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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: Develop AI solutions by using Azure data management services | - Work with Azure data platforms for AI workloads
|
| Topic 4: Connect to and consume Azure services | - Integrate Azure services
|
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NEW QUESTION # 77
You are designing a solution that will use two Azure Functions apps: App1 and App2. App1 is Windows based and will be deployed as code. App2 is Linux based and will be deployed as a container image.
Estimates show that the duration of the request processing for both apps will range from 1 to 10 minutes.
You plan to implement App1 and App2 by using the hosting plan to satisfy the following requirements:
* Request processing can complete within the estimated time range.
* The autoscaling behavior is event driven.
* The upper scaling limit is maximized.
You need to create the hosting plan for the implementation.
Which hosting plan should you create? To answer, move the appropriate hosting plans to the correct apps.
You may use each hosting plan once, more than once, or not at all. You may..
Answer:
Explanation:
Explanation:
pp1: Consumption. App2: Premium.
Detailed Explanation: For App1, the Windows code-based Consumption plan satisfies event-driven autoscaling, allows execution up to the stated ten-minute range, and has the highest scaling ceiling among the listed event-driven choices for this workload. App2 is deployed as a Linux container image; among Premium, Dedicated, and Consumption, Premium is the event-driven plan that supports Linux containerized Functions and permits long-running execution. Dedicated hosting is not the requested event-driven autoscale model. The original Premium/Premium answer therefore did not maximize App1's upper scaling limit.
Study Guide Alignment: Azure service integration: Service Bus, Event Grid, Azure Functions triggers
/bindings, and event-driven processing.
Official Microsoft Learn References: AI-200 Study Guide | Azure Functions scale and hosting
NEW QUESTION # 78
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 connection strings in the Function app application settings configured in the Azure Portal.
Does the solution meet the goal?
Answer: A
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 # 79
Hotspot Question
You deploy a Linux container image to App Service.
The container requires the following environment variables at runtime:
- A non-sensitive configuration value named MODEL_VERSION
- A database password that must remain secure
You need to configure App Service to provide these environment variables at runtime.
Which configurations should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: App setting with plain text value
To provide a non-sensitive environment variable named MODEL_VERSION to a Linux custom container deployed to Azure App Service, you should configure it as an Application Setting (App Setting). In Azure App Service, any custom configuration defined within the Application Settings is automatically injected into Linux containers as an environment variable at runtime.
Box 2: Key Vault reference syntax
You should configure the database password as an Azure Key Vault reference inside App Service Application Settings. This prevents the secret from being stored in plaintext in the app configuration and injects it securely into the container as an environment variable at runtime.
Reference:
https://learn.microsoft.com/en-us/azure/app-service/tutorial-custom-container?tabs=azure-cli&pivots=container-linux
NEW QUESTION # 80
You need to implement the semantic retrieval workflow for the recommendation engine to meet the technical and performance requirements of Fabrikam Inc.
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:
* Define a table schema with vector and metadata columns.
* Load embedding vectors and associated product metadata.
* Configure a Hierarchical Navigable Small World (HNSW) index on the embedding vector columns.
* Perform a similarity search using a WHERE clause and the < = > operator.
The workflow must first establish a PostgreSQL schema containing both the pgvector embedding column and the product metadata required for filtering. The embeddings and their associated metadata are then bulk- loaded. This ordering is important because Microsoft recommends loading data before creating vector indexes ; creating the index afterward provides faster ingestion and a more optimal index layout.
After loading, configure an HNSW index on the embedding column. HNSW is an approximate-nearest- neighbor index supported by pgvector and provides a strong speed/recall tradeoff for low-latency vector retrieval. Microsoft specifically documents HNSW for efficient cosine-distance searches in Azure Database for PostgreSQL.
Finally, execute the retrieval query so that mandatory product metadata constraints are applied through a WHERE clause , while vector similarity is evaluated with the < = > cosine-distance operator . This satisfies Fabrikam ' s requirement to calculate similarity only for eligible products.
A B-tree index is not an ANN vector index, and increasing Redis memory does not implement PostgreSQL semantic retrieval.
Study Guide references: Azure Database for PostgreSQL # pgvector, HNSW indexing, bulk-load optimization, vector similarity operators, metadata-filtered retrieval.
NEW QUESTION # 81
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. The API key must not be stored in application configuration directly. The API key must be accessed securely from Azure Key Vault.
You need to ensure that the API key is stored securely in Azure Key Vault and is available to the container at runtime without being exposed in source control or Git commit history.
Solution: Store the API key as an App Service application setting configured through the Azure Portal.
Does the solution meet the goal?
Answer: A
NEW QUESTION # 82
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