Microsoft AZ-204 Developing Solutions for Microsoft Azure Exam Dumps and Practice Test Questions Set 2 Q21-40

Visit here for our full Microsoft AZ-204 exam dumps and practice test questions.

Question 21:

You are developing an Azure Function that processes messages from multiple Event Hubs. You need to ensure high availability and at-least-once message delivery while minimizing duplicates. Which approach should you use?

A) Single consumer for all partitions
B) Use checkpointing with partitions and enable retry policies
C) ReceiveAndDelete mode for messages
D) Ignore checkpointing and process events directly

Answer: B) Use checkpointing with partitions and enable retry policies

Explanation:

Checkpointing with partitions and enabling retry policies is the optimal approach because Event Hubs organizes messages into partitions. Checkpointing allows the function to track the last successfully processed event per partition, so if a function fails, it can resume from the last checkpoint, maintaining at-least-once delivery. Retry policies help handle transient failures such as network interruptions, throttling, or temporary unavailability of Event Hubs, ensuring messages are not lost and processing is reliable. This combination allows parallel consumption of partitions, increases throughput, and preserves message ordering per partition, which is critical for many production workloads and aligns with AZ-204 best practices.

Using a single consumer for all partitions would limit throughput because one consumer cannot process multiple partitions simultaneously. While it simplifies architecture, it introduces a bottleneck and may delay processing for high-volume workloads. It also makes it harder to scale the system horizontally because adding more consumers does not improve throughput unless partition assignments are considered.

ReceiveAndDelete mode immediately removes messages when received, which can simplify processing. However, it does not allow the function to retry if an error occurs during processing. Any failure could result in permanent message loss, making it unsuitable for scenarios where data reliability and fault tolerance are essential.

Ignoring checkpointing and processing messages directly without tracking progress is another approach, but it can cause messages to be processed multiple times or skipped if the function restarts unexpectedly. Without checkpoints, the system cannot recover gracefully from failures, reducing reliability and violating best practices for event-driven, fault-tolerant applications.

Question 22:

You are developing an Azure App Service web API that stores confidential data in Azure SQL Database. You need to rotate encryption keys without downtime. Which solution should you implement?

A) Enable TDE with Azure Key Vault-managed keys
B) Implement column-level encryption manually
C) Restrict access using IP firewall rules
D) Encrypt data in the application layer only

Answer: A) Enable TDE with Azure Key Vault-managed keys

Explanation:

Enabling Transparent Data Encryption (TDE) with Azure Key Vault-managed keys provides encryption for the entire database at rest while allowing centralized key management. Azure SQL can rotate keys without downtime, re-encrypting data seamlessly. This approach supports compliance requirements like GDPR, HIPAA, and PCI-DSS, reduces operational overhead, and ensures that sensitive data remains protected even during key rotation.

Column-level encryption can protect specific sensitive fields, but managing encryption at this level adds complexity. Key rotation must be handled manually, and ensuring all application components can access the updated keys requires careful coordination. This approach can also affect query performance if many columns are encrypted.

IP firewall rules restrict access to the database from specific networks, which helps protect against unauthorized connections. However, firewall rules do not encrypt the data itself and cannot manage key rotation. While it provides an additional layer of security, it is not sufficient for protecting sensitive data at rest or ensuring secure key management.

Encrypting data at the application layer allows developers to protect sensitive information before it reaches the database. However, key management becomes the responsibility of the application, which increases the risk of errors and complicates rotation. It also lacks integration with Azure’s native encryption services and does not provide enterprise-grade, seamless security.

Question 23:

You are building a Logic App to process order approvals. The workflow should retry failed steps automatically and send notifications on persistent failures. Which configuration should you use?

A) Use a recurrence trigger only
B) Use an HTTP request trigger without retries
C) Use built-in retry policies and configure run-after conditions
D) Handle retries manually in each action without run-after

Answer: C) Use built-in retry policies and configure run-after conditions

Explanation:

Using built-in retry policies and configuring run-after conditions allows the Logic App to automatically retry actions that fail due to transient errors. You can configure maximum retry attempts, intervals, and types of retriable errors. Run-after conditions enable subsequent actions, like sending notifications, to execute when persistent failures occur. This ensures workflows are resilient, fault-tolerant, and maintainable, aligning with AZ-204 exam objectives for integration solutions.

A recurrence trigger alone executes workflows on a schedule, independent of events. It does not respond immediately to events, so using only a recurrence trigger would delay processing and add unnecessary executions, which is inefficient for event-driven scenarios like order approvals.

An HTTP request trigger in Azure Logic Apps is a convenient way to start a workflow when an external system sends an HTTP request. It is especially useful for integrating with APIs, webhooks, or custom applications that need to invoke a workflow on demand. However, while this trigger provides immediate activation of the workflow, it does not inherently provide built-in retries for downstream actions. This means that if any action within the workflow fails due to transient issues—such as network timeouts, temporary service unavailability, or rate limiting—the workflow will not automatically retry the failed action unless explicitly configured. This limitation introduces a potential risk of incomplete or inconsistent processing, especially in critical enterprise scenarios where workflows interact with multiple external systems or services.

To address these potential failures when using an HTTP trigger, developers must implement custom error handling and retry logic for each action. This involves adding conditional checks, run-after conditions, and loops to manually retry failed actions a certain number of times or until a successful response is obtained. While Logic Apps supports features like run-after conditions, if developers do not leverage these built-in features correctly, they must write additional parallel or sequential branches to handle failures, increasing workflow complexity. For example, every HTTP action, data transformation, or storage operation must include its own error-handling branch to account for temporary failures, which can quickly become cumbersome in large workflows with dozens of actions.

Furthermore, manually implementing retries increases the maintenance burden. Any changes to the workflow logic—such as adding new actions, modifying existing steps, or integrating with additional systems—requires revisiting and potentially updating the error-handling mechanisms across multiple actions. This approach is error-prone, because missing a retry branch for a single action could lead to data loss, inconsistent states, or failed workflows that are difficult to diagnose. In contrast, leveraging built-in retry policies in triggers such as the Blob Storage trigger (“When a file is created”) or Event Grid trigger allows developers to offload this responsibility to Logic Apps’ managed runtime. Logic Apps automatically retries actions based on configurable retry policies, including intervals, exponential backoff, and maximum attempts, ensuring transient failures are handled consistently and reliably without additional code or workflow branches.

Another consideration is observability and alerting. When retries are implemented manually, developers must also implement logging, notifications, and monitoring for each retry branch. This not only increases complexity but also increases the chance of missing critical failure events or generating inconsistent logs. Native retry handling in Logic Apps integrates seamlessly with Azure Monitor and Log Analytics, allowing centralized monitoring of workflow execution, failures, and retry attempts. This reduces operational overhead and provides a clearer view of workflow health and performance.

Finally, the maintainability and scalability of the workflow are impacted when retries are handled manually. As workflows grow in size and complexity, or when multiple workflows depend on the same external systems, implementing and managing retry logic manually becomes exponentially harder. By contrast, using triggers and actions with native retry capabilities ensures that the workflow remains resilient, maintainable, and easier to evolve over time, while reducing the risk of human error and operational issues.

In conclusion, while HTTP triggers are excellent for on-demand workflow execution, relying solely on manual retries for downstream actions introduces complexity, increases the risk of failures, and reduces maintainability. Leveraging Logic Apps’ native retry mechanisms is strongly recommended for building robust, fault-tolerant, and maintainable serverless workflows.

Question 24:

You are developing an Azure Function to process messages from an Azure Storage Queue. You want to retry transient failures automatically without losing messages. Which approach should you choose?

A) Implement retry policies with exponential backoff
B) Ignore failures and let the function fail
C) Use static sleep loops without retry policies
D) Rely on the queue only to handle retries

Answer: A) Implement retry policies with exponential backoff

Explanation:

Implementing retry policies with exponential backoff allows the function to handle transient errors like temporary network failures or throttling. Exponential backoff increases the wait time between retries, which prevents overwhelming the queue or service and provides robust, reliable processing. This approach is recommended for high-volume, production-grade serverless applications and aligns with AZ-204 best practices.

Ignoring failures allows transient errors to cause message loss, reducing reliability. This approach is unsuitable for production workloads that require at-least-once processing guarantees.

Static sleep loops retry after fixed intervals but do not adapt to repeated failures. This can result in inefficient resource utilization and potential throttling issues, especially during spikes in queue activity.

Relying solely on the queue to handle retries limits control over processing behavior. While queues have visibility timeouts, the function needs explicit retry logic to implement strategies like exponential backoff, logging, or error handling. Without it, transient failures may cause inconsistent results or delays in message processing.

When designing Azure Functions that process messages from a queue or other event-driven sources, handling transient failures correctly is critical to ensure reliability and prevent data loss. Each of the options listed has different implications for processing behavior, throughput, and maintainability.

Option A – Implement retry policies with exponential backoff is the recommended approach. Retry policies allow the function to automatically attempt reprocessing of failed messages in a controlled manner. Using exponential backoff, the interval between retries increases progressively with each attempt, which helps reduce system strain during persistent failures or service outages. For example, if a downstream service is temporarily unavailable, the first retry may occur after one second, the next after two seconds, then four seconds, and so on. This approach prevents thundering herd issues, where multiple retries flood the system and exacerbate failures. Additionally, Azure Functions provides built-in support for retry policies, including options for maximum retry counts, delay intervals, and retry behavior configuration per trigger type. Combined with dead-letter queues, this ensures that messages that consistently fail after maximum retries are captured for later investigation, rather than being lost or causing indefinite failures. This approach maximizes reliability, fault tolerance, and operational resilience.

Option B – Ignore failures and let the function fail is inherently unsafe. Without retries, any transient failure in processing a message will result in immediate loss of processing for that message. In queue-based architectures, letting functions fail without retries often causes message loss, workflow inconsistencies, and potential downstream data integrity issues. While some queues support automatic message re-enqueueing, relying solely on the queue does not give developers control over retry timing or handling different failure types intelligently. Ignoring failures also makes debugging and monitoring more difficult because failures may occur silently or inconsistently, complicating operational management.

Option C – Use static sleep loops without retry policies is also problematic. Developers may implement simple retry logic by sleeping for a fixed interval and attempting the operation again. While this may provide basic reattempts, it is inefficient and brittle. Fixed delays do not adapt to transient system load, network issues, or external service availability, which can result in excessive delays or unnecessary retries. Moreover, implementing static loops increases code complexity, and manual retry logic is prone to errors, lacks centralized monitoring, and does not integrate with Azure Functions’ native retry and dead-letter mechanisms. This approach is less maintainable and harder to scale.

Option D – Rely on the queue only to handle retries assumes that the queue’s delivery semantics are sufficient for reliability. While queues like Azure Storage Queues and Service Bus Queues support retry mechanisms such as visibility timeouts and poison message handling, relying solely on the queue does not give developers the flexibility to implement exponential backoff, selective retries, or failure categorization. For example, transient network errors may require short delays between retries, while downstream throttling may require longer backoff intervals. Without configurable retry logic in the function, the system cannot optimize for different types of transient failures.

For reliable queue processing in Azure Functions, the best practice is to implement retry policies with exponential backoff. This approach combines automated retries, fault tolerance, and maintainability, while minimizing the risk of system overload or message loss. Other options either compromise reliability, increase operational complexity, or fail to leverage Azure’s managed retry and monitoring capabilities effectively.

Question 25:

You are designing an Azure API Management (APIM) instance for internal APIs. You want to restrict access to only authenticated users and capture detailed request logs for auditing. Which configuration should you implement?

A) Use OAuth 2.0 with Azure AD and enable diagnostic logging
B) Anonymous access with Azure Monitor
C) Basic authentication with local logs
D) IP restrictions only

Answer: A) Use OAuth 2.0 with Azure AD and enable diagnostic logging

Explanation:

Using OAuth 2.0 with Azure AD enforces identity-based access control, ensuring that only authenticated users or applications from your organization can access APIs. Diagnostic logging captures request and response data, headers, and metadata, which can be forwarded to Log Analytics, Event Hubs, or Storage for auditing and compliance purposes. This approach is secure, scalable, and meets AZ-204 exam requirements for production-ready API management.

Anonymous access with Azure Monitor provides logging but does not restrict API usage. Anyone can call the APIs, which expose sensitive data. While monitoring may detect unauthorized access after the fact, it does not prevent it.

Basic authentication with local logs can restrict access using credentials, but it lacks the centralized identity management and scalability of OAuth 2.0. Local logs are less reliable and harder to manage for auditing or compliance.

IP restrictions block requests from certain networks but do not validate user identity. Users from allowed IPs could still access sensitive APIs, so this method alone does not meet enterprise security requirements.

Question 26

You are designing an Azure Function that will process high-volume messages from an Azure Storage Queue. You need to scale out dynamically and maintain at least once message delivery. Which configuration should you use?

A) Use a single function instance with the ReceiveAndDelete mode
B) Use multiple function instances with peek-lock and queue trigger
C) Poll the queue manually with a timer trigger
D) Process messages directly without a trigger

Answer: B) Use multiple function instances with peek-lock and queue trigger

Explanation:

Using multiple function instances with peek-lock and a queue trigger allows Azure Functions to scale automatically based on the number of messages in the queue. Peek-lock ensures messages are temporarily locked while being processed, so if a function instance fails, the message becomes available for another instance. This guarantees at least one delivery and supports high-volume workloads efficiently. Azure Functions’ queue trigger automatically distributes messages among instances, ensuring both scalability and reliability.

A single function instance with ReceiveAndDelete mode does not scale horizontally. Messages are removed immediately upon reading, and failures can cause message loss, making it unsuitable for high-volume, reliable processing.

Polling the queue manually with a timer trigger is inefficient for high-volume scenarios. It introduces latency, does not scale automatically, and increases operational complexity because you need to manage concurrency and retries manually.

Processing messages directly without a trigger requires custom orchestration and does not leverage serverless scaling, which can result in missed messages, uneven load distribution, and higher operational overhead.

When designing an Azure Function to process messages from a queue, the architecture chosen has a direct impact on throughput, reliability, and data consistency. Each of the four options listed has different implications for handling messages, error recovery, and scalability.

Option A – Use a single function instance with the ReceiveAndDelete mode is not recommended for production workloads. In ReceiveAndDelete mode, messages are immediately removed from the queue as soon as they are read by the function. While this can improve performance slightly because there is no need to explicitly complete messages, it comes at a high risk. If the function fails or encounters a transient error after receiving the message but before processing it, that message is lost permanently. This approach sacrifices reliability for speed and is unsuitable for scenarios where message processing must be guaranteed.

Option B – Use multiple function instances with a peek-lock and a queue trigger is the preferred and recommended pattern. Peek-lock mode allows the function to read a message and lock it temporarily, ensuring it is not removed from the queue until the function completes processing and explicitly completes the message. If processing fails or a transient error occurs, the message becomes visible again after the lock expires and can be retried, providing at-least-once delivery semantics. Using multiple function instances allows parallel processing across multiple messages, increasing throughput while maintaining reliability. This approach is scalable, fault-tolerant, and integrates seamlessly with Azure Storage Queues or Service Bus Queues.

Option C – Poll the queue manually with a timer trigger is a less efficient approach. Here, a timer triggers the function periodically to check the queue for new messages. While this can work in some scenarios, it introduces latency because the function only checks at fixed intervals. It also increases operational complexity because developers must implement logic to handle message retrieval, processing, retries, and failures manually. This approach does not leverage the built-in capabilities of queue triggers in Azure Functions, which handle scaling, checkpointing, and retrying automatically.

Option D – Process messages directly without a trigger is not a recommended practice. Without a trigger, there is no automated or event-driven mechanism to respond to messages, requiring external scheduling or polling. This increases development effort, introduces potential timing issues, and does not provide the benefits of Azure Functions’ serverless execution model. In addition, error handling and retries would have to be implemented manually, making the system more error-prone and harder to maintain.

Question 27:

You are developing an Azure Logic App that receives files uploaded to Azure Blob Storage. You need the workflow to trigger immediately and process each new file only once. Which configuration should you use?

A) Recurrence trigger with polling every 5 minutes
B) HTTP trigger called by an external service
C) Blob Storage trigger “When a blob is created”
D) Service Bus queue trigger

Answer: C) Blob Storage trigger “When a blob is created”

Explanation:

The Blob Storage trigger “When a blob is created” allows the Logic App to respond instantly when new files are uploaded. This is a push-based mechanism, which ensures low latency and processes each file only once. The trigger supports concurrency controls and prevents duplicate executions for the same blob, which is essential for workflows that depend on file uniqueness.

Using a recurrence trigger with polling introduces latency and may execute unnecessarily when no new files are present. It also increases costs because Logic Apps run even when there is no work to do.

An HTTP trigger relies on an external service to call the workflow. While this works for event-driven scenarios, you must manage the external caller, which can complicate the architecture. It does not natively integrate with Blob Storage events.

A Service Bus queue trigger listens for messages in a queue, not for blob creation events. Using a queue would require additional logic to push blob notifications into the queue, adding complexity and overhead to the workflow.

Question 28:

You are developing an Azure App Service API that retrieves frequently accessed product data from Azure Cosmos DB. You want to reduce read latency and minimize RU consumption. Which feature should you enable?

A) Automatic indexing
B) Multi-region writes
C) TTL (Time-to-Live)
D) Integrated Cosmos DB caching

Answer: D) Integrated Cosmos DB caching

Explanation:

Integrated Cosmos DB caching allows frequently accessed data to be stored in memory, reducing direct queries to Cosmos DB. This lowers RU consumption and provides millisecond-level response times for read-heavy workloads. The cache can be configured with expiration policies, ensuring stale data is refreshed automatically. This approach is cost-effective, scalable, and aligns with best practices for serverless and high-performance applications.

Automatic indexing improves query efficiency by creating indexes on documents automatically. While it enhances performance for complex queries, it does not reduce the number of read requests or RU usage for frequently accessed data.

Multi-region writes allow low-latency writes globally and improve availability, but they increase costs because each region consumes RUs for writes. This feature does not directly optimize read latency or RU consumption for frequently accessed items in a single region.

TTL (Time-to-Live) automatically deletes documents after a specified duration. While useful for ephemeral data, TTL does not cache frequently accessed data or reduce latency. In fact, frequently accessed items may expire if TTL is misconfigured, potentially increasing read operations.

Question 29:

You are building an Azure Function that processes messages from Event Hubs. You want high throughput and parallel processing while maintaining message order per device. Which design should you choose?

A) Single partition with one consumer
B) Multiple partitions without mapping consumers
C) Multiple partitions with one consumer per partition
D) Batch processing ignoring partitions

Answer: C) Multiple partitions with one consumer per partition

Explanation:

Multiple partitions with one consumer per partition allow parallel processing of events across partitions while maintaining message order within each partition. Each consumer independently handles its assigned partition, enabling high throughput and scalability. Checkpointing ensures at-least-once delivery even in case of failures, making this a fault-tolerant and reliable architecture suitable for IoT or telemetry workloads.

Using a single partition with one consumer limits throughput, as only one stream of messages is processed sequentially. This becomes a bottleneck for high-volume workloads and increases latency.

Multiple partitions without mapping consumers can break message order because multiple consumers may compete for the same messages, leading to out-of-order processing and inconsistent results.

Batch processing, ignoring partitions, can improve throughput, but it cannot guarantee order per device. Events from the same device could be processed out of sequence, violating business requirements for ordered processing.

Question 30:

You are developing an Azure API Management (APIM) instance for internal APIs. You want to enforce authentication for internal users and track all requests for auditing purposes. Which configuration should you implement?

A) Anonymous access with logging
B) Basic authentication with local logs
C) IP restrictions only
D) OAuth 2.0 with Azure AD and diagnostic logging

Answer: D) OAuth 2.0 with Azure AD and diagnostic logging

Explanation:

Using OAuth 2.0 with Azure AD ensures that only authenticated internal users or applications can access APIs. Diagnostic logging in APIM captures requests, responses, and headers, which can be forwarded to Log Analytics, Event Hubs, or Storage for auditing. This provides both security and compliance, making it suitable for enterprise-grade API management scenarios and aligning with AZ-204 exam objectives.

Anonymous access with logging allows you to capture requests, but does not restrict access. Any user can call the APIs, which is unsuitable for internal-only scenarios.

Basic authentication with local logs provides credential-based access but lacks centralized identity management, making it harder to enforce policies, rotate credentials, and maintain audit compliance at scale.

IP restrictions block requests from unapproved networks but do not verify the identity of individual users. Users within allowed networks could still access sensitive APIs without authentication, so this approach does not meet enterprise security requirements.

Question 31:

You are developing an Azure Function that triggers when messages are added to a Service Bus Topic. The function must process messages reliably and handle message duplication. Which approach should you use?

A) Use ReceiveAndDelete mode with a single consumer
B) Use Peek-lock mode with duplicate detection enabled
C) Ignore message locks and rely on queue visibility timeout
D) Use multiple consumers with the ReceiveAndDelete mode

Answer: B) Use Peek-lock mode with duplicate detection enabled

Explanation

Peek-lock mode with duplicate detection ensures that each message is temporarily locked while being processed. If the function completes successfully, the message is removed. If processing fails, the message lock expires, and it becomes available again. Duplicate detection prevents the same message from being processed multiple times if it is sent more than once. This combination guarantees at least one delivery, reduces duplicate processing, and aligns with AZ-204 serverless messaging best practices.

ReceiveAndDelete mode with a single consumer immediately removes messages from the queue. If a failure occurs during processing, messages are lost. While simple, it does not meet requirements for reliable delivery or duplicate handling.

Ignoring message locks and relying on queue visibility timeout can lead to unpredictable results. Messages may be reprocessed multiple times or skipped if the consumer crashes or is delayed. This approach lacks control and is unsuitable for production workloads requiring reliability.

Using multiple consumers with the ReceiveAndDelete mode allows parallel processing, but each consumer immediately removes messages, creating a high risk of message loss and inconsistent processing. It does not provide duplicate protection or fault tolerance.

Question 32:

You are designing an Azure Logic App to integrate multiple systems. Some connectors may fail occasionally. You want the workflow to retry actions automatically without manual intervention. Which configuration should you use?

A) Recurrence trigger only
B) HTTP trigger without retry
C) Built-in retry policies and run-after configuration
D) Manual retry logic in each step

Answer: C) Built-in retry policies and run-after configuration

Explanation:

Built-in retry policies allow Logic Apps to automatically retry actions that fail due to transient issues, such as network glitches or service throttling. You can configure maximum retry attempts, intervals, and error types. The run-after configuration ensures that subsequent steps, like sending notifications or compensating actions, execute based on the outcome of previous steps. This approach provides robustness, maintainability, and scalability in complex workflows.

A recurrence trigger only executes the workflow on a schedule. While suitable for periodic tasks, it does not handle transient failures or provide immediate reaction to events, making it less reliable for real-time integration.

HTTP triggers can start workflows when an external service calls them. Without retries, any transient failure in downstream steps can cause workflow interruption. Additional custom logic would be needed to recover, increasing complexity.

Manual retry logic in each step requires developers to handle errors and implement custom retry policies for every action. This approach is more error-prone and difficult to maintain, especially as the number of actions and workflows grows.

Question 33:

You are developing an Azure App Service API that reads data from Azure Cosmos DB. You want to reduce request latency and minimize RU consumption for frequently accessed documents. Which feature should you enable?

A) Automatic indexing
B) Multi-region writes
C) TTL (Time-to-Live)
D) Integrated Cosmos DB caching

Answer: D) Integrated Cosmos DB caching

Explanation:

Integrated Cosmos DB caching stores frequently accessed items in memory, allowing milliseconds-level response times. This reduces the number of direct requests to Cosmos DB, minimizing RU consumption and improving cost-efficiency. Cached items can have expiration policies to keep data fresh. This approach is ideal for read-heavy workloads and aligns with cloud-native design patterns for high-performance applications.

Automatic indexing improves query efficiency by creating indexes on documents. While it can reduce query latency, it does not reduce RU consumption for repeated reads of the same items.

Multi-region writes enable low-latency writes across regions and improve global availability. However, this feature does not optimize read performance for a single region and increases costs due to replication.

TTL automatically deletes documents after a configured period. This is useful for temporary data, but does not cache frequently accessed items or improve read latency. TTL does not prevent repeated read operations from consuming RUs.

Question 34:

You are building an Azure Function that reads messages from Event Hubs. You need high throughput and parallel processing, while ensuring order is preserved per partition. Which design should you implement?

A) Single partition with one consumer
B) Multiple partitions without consumer mapping
C) Multiple partitions with one consumer per partition
D) Batch processing ignoring partitions

Answer: C) Multiple partitions with one consumer per partition

Explanation:

Multiple partitions with a dedicated consumer per partition allow the system to process events in parallel, maximizing throughput while maintaining message order within each partition. Checkpointing ensures at-least-once delivery, even if a consumer fails. This approach is fault-tolerant, scalable, and suitable for telemetry and IoT workloads, aligning with AZ-204 exam recommendations.

Using a single partition with one consumer limits throughput because only one stream of messages is processed sequentially. It creates a bottleneck and can lead to high latency for large data volumes.

Multiple partitions without mapping consumers can result in unordered message processing. Consumers may compete for messages, leading to inconsistent ordering and potential data integrity issues.

Batch processing without considering partitions may improve throughput, but cannot guarantee ordering per device or partition. Messages might be processed out of sequence, which can violate business or application requirements.

Question 35:

You are designing an Azure API Management (APIM) instance to expose internal APIs. You want to restrict access to authenticated users and capture all requests for auditing. Which approach should you use?

A) Anonymous access with logging
B) Basic authentication with local logs
C) IP restrictions only
D) OAuth 2.0 with Azure AD and diagnostic logging

Answer: D) OAuth 2.0 with Azure AD and diagnostic logging

Explanation:

OAuth 2.0 with Azure AD enforces identity-based access control, ensuring only authenticated internal users can access APIs. Diagnostic logging captures requests, responses, headers, and metadata. Logs can be sent to Log Analytics, Event Hubs, or Storage for auditing and compliance purposes. This configuration provides security, compliance, and scalability, meeting AZ-204 exam requirements.

Anonymous access with logging does not restrict API usage. Anyone can call APIs, leaving internal data exposed. Logging alone only captures activity without enforcing security.

Basic authentication with local logs provides credential-based access but lacks centralized identity management. Managing credentials and auditing is more complex, especially for enterprise-scale APIs.

IP restrictions limit access by network, but do not validate user identity. Users within allowed IPs can still access APIs without proper authentication, making it unsuitable for secure internal access.

Question 36:

You are developing an Azure Function that processes telemetry events from IoT devices via Event Hubs. You want to ensure message ordering per device while maximizing throughput. Which design should you implement?

A) Single partition with one consumer
B) Multiple partitions without dedicated consumers
C) Multiple partitions with one consumer per partition
D) Batch processing ignoring partitions

Answer: C) Multiple partitions with one consumer per partition

Explanation:

Using multiple partitions with one consumer per partition allows each partition to be processed independently, enabling parallel processing while maintaining message order within each partition. Checkpointing ensures at-least-once delivery, and retry policies handle transient failures. This design is scalable, fault-tolerant, and suitable for IoT telemetry ingestion where message sequence is critical.

A single partition with one consumer limits throughput because all messages are processed sequentially. While it guarantees order, it creates a bottleneck in high-volume scenarios.

Multiple partitions without dedicated consumers may result in out-of-order processing, as multiple consumers could compete for messages in a partition, violating device-specific ordering.

Batch processing without considering partitions can improve throughput, but message order cannot be guaranteed. Events from the same device could be processed out of sequence, which can disrupt downstream analytics or application logic.

Question 37:

You are designing an Azure App Service API that stores sensitive user information in Azure SQL Database. You want to encrypt data at rest, allow automatic key rotation, and minimize downtime. Which solution should you implement?

A) Enable TDE with Azure Key Vault-managed keys
B) Column-level encryption with manual key rotation
C) IP firewall rules only
D) Encrypt data in the application layer

Answer: A) TDE with Azure Key Vault-managed keys

Explanation:

Transparent Data Encryption (TDE) with Azure Key Vault-managed keys encrypts the entire database at rest. Key Vault allows centralized key management and automatic rotation without downtime. Azure SQL handles re-encryption seamlessly, ensuring compliance and security while minimizing operational overhead. This approach aligns with best practices for enterprise-grade applications and is exam-relevant for AZ-204.

Column-level encryption provides field-level protection, but key rotation must be managed manually. Implementing this across a large database can be error-prone and may require downtime, increasing operational complexity.

IP firewall rules restrict access to authorized networks but do not encrypt data. They provide an additional layer of security but cannot meet encryption or compliance requirements by themselves.

Encrypting data at the application layer ensures sensitive data is encrypted before it reaches the database. While effective in some scenarios, key management becomes the developer’s responsibility. It increases complexity, makes key rotation harder, and lacks seamless integration with Azure security features.

Question 38:

You are developing a Logic App to automate order processing. Some connectors occasionally fail due to transient errors. You want automatic retries and error notifications when failures persist. Which configuration should you choose?

A) Recurrence trigger only
B) HTTP trigger without retry
C) Built-in retry policies and run-after configuration
D) Manual retry logic in each step

Answer: C) Built-in retry policies and run-after configuration

Explanation:

Built-in retry policies allow Logic Apps to automatically retry failed actions, handling transient errors such as network issues or service throttling. Run-after configuration enables subsequent actions, like notifications or compensating operations, to execute if an action fails persistently. This provides resilient, maintainable, and scalable workflows, fully aligned with AZ-204 exam objectives.

Recurrence triggers only execute workflows on a schedule. While useful for periodic tasks, they do not handle event-driven scenarios or transient failures effectively, leading to delayed or failed processing.

HTTP triggers allow workflows to start via an external call. Without retry policies, transient errors could interrupt the workflow, requiring additional custom error handling and increasing complexity.

Manual retry logic in each action increases developer workload, is prone to errors, and is harder to maintain as workflows grow. Using built-in capabilities simplifies error handling and ensures consistency across the workflow.

Question 39:

You are building an Azure Function that reads messages from Azure Storage Queues. You need automatic retries and fault tolerance for transient errors. Which approach should you implement?

A) Retry policies with exponential backoff
B) Ignore failures and let the function fail
C) Static sleep loops for retries
D) Rely only on queue visibility timeouts

Answer: A) Retry policies with exponential backoff

Explanation:

Retry policies with exponential backoff are designed to handle transient failures efficiently. The delay between retries increases exponentially, preventing the function from overwhelming the queue or service during repeated errors. This ensures at-least-once delivery and fault tolerance while maintaining performance and scalability.

Ignoring failures allows transient errors to cause message loss, reducing reliability. It is not suitable for production scenarios where consistent processing is required.

Static sleep loops retry at fixed intervals, which can be inefficient under load. They do not adapt to repeated failures, which can lead to resource exhaustion or throttling issues.

Relying only on queue visibility timeouts provides a basic mechanism to reprocess messages after lock expiration, but it does not offer controlled retry intervals, logging, or adaptive handling for transient failures. Retry policies give fine-grained control and reliability that queues alone cannot provide.

Question 40:

You are designing an Azure API Management (APIM) instance for internal APIs. You need to restrict access to internal users and capture request data for auditing. Which configuration should you use?

A) Anonymous access with logging
B) Basic authentication with local logs
C) IP restrictions only
D) OAuth 2.0 with Azure AD and diagnostic logging

Answer: D) OAuth 2.0 with Azure AD and diagnostic logging

Explanation:

OAuth 2.0 with Azure AD ensures that only authenticated internal users or applications can access the APIs. Diagnostic logging captures request and response data, headers, and metadata, which can be stored in Log Analytics, Event Hubs, or Storage for auditing and compliance. This setup provides secure, auditable, and scalable API management suitable for enterprise scenarios and aligns with AZ-204 exam requirements.

Anonymous access with logging captures requests but does not restrict access. APIs remain exposed, and logging only records activity without enforcing security.

Basic authentication with local logs restricts access using credentials, but lacks centralized identity management and makes auditing harder. Credential rotation and compliance tracking are more complex.

IP restrictions block requests from unapproved networks but do not verify individual user identities. Users within allowed IPs could still access APIs without authentication, making it unsuitable for internal-only access control.