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| Section | Objectives |
|---|---|
| Implement and monitor AI workloads | - Deploy AI models and services - Monitor performance and troubleshoot issues |
| Implement Azure AI solutions | - Implement knowledge mining with Azure AI Search - Implement natural language processing solutions - Implement computer vision solutions - Implement generative AI solutions using Azure OpenAI |
| Plan and manage Azure AI solutions | - Select appropriate Azure AI services - Plan security and compliance requirements - Monitor and optimize AI solutions |
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NEW QUESTION # 73
Drag and Drop Question
You are reviewing secret access patterns used by an AI application that retrieves credentials from Key Vault.
You need to evaluate the security impact of each implementation approach.
What is the outcome of each approach? To answer, move the appropriate outcomes to the correct implementations. You may use each outcome once, more than once, or not at all. You may need to move the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Supports automatic rotation.
When an AI application retrieves a secret from Azure Key Vault without specifying a version, it always fetches the latest active version. This choice impacts your security and operations in the following ways:
Latest Version Fetch: The vault automatically serves the current active secret value.
No Code Changes: Your application reads the new credential immediately after rotation without a code redeployment.
Zero Downtime: Automated rotation pipelines can update the secret in Key Vault while the app seamlessly switches to the new token.
Box 2: Requires manual update after rotation
Using a specific version identifier when retrieving a secret from Key Vault means the application always requests one exact version of that secret. This choice requires manual update after rotation because the application code or configuration pins a specific version ID and will not fetch the new value when the secret rotates.
Box 3: Prevents credential storage
Prevents credential storage is the correct outcome of using managed identity for authentication.
No credentials to manage: Managed identities eliminate the need for developers to manage credentials entirely.
Automatic token handling: Azure automatically creates an identity for the application in Microsoft Entra ID and handles the service principal's lifecycle behind the scenes, ensuring that no secrets or connection strings are ever hardcoded or stored in configuration files.
Box 4: Introduces credential exposure
Introduces credential exposure and Requires manual update after rotation are the primary outcomes of storing a client secret in environment variables.
Storing clear-text secrets in environment variables poses a significant security risk. Any process, compromised dependency, or user with access to the execution environment (such as container details, application logs, or process dumps) can easily read them.
Reference:
https://medium.com/simform-engineering/building-automated-secret-rotation-using-azure-key-vault-and-net-01e0701f0a2e
https://www.varonis.com/blog/azure-managed-identities
NEW QUESTION # 74
You store embeddings in a property named embedding in an Azure Cosmos DB for NoSQL container.
You must retrieve the five most similar documents to an input embedding.
You need to construct the similarity query.
What should you do?
Answer: A
Explanation:
To retrieve the five most similar documents from Azure Cosmos DB for NoSQL using vector search, you must limit the number of results.
When performing vector similarity searches using the VectorDistance() function, you must restrict the output size directly in the query.
Reference:
https://stemhash.com/semantic-search-with-llms/
NEW QUESTION # 75
You are developing a microservices-based application that uses Azure Container Apps The application consists of several containerized services that handle tasks, such as processing orders, managing inventory, and generating reports.
You must secure the container apps. All apps must reside in the same virtual network, share the same Dapr configuration, and share the same logging location.
Apps must support the configuration of the amount of memory and compute resources available to containers.
You need to configure the Azure Container App
How should you complete the CLI command? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Verified Answer: `az containerapp env create ... --enable-workload-profiles`.
Detailed Explanation: The shared boundary for Container Apps is the managed environment. Apps in one environment can share the environment's virtual network integration, observability destination, and Dapr- related environment configuration. Enabling workload profiles provides selectable compute profiles and resource sizing options for workloads in that environment. The CLI must therefore create a Container Apps environment and enable workload profiles. Creating an individual container app alone would not establish the common environment-level network, logging, and resource-profile boundary required by the scenario.
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 Container Apps environments
NEW QUESTION # 76
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 plan to deploy a container to an Azure App Service API app named api1. You host the source code for api1 in a GitHub repository. The container uses the API key at runtime to connect to a backend service.
The container must be able to retrieve the API key at runtime without exposing it in the source repository or Git commit history.
You need to ensure that the API key remains outside of Git commit history and is available to the container at runtime.
Solution: Store the API key as a GitHub repository secret.
Does the solution meet the goal?
Answer: B
Explanation:
Correct:
* Store the API key in Azure Key Vault and reference it from an App Service application setting.
Storing the API key in Azure Key Vault and referencing it via App Service application settings is the recommended, secure approach. This strategy completely removes sensitive credentials from your GitHub repository and Git commit history while injecting them safely into your container environment at runtime.
Incorrect:
* Embed the API key as a hardcoded environment variable in the Dockerfile.
* Store the API key as a GitHub repository secret.
Reference:
https://www.qservicesit.com/full-stack-applications-on-azure
NEW QUESTION # 77
You provisioned an Azure Cosmos DB for NoSQL account named account1 with the default consistency level.
You plan to configure the consistency level on a per request basis. You plan to request Consistent Prefix consistency on a per-request basis.
You need to identify the resulting consistency level for read and write operations.
Which levels result from this configuration? To answer, select the appropriate options in the answer a rea.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Verified Answer: Read operations: Consistent Prefix. Write operations: Session (the account default remains effective for writes).
Detailed Explanation: Azure Cosmos DB allows a client or request to override consistency for reads.
Microsoft explicitly notes that such an override applies only to reads; it does not change how writes are committed and replicated under the account's configured consistency. Because a new account uses Session consistency by default, requesting Consistent Prefix affects the reads while the account continues to use its Session consistency behavior for writes. This distinction is the key point tested by the hotspot.
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 | Manage Cosmos DB consistency levels | Cosmos DB consistency level choices
NEW QUESTION # 78
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