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| Section | Objectives |
|---|---|
| Develop containerized solutions on Azure | - Implement containerized applications
|
| Secure, monitor, troubleshoot Azure solutions | - Operate AI cloud solutions
|
| Connect to and consume Azure services | - Integrate Azure services
|
| Develop AI solutions by using Azure data management services | - Work with Azure data platforms for AI workloads
|
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NEW QUESTION # 27
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: A
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 # 28
You are designing a messaging solution by using Service Bus for AI document processing.
You need to ensure that a published message is delivered to multiple independent consumers.
Each consumer must receive their own copy of the message.
Which two Service Bus entities should you use? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: B,C
Explanation:
Topics and Subscriptions are the two Azure Service Bus entities that fit this scenario.
Topics: The publisher sends the document processing message to a single topic, which acts as the central distribution hub.
Subscriptions: Each independent consumer creates its own individual subscription under that topic. When a message arrives, a copy is forwarded to every independent subscription so each consumer can process their own copy safely and separately.
Reference:
https://learn.microsoft.com/en-us/azure/service-bus-messaging/service-bus-queues-topics-subscriptions
NEW QUESTION # 29
You are developing an Azure Functions app.
All functions in the app meet the following requirements:
* Run until either a successful run or until 10 run attempts occur.
* Ensure that there are at least 20 seconds between attempts for up to 15 minutes.
You need to configure the host.json file.
How should you complete the code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 30
You develop an AI application that stores telemetry documents in Azure Cosmos DB for NoSQL.
Each document contains 30 properties. Queries filter only on deviceId and timestamp.
Write operations consume more RUs than expected.
You need to reduce RU consumption for write operations while maintaining query performance.
What should you do?
Answer: D
Explanation:
Modify the indexing policy to include only deviceId and timestamp paths, and exclude all other paths. By default, Azure Cosmos DB indexes every property in a document, which drastically inflates the Request Units (RUs) consumed during write operations.
Reference:
https://www.datacamp.com/tutorial/azure-cosmos-db
NEW QUESTION # 31
You need to configure the database resources for the Azure Database for PostgreSQL instance.
How should you complete the configuration to meet the business and technical requirements? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
* Meet the 200-ms semantic search latency requirement: Increase compute vCores.
* Optimize the environment for high-dimensional pgvector index residency: Increase memory allocation.
* Support the continuous ingestion of transaction-based embeddings: Enable storage autoscale.
For the strict sub-200-ms vector-search latency target, increasing compute vCores is the appropriate choice.
Vector similarity operations are computationally intensive, and additional CPU capacity improves mathematical throughput and parallel query execution. Microsoft's pgvector guidance emphasizes query-plan optimization, ANN indexes such as HNSW, and sufficient compute resources when optimizing vector workloads.
For high-dimensional pgvector index residency , increase memory allocation . HNSW provides strong query-performance characteristics but consumes more memory than IVFFlat. Keeping frequently accessed vector index structures in memory minimizes disk access and materially improves latency. Microsoft explicitly notes that HNSW requires more memory while providing a better speed/recall tradeoff.
For the continuous ingestion of millions of embeddings, enable storage autoscale . Azure Database for PostgreSQL Flexible Server can automatically increase allocated storage as capacity approaches configured thresholds, avoiding an out-of-storage condition as data volumes grow. Microsoft recommends storage autogrow for workloads whose storage demand can increase dynamically.
Increasing max_connections does not directly improve vector computation or index residency, read replicas primarily scale reads, and backup retention does not address ingestion capacity.
Study Guide references: Azure Database for PostgreSQL Flexible Server # pgvector performance optimization; compute and memory sizing; HNSW indexing; storage autogrow.
NEW QUESTION # 32
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