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| Section | Objectives |
|---|---|
| Topic 1: Develop containerized solutions on Azure | - Implement containerized applications
|
| Topic 2: Secure, monitor, troubleshoot Azure solutions | - Operate AI cloud solutions
|
| Topic 3: Connect to and consume Azure services | - Integrate Azure services
|
| Topic 4: Develop AI solutions by using Azure data management services | - Work with Azure data platforms for AI workloads
|
あなたがより少ない時間と労力を置いてMicrosoftのAI-200試験を準備するために我々CertJukenは多くの時間と労力を投資してあなたにソフトウェアを作成します。我々の全額で返金する承諾は話して行動しないわけではない、我々はいくつ自社製品に自信を持っても、あなたに満足させる効果がないなら、我々は速やかに全額で返金します。しかし、我々はMicrosoftのAI-200試験のソフトウェアは、あなたの期待に応えると信じて、私はあなたの成功を祈っています！
質問 # 102
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.
NOTE: Each correct selection is worth one point.
正解：B、D、E
解説：
To support automated container image rebuilds based on your requirements, you need to configure source triggers, base image triggers, and timer triggers in Azure Container Registry (ACR) Tasks.
Timer Triggers: Automates regular scheduled rebuilds. This uses a cron schedule expression to run image builds at specific intervals (e.g., weekly or monthly) regardless of code changes.
Source Triggers: Automates rebuilds when application updates occur. This monitors changes in your source code repository (like GitHub or Azure Repos) and fires a build when code is committed.
Base Image Triggers: Automates rebuilds when the underlying OS or framework image updates.
ACR tracks dependencies and automatically kicks off a new build when your defined FROM image changes in the public registry or your private registry.
Reference:
https://oneuptime.com/blog/post/2026-02-16-how-to-set-up-acr-tasks-for-automated-container-image-builds-on-git-commit/view
質問 # 103
An HTTP-triggered Azure Function must read a JSON configuration file stored in Azure Blob Storage.
You need to retrieve the configuration file contents.
What should you use?
正解：A
解説：
To read a configuration file stored in Azure Blob Storage from an HTTP-triggered function, you must use an Azure Blob storage input binding.
Input bindings allow you to connect external data to your function natively without writing code to explicitly connect, authenticate, and download data using SDK clients.
Strategic Overview
The Trigger: You will use an HTTP Trigger to execute the function via a web request.
The Input: You will use a Blob Input Binding pointing to the specific JSON path in your storage account.
The Result: The Azure Functions runtime automatically retrieves the file contents and provides them directly to your function code as a string, byte array, or parsed object parameter.
Reference:
https://learn.microsoft.com/en-us/azure/azure-functions/functions-triggers-bindings
質問 # 104
Case Study 2 - Proseware Inc.
Background
Proseware Inc. develops AI-powered knowledge management solutions for enterprise customers.
The company is modernizing its platform to support semantic search, intelligent document retrieval, and real-time partner integrations.
The engineering team uses Python and Azure SDKs. The architecture is being redesigned to support containerized microservices, vector search workloads, and serverless backend processing.
Planned Application Architecture
Microservices are containerized by using Docker.
Code for containerized microservices and Azure Function apps is developed locally but stored in a GitHub repository.
Custom images for containerized microservices are stored in Azure Container Registry (ACR).
Base images are stored in Docker Hub. Custom images must be rebuilt automatically whenever their base images are updated.
Azure Cosmos DB for NoSQL stores documents, metadata, and vector embeddings.
Azure Functions generate vector embeddings of Azure Cosmos DB for NoSQL-hosted documents and send messages to Service Bus to trigger search index updates.
Azure Container Apps (ACA) apps host backend API services that provide semantic search across Azure Cosmos DB for NoSQL documents. API services process Service Bus messages and update search indexes.
Azure Kubernetes Service (AKS) processes batch vector embedding regeneration for existing Azure Cosmos DB for NoSQL documents (whenever the embedding model is changed).
An extranet-facing containerized webhook allows business partners to submit documents to be processed by internal AI workflows for semantic search and retrieval.
Monitoring
Telemetry generated by Azure resources is sent to Azure Monitor.
A Log Analytics workspace is used to collect ACA apps logs, AKS container logs, and Azure Functions apps logs.
Monitoring of Azure Functions is currently implemented by using Azure Application Insights SDK instrumentation.
Business Requirements
Embeddings for new or updated Azure Cosmos DB for NoSQL-hosted documents must be automatically generated.
Backend API services must scale automatically during business hours.
Cold start delay of backend APIs must be minimized.
Secrets must be stored outside of container images.
Developers must be able to correlate telemetry across Azure Functions hosts and apps.
All tracing must be implemented by using OpenTelemetry SDK instrumentation.
Development efforts must be minimized.
Technical Requirements
Container images must be built automatically and validated before code updates are merged into the main branch.
Image build automation must run inside the Azure Container Registry, eliminating dependency on local developer machines and external build services.
Dependency of image builds on local developer machines must be eliminated.
Event-driven scaling in ACA must occur based on the number of pending messages in the Azure Service Bus queue.
Azure Cosmos DB for NoSQL RU consumption must be minimized.
Vector similarity search must use embeddings stored in Azure Cosmos DB for NoSQL.
The partner-facing containerized webhook service must run on Azure App Service.
Secrets must NOT be stored in container images, source control, or application configuration directly. They must be accessed securely at runtime.
All secrets must be stored centrally in Azure Key Vault and accessed at runtime through a managed identity.
Azure App Service must supply secrets at runtime without relying on external services.
Resources and workloads must be deployed by using Bicep templates through an automated, version-controlled pipeline. Local and command-line deployments must be eliminated to ensure repeatable, auditable deployments.
Known Issues
RU consumption spikes during vector similarity queries.
You need to deploy Azure Function resources and apps to meet the business and technical requirements. What should you use?
正解：D
解説：
GitHub Actions is the correct tool to use.
Automated Pipeline: It natively automates infrastructure deployment (Bicep) and code compilation through version-controlled workflows.
Eliminates Local Deployments: Workloads are triggered by repository events (like a code merge), removing the need for manual command-line interventions.
Auditability & Repeatability: It keeps a centralized log of every deployment, ensuring a clear audit trail for governance.
Scenario:
Technical Requirements
Resources and workloads must be deployed by using Bicep templates through an automated, version-controlled pipeline. Local and command-line deployments must be eliminated to ensure repeatable, auditable deployments.
Business Requirements
Development efforts must be minimized.
Reference:
https://github.com/marketplace/actions/azure-functions-action
質問 # 105
You need to implement trace correlation according to the business requirements.
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.
NOTE: More than one order of answer choices is correct. You will receive credit for any of the correct orders you se lect.
正解：
解説：
Explanation:
Verified answer: 1) Instrument the application code with the OpenTelemetry SDK; 2) configure an Azure Monitor trace exporter in the OpenTelemetry SDK; 3) redeploy the instrumented services.
Detailed Explanation: Trace correlation requires the application to create OpenTelemetry spans and an exporter to send those spans to Azure Monitor. Instrumentation must therefore be present before the updated service is deployed. The previous Application Insights SDK instrumentation does not satisfy the case-study requirement that all tracing use OpenTelemetry. Configuring an OpenTelemetry view is unrelated to basic distributed-trace export, and calling TrackEvent is an Application Insights SDK pattern rather than the required OpenTelemetry approach.
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 | Enable Azure Monitor OpenTelemetry
質問 # 106
Hotspot Question
You are reviewing the message-processing code in a backend worker service.
You review the following Python code that initializes and starts a Service Bus processor.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation:
Box 1: No
No, messages are not removed from the queue as soon as they are received when using PEEK_LOCK mode.
How PEEK_LOCK Works
The Lock: The Service Bus processor locks the message exclusively for your worker for a specific duration.
The Invisible State: Other workers cannot see or process the message while the lock is active.
The Removal: The message is only permanently removed from the queue after it is successfully processed and explicitly completed.
Box 2: Yes
Yes, the message can be retried by another consumer if it fails due to a TransientServiceError.
Explicit abandonment: The code calls receiver.abandon_message(msg) when catching a TransientServiceError.
Lock release: Abandoning a message immediately releases the PEEK_LOCK held by the current worker.
Immediate availability: The message returns to the active queue and becomes instantly visible to other competing consumers.
Box 3: No
No, messages with invalid JSON payloads will not be retried until the maximum delivery count is reached. They will be sent to the dead-letter queue (DLQ) immediately on the very first attempt.
Immediate Dead-Lettering: The code catches the json.JSONDecodeError and explicitly calls receiver.dead_letter_message(msg).
Instant Termination: This API call tells Azure Service Bus to move the message to the DLQ right away, bypassing the normal delivery count logic.
No Re-delivery: Because the message is moved to the DLQ, the broker removes it from the main queue, stopping any further processing attempts.
Reference:
https://learn.microsoft.com/en-us/python/api/azure-servicebus/azure.servicebus.aio.servicebusreceiver
https://learn.microsoft.com/en-us/azure/service-bus-messaging/service-bus-python-how-to-use-queues
質問 # 107
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