Microsoft試験に合格し、関連する認定を取得するすべての顧客のニーズを満たすために、当社の専門家はすべての顧客向けに更新システムを設計しました。 AI-200試験問題は毎日更新されます。 当社のIT専門家は、AI-200試験準備が更新されているかどうかを確認する責任を負います。 AI-200テストの質問が更新されると、すぐにシステムがお客様にメッセージを送信します。 AI-200試験準備を使用する場合、更新システムをお楽しみいただき、AI-200試験にDeveloping AI Cloud Solutions on Azure合格することができます。
| Section | Objectives |
|---|---|
| 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
|
| Connect to and consume Azure services | - Integrate Azure services
|
| Secure, monitor, troubleshoot Azure solutions | - Operate AI cloud solutions
|
MicrosoftのDeveloping AI Cloud Solutions on Azureガイド急流で試験に合格できない場合は、全額返金されます。 クライアントのみが試験証明書とスキャンコピーまたはAI-200試験の不合格スコアのスクリーンショットを提供した場合、すぐにクライアントに返金します。 払い戻しの手順は非常に簡単です。 AI-200試験問題についてTopexamクライアントに問題や疑念がある場合は、メールを送信するか、オンラインでお問い合わせください。できるだけ早くクライアントの問題に返信して解決します。
質問 # 36
You need to deploy Azure Function resources and apps to meet the business and technical requirements.
What should you use?
正解：A
解説：
Use GitHub Actions because Proseware requires an automated, version-controlled deployment pipeline and explicitly prohibits local and command-line deployments. Microsoft documents GitHub Actions as a supported CI/CD mechanism for Azure Functions: a workflow stored under .github/workflows can build and deploy Function app code automatically in response to repository events such as pushes or pull requests. The Azure Functions deployment action can deploy the application package directly to the target Function app.
GitHub Actions also satisfies the infrastructure requirement. Microsoft provides first-class support for deploying Bicep templates through GitHub Actions , enabling Azure resources to be provisioned declaratively from the same source-controlled pipeline. A pull-request workflow can perform validation or Bicep what-if analysis before changes are merged, while a main-branch workflow can perform the actual deployment after approval. This provides repeatability, auditing, and separation from individual developer machines.
Azure Functions Core Tools and Azure CLI are commonly used for interactive/local deployments, which directly conflicts with the requirement to eliminate command-line deployment. Local Git deployment also fails the requirement for a centrally automated Bicep-based deployment process.
Study Guide references: Azure Functions CI/CD; GitHub Actions; infrastructure as code with Bicep; automated Azure deployments; version-controlled deployment workflows.
Category Breakdown
Category Number of Questions
Connect to and consume Azure services 41
Develop AI solutions by using Azure data management services 39
Secure, monitor, troubleshoot Azure solutions 34
Develop containerized solutions on Azure 28
TOTAL 142
Exam Topic Breakdown
Exam Topic Number of Questions
質問 # 37
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?
正解：A
解説：
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.
質問 # 38
You plan to deploy a web application to AKS.
The solution must:
* Scale out the application by adding more pods during peak CPU usage
* Expose the application internally within the cluster only
You need to configure a Kubernetes resource for each requirement
Which resources should you configure? To answer, move the appropriate resources to the correct requirements You may use each resource once, more than once, or not at all. You may need to move split bar between .. or scroll to view content.
正解：
解説：
Explanation:
Verified Answer: Scale out the application: HorizontalPodAutoscaler. Expose internally only: ClusterIP service.
Detailed Explanation: The Kubernetes HorizontalPodAutoscaler increases or decreases pod replicas in response to CPU, memory, or other metrics, so it is the resource that implements CPU-driven pod scale-out. A ClusterIP service creates an internal service IP that is reachable within the Kubernetes cluster and is the normal choice for an internal-only service. A Deployment defines the workload but does not itself implement metric-driven autoscaling, while Ingress is intended to route inbound network traffic.
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 | AKS scaling overview | Kubernetes services in AKS
質問 # 39
You deploy a GPT-4o model in Azure AI Foundry. Users report inconsistent completions for the same prompt. You need to make outputs more deterministic without changing the prompt. What should you do?
正解：D
解説：
Temperature controls randomness in token selection. Setting it near 0 makes the model consistently choose the highest-probability next token, producing more deterministic and repeatable output for identical prompts.
質問 # 40
You must ensure that PII (personally identifiable information) such as names and phone numbers is automatically redacted from support ticket transcripts before they are stored. What should you use?
正解：C
解説：
Azure AI Language's PII detection feature identifies and can redact categories of sensitive information (names, phone numbers, SSNs, etc.) from unstructured text, which is the purpose- built service for this scenario.
質問 # 41
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