AZ-204 Case Studies & Practical Application 5

Question 1

A developer needs to run a one-time data migration job in Azure that processes 10 million records. The job must finish within 2 hours and should not affect the production App Service. Which compute option is most appropriate?

Accepted Answer

Azure Batch with a pool of VMs sized for parallel task execution

Answer

Azure Batch is purpose-built for large-scale parallel compute jobs, allowing a pool of VMs to process millions of records in parallel without impacting other services.

Question 2

A SaaS application uses Azure AD B2C for customer identity. After login, the app's API must know the user's subscription tier stored in B2C. How should this be included in the token?

Accepted Answer

Use a custom user attribute in B2C and add it as a claim to the token via a user flow

Answer

B2C custom attributes can be included as token claims through user flow configuration, embedding application-specific data directly in the JWT without additional API calls.

Question 3

A developer implements an Azure Durable Function orchestrator that calls three activity functions in parallel and waits for all to complete. Which Durable Functions pattern does this represent?

Accepted Answer

Fan-out/fan-in using Task.WhenAll on multiple activity calls

Answer

Fan-out/fan-in starts multiple activity functions simultaneously and uses Task.WhenAll to aggregate results, enabling parallel execution within a single orchestration.

Question 4

A developer is securing an Azure Function HTTP trigger. It should only accept requests from a specific Azure Front Door instance. What is the recommended approach?

Accepted Answer

Validate the X-Azure-FDID header against the expected Front Door ID and use IP restriction to allow only Front Door's service tags

Answer

Validating the X-Azure-FDID header combined with IP restrictions on the AzureFrontDoor.Backend service tag ensures only your specific Front Door instance can reach the function.

Question 5

An application uses Azure Table Storage for telemetry data partitioned by DeviceId. Queries for a specific device's data over a date range are slow. What should the developer check first?

Accepted Answer

Verify the RowKey is set to a time-based value so date-range queries become efficient partition scans

Answer

In Azure Table Storage, using a time-based RowKey with a fixed PartitionKey turns date-range queries into efficient partition-range scans using the built-in primary index.

AZ-204 - Microsoft Azure Developer Associate Practice Test

AZ-204 - Microsoft Azure Developer Associate Practice Test

AZ-204 Case Studies & Practical Application 5