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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Integrate backend services and build event-driven architectures | 25% | - Build serverless APIs and workflows
|
| Topic 2: Secure, monitor, and optimize AI solutions | 20% | - Implement observability and reliability
|
| Topic 3: Develop AI solutions using Azure data services | 30% | - Implement vector-enabled databases
|
| Topic 4: Develop containerized AI solutions on Azure | 25% | - Monitor and troubleshoot containerized workloads
|
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NEW QUESTION # 49
You need to deploy a batch retraining workload.
How should you complete the scaling configuration? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
* Trigger scaling based on queue depth: Azure Service Bus scaler
* Deallocate instances when the message queue is empty: Minimum replicas = 0
* Prevent uncontrolled burst scaling: Maximum replicas = 10
The batch retraining workload must scale according to queue length , so the correct KEDA trigger is the Azure Service Bus scaler . KEDA integrates with Azure Service Bus and monitors queue or subscription metrics, allowing AKS workloads to scale according to the number of pending messages rather than CPU utilization or HTTP request concurrency. Microsoft documents KEDA as the event-driven autoscaling mechanism for AKS and specifically supports Azure-based scalers for queue-driven workloads.
To meet the requirement to deallocate workload instances when no messages remain, configure minimum replicas = 0 . KEDA supports scale-to-zero for event-driven workloads, which is particularly appropriate for intermittent batch or machine-learning processing because no pods need to remain running while the queue is empty. Microsoft specifically identifies scale-to-zero as a KEDA capability and recommends it for sporadic ML workloads where cost optimization is important.
Finally, configure maximum replicas = 10 . A finite maximum replica count establishes an explicit upper scaling boundary, preventing large queue bursts from creating an uncontrolled number of worker replicas and protecting both infrastructure cost and downstream services.
Study Guide references: AKS # KEDA; Azure Service Bus event-driven scaling; minReplicaCount; maxReplicaCount; scale-to-zero; autoscaling limits.
NEW QUESTION # 50
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: Embed the API key as a hardcoded environment variable in the Dockerfile.
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 # 51
You are designing an Azure Database for PostgreSQL table for semantic search. Queries frequently filter on the created_at column. The schema must support vector similarity search and reliable date filtering You need to ensure that the table meets the requirements.
Which two actions should you perform? Each correct answer presents part of the solution. Choose two NOTE: Each correct selection is worth one point.
Answer: A,D
Explanation:
Detailed Explanation: A semantic-search schema should use the pgvector vector type for embeddings so PostgreSQL can apply vector distance operators and vector indexes. The creation timestamp should use an actual timestamp data type so comparisons, range predicates, ordering, and indexes use date semantics rather than string ordering. Storing vectors or dates as free-form character data would sacrifice type validation and make similarity or date filtering less efficient and less reliable.
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 | Vector similarity search with Azure PostgreSQL
NEW QUESTION # 52
A development team wants to compare multiple prompt variations against the same test dataset and visualize which prompt performs best. What should they use in Azure AI Foundry?
Answer: D
Explanation:
Prompt flow variants allow you to define multiple versions of a prompt/node, run them against the same evaluation dataset, and compare results side-by-side to identify the best-performing variant.
NEW QUESTION # 53
You are developing a Python application that reads data from Azure Cosmos DB for NoSQL by using the azure-cosmos SDK.
You need to connect to an existing account and run a SQL query against an existing container.
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:
* Initialize CosmosClient.
* Specify the database name.
* Obtain the container reference.
* Run the query.
The Python Azure Cosmos DB SDK follows the resource hierarchy Account # Database # Container # Items . The first step is to initialize a reusable CosmosClient with the Azure Cosmos DB account endpoint and an appropriate credential. Microsoft identifies CosmosClient as the primary client for interacting with databases in an account.
Next, specify the existing database by calling client.get_database_client(database_name). This returns a DatabaseProxy representing that database. Importantly, DatabaseProxy should not be instantiated directly ; Microsoft explicitly states that it should be obtained through CosmosClient.get_database_client().
From the database reference, obtain the target container by using database.get_container_client (container_name). Microsoft documents this as the standard way to retrieve an existing container reference.
Finally, execute the SQL query through container.query_items() , which accepts the Cosmos DB for NoSQL query and optional parameters and returns an iterable result set.
Therefore, Initialize DatabaseProxy is the distractor: the SDK creates the DatabaseProxy through get_database_client; application code should not construct it directly.
Study Guide references: Azure Cosmos DB for NoSQL # Python SDK; CosmosClient; database and container clients; ContainerProxy.query_items().
NEW QUESTION # 54
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