Functions with Cosmos DB integration in Azure - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ service_behavior

intermediate

How does an Azure Function trigger with Cosmos DB change feed behave?

An Azure Function is set to trigger on changes in a Cosmos DB container using the change feed. What happens when multiple changes occur rapidly in the container?

A The function batches multiple changes and processes them together, while preserving order within the batch.

B The function processes each change individually and sequentially, ensuring order is preserved.

C The function triggers only once per minute regardless of changes, processing all changes at once.

D The function triggers only for the first change and ignores subsequent changes until manually reset.

💡 Hint

Think about how change feed triggers optimize processing for multiple changes.

The Azure Function triggered by Cosmos DB change feed batches multiple changes together for efficient processing. While the batch preserves order of changes within it, the function does not process each change individually. This improves performance and scalability.

❓ Configuration

intermediate

Which configuration setting controls the lease container for Cosmos DB trigger in Azure Functions?

You want to configure an Azure Function with a Cosmos DB trigger. Which app setting specifies the container used to store leases for tracking processed changes?

A AzureWebJobsCosmosDBLeaseContainerName

B CosmosDBLeaseContainer

C CosmosDBTriggerLeaseContainerName

D AzureWebJobsCosmosDBLeasesContainerName

💡 Hint

Look for the exact app setting name used by Azure Functions runtime for Cosmos DB leases.

The correct app setting is AzureWebJobsCosmosDBLeaseContainerName. This setting tells the function which Cosmos DB container to use for storing leases that track the change feed progress.

❓ Architecture

advanced

Designing a scalable Azure Function with Cosmos DB input binding

You need to design an Azure Function that reads documents from Cosmos DB using an input binding. The function must scale efficiently when many requests come in simultaneously. Which architecture choice best supports this?

A Use a single function instance with a large RU/s provisioned on Cosmos DB to handle all requests sequentially.

B Use a timer-triggered function that reads all documents periodically and caches results for all requests.

C Use a function with input binding that queries Cosmos DB without specifying partition key to simplify code.

D Use multiple function instances with input bindings that query Cosmos DB by partition key to distribute load evenly.

💡 Hint

Consider how Cosmos DB partitioning affects scalability and function instance distribution.

Using multiple function instances with input bindings that specify the partition key allows Cosmos DB to distribute the load evenly. This supports efficient scaling and avoids hot partitions or throttling.

❓ security

advanced

Securing Azure Functions with Cosmos DB output binding

You have an Azure Function that writes data to Cosmos DB using an output binding. What is the best practice to secure the connection string used by the function?

A Store the Cosmos DB connection string directly in the function code as a constant string.

B Store the connection string in Azure Key Vault and reference it via managed identity in the function app settings.

C Store the connection string in a public GitHub repository for easy access by developers.

D Hardcode the connection string in the function app settings without encryption.

💡 Hint

Think about secure storage and access of secrets in Azure.

Storing secrets like connection strings in Azure Key Vault and accessing them via managed identity is a best practice. It avoids exposing secrets in code or unencrypted settings and supports secure, centralized secret management.

🧠 Conceptual

expert

Understanding consistency levels impact on Azure Function with Cosmos DB trigger

An Azure Function is triggered by Cosmos DB change feed. The Cosmos DB account uses Session consistency. How does this consistency level affect the function's visibility of changes?

A The function sees changes only after a full global replication completes, causing high latency.

B The function sees all committed changes immediately and in order across all regions.

C The function sees changes in order per session, but changes from other sessions may be delayed or out of order.

D The function cannot use change feed with Session consistency and will fail to trigger.

💡 Hint

Recall what Session consistency guarantees about read order and visibility.

Session consistency guarantees that reads within a session see writes in order, but across different sessions, there may be delays or out-of-order visibility. The change feed reflects committed changes but may not be globally ordered immediately.