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| Section | Objectives |
|---|---|
| Connect to and consume Azure services | - Integrate Azure services
|
| Develop AI solutions by using Azure data management services | - Work with Azure data platforms for AI workloads
|
| Develop containerized solutions on Azure | - Implement containerized applications
|
| Secure, monitor, troubleshoot Azure solutions | - Operate AI cloud solutions
|
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NEW QUESTION # 29
Drag and Drop Question
A recommendation system stores 3 million embeddings in Azure Database for PostgreSQL.
During peak hours, P95 similarity-query latency increases, and the cache hit ratio drops significantly.
You suspect that the vector index working set no longer fits in memory, resulting in more disk reads.
You need to scale server resources and validate the outcome.
Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation:
Step 1: Before peak hours, run a fixed workload to capture baseline P95 latency and capture EXPLAIN ANALYZE for the vector query Establishing a baseline is crucial for performance tuning. Running EXPLAIN ANALYZE provides deep insight into whether the query is actually using the vector index (like HNSW or IVFFlat) or falling back to a slow sequential scan.
Step 2: Scale to a tier with more memory per vCore
Since the problem states that the vector index working set no longer fits in memory, scaling to a tier with higher memory (such as Memory-Optimized tiers) directly solves the root cause by allowing the RAM to fully cache the vector index, eliminating slow disk reads.
Step 3: Re-run the same benchmark and compare P95 and P99 latency
To validate the outcome of your scaling operations, you must run the identical benchmark workload and compare high-percentile latencies (P95, P99) against your baseline to prove the bottleneck was resolved.
Step 4: Review memory pressure and cache hit ratio during peak hours and compare them to the baseline This step confirms the success of the resource scaling. A successful remediation will show a significantly increased cache hit ratio and reduced memory pressure/disk I/O compared to the baseline metrics.
Reference:
https://www.pgedge.com/blog/ai-features-in-pgadmin-ai-insights-for-explain-plans
NEW QUESTION # 30
An AI application uses a database. The database credential rotates every 30 days.
The application currently requires a manual update each time the credential rotates.
You need to ensure that the application always uses the latest secret version without manual updates.
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
NEW QUESTION # 31
You are designing a message based order processing system by using Azure Service Bus.
Each order message must be processed in the same sequence it was received. In addition, a separate audit service must receive copies of all order messages far compliance tracking You need to configure a Service Bus namespace and its entities to meet the ordering and audit requirements Which service bus options should you use? To answer, select the appropriate options m the answer area NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 32
You plan to deploy a web app to App Service on Linux. You create an App Service plan. You create and push a custom Docker image that contains the web app to Azure Container Registry.
You need to access the console logs generated from inside the container in real-time.
How should you complete the Azure CLI command? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
nable container logging with `az webapp log config --docker-container-logging filesystem`, then stream it with `az webapp log tail`.
Detailed Explanation: The Azure CLI exposes separate operations for enabling App Service container logging and viewing the live stream. For a custom Linux container, docker/container logging must first be written to the App Service filesystem. The `az webapp log tail` command then starts real-time log tracing.
Commands such as `show` or `download` inspect configuration or retrieve historical files; they do not provide the requested live stream from the running container.
Study Guide Alignment: Containerized Azure workloads: registry builds, App Service containers, Container Apps revision/scaling behavior, and AKS deployment choices.
Official Microsoft Learn References: AI-200 Study Guide | Azure CLI: az webapp log
NEW QUESTION # 33
Drag and Drop Question
You are developing a serverless Java application on Azure. You create a new Azure Key Vault to work with secrets from a new Azure Functions application.
The application must meet the following requirements:
- Reference the Azure Key Vault without requiring any changes to the
Java code.
- Dynamically add and remove instances of the Azure Functions host
based on the number of incoming application events.
- Ensure that instances are perpetually warm to avoid any cold starts.
- Connect to a VNet.
- Authentication to the Azure Key Vault instance must be removed if the Azure Functions application is deleted.
You need to grant the Azure Functions application access to the Azure Key Vault.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation:
Step 1: Create the Azure Functions app with a Premium plan type
Perpetually Warm & VNet: The Premium plan type is the only serverless-scale hosting option that satisfies requirement 3 (keeping instances warm to eliminate cold starts) and requirement 4 (native regional VNet integration). The Consumption plan type does not keep instances warm and does not support VNet injection.
Event-Driven Scaling: It fulfills requirement 2 by dynamically adding and removing host instances using the event-driven scale controller.
Step 2: Create a system-assigned managed identity for the application
Lifecycle Coupling: It directly satisfies requirement 5. A system-assigned managed identity is tied strictly to the lifecycle of the Azure Functions resource. If the Functions application is deleted, Azure automatically removes the identity and its authentication token, ensuring that access to the Key Vault is immediately revoked. A user-assigned identity exists as an independent resource and would persist after the application's deletion.
Step 3: Create an access policy in Azure Key Vault for the application identity Granting Permissions: This step completes the access requirements. By adding an access policy (or an Azure RBAC role assignment) in Azure Key Vault specifically for the created system- assigned managed identity, you grant the runtime environment the necessary permissions to retrieve secrets.
No Code Changes: Combining the managed identity with an App Setting configured as an Azure Key Vault Reference allows the Functions platform to fetch secrets automatically at runtime, keeping your Java source code unchanged Reference:
https://learn.microsoft.com/en-us/azure/azure-functions/functions-best-practices
NEW QUESTION # 34
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