attempts and drops the message.
It treats these specific errors as non-transient, client-side issues, meaning it skips its standard 24- hour retry policy.
Configuring a dead-letter destination ensures that these dropped events are safely preserved for future troubleshooting and reprocessing.
Reference:
https://turbo360.com/blog/azure-event-grid-dead-letter-monitoring
NEW QUESTION # 50
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