AZ-204 Develop for Azure storage Practice Test 5 — 15 Questions

Question 1

A serverless app must react whenever audit documents are inserted or updated in Cosmos DB. Which trigger should the Azure Function use?

Accepted Answer

Cosmos DB trigger. The Azure Cosmos DB trigger listens to the change feed of a Cosmos DB container, which captures inserts and updates to documents. This makes it the ideal choice for reacting to audit document changes in a serverless app, as it automatically invokes the function when new or modified documents appear in the feed.

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Queue trigger

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Timer trigger

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HTTP trigger

Question 2

A Blob Storage workflow for product images must prevent accidental overwrite and support recovery of prior versions. Which two features should be enabled?

Accepted Answer

Blob soft delete. Blob soft delete (B) protects against accidental deletion or overwrite by retaining deleted blobs for a configurable retention period, allowing recovery. Blob versioning (D) automatically maintains prior versions of a blob, enabling restoration of any previous state. Together, they provide comprehensive protection against overwrites and support version recovery.

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SFTP support

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Static website hosting

Question 3

A queue-processing application stores work items in Azure Queue Storage. A worker crashes after receiving a message. What determines when the message becomes available for another worker?

Accepted Answer

Visibility timeout. When a worker receives a message from Azure Queue Storage, the message becomes invisible to other workers for a period defined by the visibility timeout. If the worker crashes without deleting or updating the message, the visibility timeout expires and the message reappears in the queue, making it available for another worker to process. This mechanism ensures at-least-once processing and prevents message loss on worker failure.

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Blob lease duration

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Message TTL only

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Poison queue threshold only

Question 4

A .NET app performs point reads from Cosmos DB by id and partition key. The team wants the lowest latency and best throughput efficiency. Which API call pattern should be used?

Accepted Answer

ReadItemAsync with id and partition key. ReadItemAsync with id and partition key is the most efficient API call pattern for point reads in Cosmos DB because it directly accesses the document using the partition key and item ID, requiring only a single request to the exact partition and replica. This avoids the overhead of querying multiple partitions or scanning all items, resulting in the lowest latency and best throughput efficiency, as it consumes the minimum request units (RUs) possible for a read operation.

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Stored procedure scanning all items

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Change feed processor

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SELECT * query without partition key

Question 5

An application needs to upload large thumbnail metadata to Blob Storage reliably over unstable networks. Which upload approach should be used?

Accepted Answer

Block blob staged block upload with commit. Block blob staged block upload with commit is the correct approach because it allows uploading large thumbnails in smaller, independent blocks that can be retried individually if a network failure occurs. This method uses the Put Block and Put Block List REST APIs, enabling reliable uploads over unstable networks by committing only successfully uploaded blocks. It is specifically designed for large files and provides fine-grained control over upload progress and error recovery.

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Page blob only

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Append blob only

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Table Storage batch operation

Question 6

A table stores session records in Azure Table Storage. Queries frequently retrieve all records for one customer in a time range. What key design is best?

Accepted Answer

PartitionKey as customer ID and RowKey based on sortable timestamp. Azure Table Storage queries are most efficient when they target a single partition key. Using the customer ID as the PartitionKey ensures all records for a customer are in the same partition, and using a sortable timestamp (e.g., inverted ticks) as the RowKey allows efficient range queries within that partition, leveraging the table's natural index order.

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RowKey as a constant value

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PartitionKey as a random GUID for every record

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PartitionKey as the full JSON payload

Question 7

A Blob-triggered function processing audit documents fires multiple times for the same blob after retries. What should the function design include?

Accepted Answer

Idempotent processing based on blob name/version or metadata. Azure Blob Storage triggers can cause multiple function invocations for the same blob due to retries, internal queue processing, or event-driven architecture guarantees. Designing the function to be idempotent—using the blob name, version, or metadata as a unique identifier—ensures that duplicate processing does not produce side effects like duplicate audit records or data corruption. This aligns with the at-least-once delivery semantics of Azure Blob Storage triggers.

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Disable all logging

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Assume each event is delivered exactly once

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Use public blob access

Question 8

An app must store relational state and perform transactions across multiple tables with T-SQL support. Which Azure data service should the developer choose?

Accepted Answer

Azure SQL Database. Azure SQL Database is a fully managed relational database service that supports T-SQL and ACID transactions across multiple tables, making it the correct choice for storing relational state and performing transactional operations. It provides built-in high availability, automatic backups, and elastic scaling, which are essential for enterprise applications requiring consistent, multi-table transactions.

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Azure Queue Storage

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Azure Cache for Redis

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Azure Blob Storage

Question 9

A Cosmos DB workload for telemetry events has predictable traffic during business hours and almost no traffic overnight. The team wants to reduce cost while keeping performance during peak hours. What should be configured?

Accepted Answer

Autoscale throughput with an appropriate maximum RU/s. Autoscale throughput (option B) is correct because it dynamically scales the provisioned RU/s between 10% of the configured maximum and the maximum itself based on actual demand. For a workload with predictable peak traffic during business hours and near-zero traffic overnight, autoscale eliminates the cost of provisioning for peak capacity 24/7 while ensuring performance is not throttled during high-demand periods. This directly addresses the cost-reduction goal without sacrificing peak-hour performance.

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Analytical store only

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Manual throughput set permanently to peak RU/s

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Disable indexing entirely

Question 10

An application stores customer invoices in Azure Blob Storage. Deleted blobs must be recoverable for 14 days. What should be enabled? The design must avoid adding custom operational scripts.

Accepted Answer

Blob soft delete with a 14-day retention period. Blob soft delete protects against accidental deletion by retaining deleted blobs for a specified retention period. Enabling it with a 14-day retention period allows recovery of deleted invoices within that window without custom scripts, meeting the requirement exactly.

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Archive access tier

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Static website hosting

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Immutable blob legal hold

Question 11

A storage account for thumbnail metadata must allow an application to read only blobs under one container for two hours. The application should not receive the account key. What should be issued? The design must avoid adding custom operational scripts.

Accepted Answer

A service SAS scoped to the container with read permission and expiry. A service SAS (Shared Access Signature) scoped to a specific container with read permission and an expiry time of two hours meets the requirement: it grants time-limited read access to blobs under that container without exposing the account key. The SAS token is issued to the application, which can then use it to authenticate requests directly to Azure Blob Storage, avoiding the need for custom scripts.

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A public access level on the container

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A management group assignment

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The storage account access key

Question 12

A Cosmos DB container for session records receives hot-partition throttling because the partition key has only five possible values. What should the developer change? The design must avoid adding custom operational scripts.

Accepted Answer

Choose a partition key with higher cardinality and even request distribution. Hot-partition throttling occurs when a partition key has low cardinality (few distinct values), causing uneven request distribution and exceeding the physical partition's throughput limits. Choosing a partition key with higher cardinality and even request distribution spreads operations across more physical partitions, eliminating throttling without custom scripts.

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Increase the default TTL

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Enable analytical store only

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Use a stored procedure for every write

Question 13

A serverless app must react whenever audit documents are inserted or updated in Cosmos DB. Which trigger should the Azure Function use? The design must avoid adding custom operational scripts.

Accepted Answer

Cosmos DB trigger. The Azure Cosmos DB trigger is the correct choice because it natively listens to the Cosmos DB change feed, which captures inserts and updates to documents. This allows the Azure Function to react automatically without any custom scripts or polling logic, aligning with the serverless and operational simplicity requirements.

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Queue trigger

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Timer trigger

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HTTP trigger

Question 14

An application stores sensor readings in Azure Table Storage. Each sensor produces thousands of readings per hour. Queries always filter by sensor ID and time range. A developer needs to choose the partition key and row key. Which design best balances query performance and write throughput?

Accepted Answer

Partition key: sensor ID; row key: ISO timestamp of the reading. It uses sensor ID as the partition key, which ensures all readings for a given sensor are stored in the same partition, enabling efficient range queries by row key (timestamp). This design avoids hot partitions by distributing writes across different sensors, while the row key allows fast point lookups and range scans within a time window, balancing query performance and write throughput.

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Partition key: a single constant ('all-sensors'); row key: sensor ID + timestamp

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Partition key: timestamp (rounded to the hour); row key: sensor ID

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Partition key: random GUID per reading; row key: timestamp

Question 15

A background worker retrieves a message from Azure Queue Storage and begins processing. The processing logic takes longer than the configured visibility timeout. Before the worker finishes, the timeout expires. What happens to the message?

Accepted Answer

The message becomes visible again in the queue and another worker can dequeue it. When the visibility timeout expires, the message becomes visible again in the queue because Azure Queue Storage uses a lease-based mechanism. The worker that dequeued the message loses its exclusive visibility lease, allowing another worker to dequeue and process the same message. This ensures at-least-once delivery semantics, preventing message loss if a worker fails or takes too long.

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The message is permanently deleted because the worker already dequeued it

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The message moves to a dead-letter queue after the visibility timeout expires

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Processing continues uninterrupted; the visibility timeout applies only to the initial retrieval window