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| Section | Objectives |
|---|---|
| Topic 1: Implement Azure AI solutions | - Implement generative AI solutions using Azure OpenAI - Implement knowledge mining with Azure AI Search - Implement computer vision solutions - Implement natural language processing solutions |
| Topic 2: Plan and manage Azure AI solutions | - Plan security and compliance requirements - Select appropriate Azure AI services - Monitor and optimize AI solutions |
| Topic 3: Implement and monitor AI workloads | - Monitor performance and troubleshoot issues - Deploy AI models and services |
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NEW QUESTION # 42
You need to configure image build automation based on the technical requirements.
Which settings should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
* Trigger for container images build: Base image update
* Implementation of image builds: ACR Task
The correct trigger is Base image update because Proseware requires every custom container image to be rebuilt automatically whenever its Docker Hub base image changes. Azure Container Registry Tasks can detect base-image dependencies from the Dockerfile FROM instruction and automatically trigger a rebuild when that base image is updated. Microsoft confirms that ACR Tasks can track base images in public repositories such as Docker Hub and Microsoft Container Registry, as well as images stored in Azure Container Registry.
The build implementation must be an ACR Task . This directly satisfies the technical requirement that image- build automation run inside Azure Container Registry , eliminating dependencies on local developer machines or separate external build services. ACR Tasks provides cloud-based container building and can automate builds based on source-code commits, base-image updates, or scheduled triggers.
A GitHub workflow could orchestrate CI/CD, but using it as the actual image-building implementation would conflict with the explicit requirement that builds execute inside ACR. Docker Compose defines multi- container applications; it is not the native ACR build automation mechanism. A Commit trigger addresses source changes rather than the stated base-image update requirement, while Scheduled builds would introduce unnecessary periodic execution.
Study Guide references: Azure Container Registry Tasks; base-image dependency tracking; automated rebuilds; Docker Hub base-image triggers; cloud-native container builds.
NEW QUESTION # 43
You are building a RAG (retrieval-augmented generation) solution using Azure AI Foundry. The knowledge base consists of 50,000 PDF documents stored in Azure Blob Storage. You need to make the document content searchable by the language model with minimal custom code. What should you use?
Answer: C
Explanation:
Azure AI Search's integrated vectorization feature lets you point a skillset directly at a blob data source, and it handles chunking, embedding generation (via an Azure OpenAI embedding model), and indexing automatically -- minimizing custom code compared to manually building a chunking/embedding pipeline.
NEW QUESTION # 44
You are building a RAG (retrieval-augmented generation) solution using Azure AI Foundry. The knowledge base consists of 50,000 PDF documents stored in Azure Blob Storage.
You need to make the document content searchable by the language model with minimal custom code.
What should you use?
Answer: C
Explanation:
Use Azure AI Search integrated vectorization with a skillset . Integrated vectorization automates the main ingestion stages required for RAG: an Azure AI Search indexer can read documents directly from a supported data source such as Azure Blob Storage, extract and split document content into chunks, invoke an embedding model, and store both searchable text and vector representations in the search index. Microsoft specifically states that integrated vectorization reduces development and maintenance effort because developers do not need to implement the chunking and embedding pipeline manually.
A skillset can include a Text Split skill for chunking and an Azure OpenAI Embedding skill or another supported embedding skill for vector generation. At query time, a matching vectorizer can convert user questions into vectors so Azure AI Search can perform similarity or hybrid retrieval for grounding the language model.
Custom text classification solves a different NLP problem. Building Azure Functions to process all 50,000 PDFs manually would introduce unnecessary custom orchestration and maintenance. Azure Table Storage does not provide the native vector-search and RAG indexing capabilities required here.
Study Guide references: Azure AI Search # integrated vectorization; indexers; skillsets; Text Split skill; embedding skills; vector indexes; RAG retrieval.
NEW QUESTION # 45
You are developing several microservices to run on Azure Container Apps.
The microservices must allow HTTPS access by using a custom domain.
You need to configure the custom domain in Azure Container Apps.
In which order should you 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) Enable ingress; 2) add the required DNS records; 3) validate ownership of the custom domain; 4) add the custom domain to the Container App; 5) bind the certificate.
Detailed Explanation: A custom HTTPS hostname requires an ingress endpoint, DNS proof that the requester controls the hostname, the hostname association itself, and a TLS certificate bound to that hostname.
DNS records must exist before ownership validation can succeed. Once ownership is validated, the domain can be associated with the Container App and the certificate can be bound to provide HTTPS. Skipping ingress or DNS validation would prevent the hostname from being correctly routed and secured.
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 | Custom domains and certificates in Container Apps
NEW QUESTION # 46
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 are preparing a production deployment for an Azure Function app. The app will run across multiple environments.
The solution must support environment-specific configuration and prevent secrets from being stored in source control.
You need to develop the solution.
Solution: Store production secrets in environment variables set by the Dockerfile.
Does the solution meet the goal?
Answer: B
Explanation:
Correct:
* Use App Configuration with Key Vault references to store environment-specific settings and secrets, accessed from the function app by using a managed identity.
This is an industry-standard best practice architectural pattern.
Using Azure App Configuration combined with Azure Key Vault references completely satisfies your compliance requirements. It centralizes feature flags and non-sensitive configurations, keeps sensitive data safely out of source control, handles multi-environment deployments cleanly, and eliminates credentials via a passwordless Managed Identity.
Incorrect:
* Store connection strings in the Function app application settings configured in the Azure Portal.
* Store production secrets in environment variables set by the Dockerfile.
Reference:
https://learn.microsoft.com/en-us/azure/app-service/app-service-key-vault-references
NEW QUESTION # 47
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