Microsoft AZ-204 Developing Solutions for Microsoft Azure Exam Dumps and Practice Test Questions Set 3 Q41-60

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Question 41:

You are developing an Azure Function that processes events from Azure Event Hubs. The function must scale automatically and handle high-volume events while maintaining event order per device. 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 enables parallel processing of event streams while preserving the order of events within each partition. Each consumer processes its assigned partition independently, and checkpointing ensures at-least-once delivery. Retry policies help handle transient errors. This architecture is highly scalable and fault-tolerant, suitable for telemetry ingestion or IoT workloads where event order is critical.

A single partition with one consumer guarantees order but limits throughput. It becomes a bottleneck in high-volume scenarios, increasing latency and potentially causing event processing delays.

Multiple partitions without dedicated consumers can result in unordered processing, as consumers may pick up events from multiple partitions, breaking sequence guarantees.

Batch processing, ignoring partitions, improves throughput but cannot maintain device-specific order. Events from the same device may be processed out of order, disrupting downstream logic. When designing a system that processes messages from Azure Event Hubs, understanding the relationship between partitions and consumers is critical for achieving high throughput, low latency, and ordered message processing. Each option listed has important implications for system performance, reliability, and data consistency.

Option A – Single partition with one consumer is the simplest approach, but severely limits throughput. Event Hubs partitions act as independent, ordered streams of events. If only a single partition is used with a single consumer, all messages are processed sequentially by one function instance. While this guarantees ordering within the stream, it creates a bottleneck for high-volume event workloads. If thousands of IoT devices are sending telemetry data simultaneously, a single partition and consumer cannot process events quickly enough, resulting in increased latency and delayed processing. This approach is only suitable for very low-throughput scenarios where ordering is the primary concern.

Option B – Multiple partitions without dedicated consumers allows messages to be distributed across multiple partitions, but if consumers are not assigned per partition, ordering is not guaranteed, and processing can become unpredictable. Azure Event Hubs requires that a consumer read events from a partition to maintain ordering. Without dedicated consumers per partition, multiple function instances could attempt to read from the same partition or overlap in processing, causing race conditions, potential duplicate processing, and out-of-order execution. This approach sacrifices the very guarantees that partitions provide.

Option C – Multiple partitions with one consumer per partition is the recommended approach for high-throughput and fault-tolerant processing. Each partition is assigned a dedicated consumer, ensuring that messages within a partition are processed in order while allowing parallelism across partitions. This model takes full advantage of Event Hubs’ scalable design. By distributing partitions across multiple function instances, throughput is maximized while maintaining ordering within each stream. Additionally, Azure Functions supports checkpointing per partition, allowing the system to recover from failures without losing messages or reprocessing already handled events. Scaling out consumers in proportion to partition count ensures efficient utilization of resources and enables low-latency, high-volume processing in real-time streaming scenarios such as telemetry ingestion or financial transaction processing.

Option D – Batch processing, ignoring partition, is generally not recommended. While batching may reduce overhead by processing multiple events at once, ignoring partition boundaries can lead to inconsistent ordering, race conditions, and reduced reliability. Event Hubs’ partitions exist to maintain ordering guarantees, and bypassing this mechanism undermines the fundamental design principles of the service. Additionally, batch processing without respecting partitions complicates checkpointing and failure recovery, increasing the risk of duplicate processing or data loss.

For scenarios requiring both high throughput and ordered message processing, the best practice is to use multiple partitions with one consumer per partition. This approach provides parallelism, maintains ordering within partitions, and supports fault-tolerant, scalable, and low-latency processing. Other approaches either limit throughput, compromise ordering, or increase the risk of processing failures and operational complexity.

Question 42:

You are designing an Azure App Service API that stores sensitive customer data in Azure SQL Database. You need encryption at rest, automatic key rotation, and minimal 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:

TDE with Azure Key Vault-managed keys encrypts the entire database at rest and integrates with Key Vault for centralized key management. Automatic key rotation can be performed without downtime, and Azure SQL handles re-encryption transparently. This ensures compliance with standards such as PCI DSS, GDPR, and HIPAA while minimizing operational overhead.

Column-level encryption provides field-level encryption but requires manual key management and rotation. Scaling this approach for large datasets can be error-prone and may require downtime during rotation.

IP firewall rules restrict network access but do not provide encryption or key rotation capabilities. They are an additional security layer, but do not meet encryption requirements.

Encrypting data in the application layer ensures sensitive data is encrypted before reaching the database. This shifts key management to developers, making rotation more complex and increasing operational risk compared to Azure-managed TDE.

Question 43:

You are building an Azure Logic App that integrates multiple systems. Some connectors fail occasionally due to transient errors. You want automatic retries and error notifications. 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 automatic retries when actions fail due to transient errors like network issues or throttling. The run-after configuration lets you execute subsequent steps, such as notifications or compensating actions, if an action fails repeatedly. This approach provides resilient, maintainable, and scalable workflows, adhering to AZ-204 integration best practices.

Recurrence triggers execute workflows on a schedule and are not event-driven. They cannot handle transient failures effectively, leading to delayed processing or unnecessary runs.

HTTP triggers start workflows via external calls but do not handle downstream transient errors automatically. Without retry logic, failed steps may disrupt the workflow, requiring additional custom handling.

When designing Azure Logic Apps workflows, ensuring reliable execution in the presence of transient failures is critical for production-ready automation. Many workflows involve multiple actions that interact with external systems, APIs, or services. These external dependencies are often prone to transient issues such as network interruptions, temporary unavailability, throttling, or brief service outages. If these failures are not handled correctly, the workflow can fail mid-execution, resulting in incomplete processing, inconsistent states, or the need for manual intervention. Therefore, implementing a robust strategy for retrying failed actions is essential to maintain workflow reliability and operational efficiency.

Option C, which uses built-in retry policies combined with run-after configuration, is the recommended approach because it leverages native capabilities of Azure Logic Apps to provide automated, configurable retries for actions that fail due to transient issues. Retry policies allow developers to specify key parameters such as the maximum number of retry attempts, the interval between retries, and the backoff strategy, whether fixed or exponential. Exponential backoff gradually increases the interval between retries after each failure, which is especially useful when downstream services are temporarily overloaded, as it reduces pressure on the system and avoids creating a thundering herd problem. Run-after conditions allow subsequent actions in the workflow to execute based on the outcome of previous actions—whether they succeeded, failed, were skipped, or timed out. Together, these mechanisms provide comprehensive error-handling capabilities without the need to write complex custom logic.

Option A, using a recurrence trigger only, is insufficient for handling failures within the workflow. A recurrence trigger schedules the workflow to execute at regular intervals but does not automatically handle failures of individual actions. If a failure occurs in a downstream step, the workflow cannot recover automatically, and the failed action must be retried manually or the entire workflow rerun. This introduces delays, increases operational overhead, and reduces overall reliability, particularly in workflows that require real-time or near-real-time processing.

Option B, using an HTTP trigger without retry, allows the workflow to be invoked on demand but does not inherently provide mechanisms for handling action-level failures. Any transient failure in a downstream action requires manual error handling, which can be inconsistent and error-prone. Moreover, without retries, workflows triggered by external systems may experience partial processing, leading to incomplete data updates or inconsistent integration states between systems.

Option D, implementing manual retry logic in each step, is technically feasible but introduces unnecessary complexity and maintenance challenges. Each action must include its own retry loops, condition checks, and error-handling logic. This approach increases workflow complexity, reduces readability, and can lead to human errors during workflow modifications or scaling. It also makes monitoring and debugging more difficult, as retries and failures are spread across multiple manual constructs instead of being centrally managed.

In summary, using built-in retry policies and run-after configuration is the most efficient and reliable approach to handling transient failures in Azure Logic Apps. This approach ensures that workflows are fault-tolerant, reduces operational burden, maintains consistency, and leverages the platform’s native error-handling capabilities. By relying on this approach, organizations can design scalable, maintainable, and resilient serverless workflows that meet production reliability standards.

Manual retry logic increases developer overhead, is error-prone, and harder to maintain across multiple actions. Built-in retries simplify workflow management and improve reliability.

Question 44:

You are developing an Azure Function that processes messages from Azure Storage Queues. You need automatic retries and fault tolerance. 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 allow functions to handle transient errors efficiently. The delay between retries increases exponentially, preventing the function from overwhelming the queue or service. This guarantees at least once delivery, ensures fault tolerance, and aligns with best practices for serverless message processing.

Ignoring failures causes message loss when transient errors occur, reducing reliability and consistency in processing. When designing Azure Functions that process messages from queues such as Azure Storage Queues or Service Bus Queues, implementing an effective retry strategy is critical to ensure reliability, data consistency, and fault tolerance. Messages processed by functions can fail due to transient issues like temporary network outages, downstream service unavailability, throttling limits, or momentary configuration problems. Without a proper retry mechanism, such failures can result in lost messages, inconsistent processing, and a failure to meet service-level expectations.

Option A, implementing retry policies with exponential backoff, is the most effective and widely recommended approach. Retry policies allow the function to automatically attempt reprocessing of failed messages according to configurable parameters, including the maximum number of retry attempts and the delay strategy between retries. Exponential backoff increases the delay after each failure attempt, for example, retrying after one second, then two seconds, then four seconds, and so on. This approach helps prevent overwhelming downstream services, which might already be experiencing temporary issues, while ensuring that messages are eventually processed successfully. When combined with dead-letter queues, messages that fail after the maximum number of retries are captured for later analysis or reprocessing, which further ensures reliability and operational safety.

Option B, ignoring failures and allowing the function to fail, is highly unreliable. In this scenario, any transient failure causes the message to be lost or remain unprocessed unless the queue itself automatically re-enqueues it. Even with automatic re-enqueueing, the lack of controlled retries means that message processing timing is unpredictable, and repeated failures can cause workflow bottlenecks or missed data. Ignoring failures also increases operational risk, as administrators may not be aware of undelivered messages until downstream systems report inconsistencies.

Option C, implementing static sleep loops for retries, is technically possible but inefficient. A static loop might retry a message after a fixed interval, such as every five seconds, regardless of downstream system conditions. Fixed delays do not adapt to the severity of transient failures or current system load, potentially leading to unnecessary retries, resource wastage, and longer processing times. Additionally, managing retry counts and error conditions manually in code adds complexity, increases the likelihood of implementation errors, and makes scaling the solution more difficult.

Option D, relying only on queue visibility timeouts, is limited in effectiveness. Visibility timeouts ensure that a message remains invisible to other consumers while being processed and becomes available again if the function does not complete processing within the timeout period. However, visibility timeouts alone do not provide adaptive retry intervals, exponential backoff, or control over maximum retry attempts. They also do not differentiate between transient and persistent failures, potentially leading to repeated immediate retries that do not allow the system to recover from temporary issues.

In conclusion, implementing retry policies with exponential backoff (Option A) is the most robust and maintainable approach for Azure Functions consuming messages from queues. This strategy ensures high reliability, controlled retry behavior, and fault-tolerant processing while minimizing the risk of overwhelming downstream services. It also integrates seamlessly with Azure Functions’ native features, such as dead-letter queues and logging, providing a scalable, production-ready solution. Other approaches either compromise reliability, increase complexity, or fail to fully leverage the platform’s capabilities.

Static sleep loops retry at fixed intervals, which can be inefficient under load and may result in resource throttling or delayed processing.

Relying solely on queue visibility timeouts provides limited recovery. Messages may be retried automatically after lock expiration, but there is no adaptive control, logging, or configurable retry interval, making it less reliable than exponential backoff.

Question 45:

You are designing an Azure API Management (APIM) instance for internal APIs. You want to restrict access to authenticated users and capture all request data 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 request and response data, headers, and metadata, which can be stored in Log Analytics, Event Hubs, or Storage for auditing and compliance. This configuration is secure, auditable, and scalable, meeting AZ-204 exam requirements.

Anonymous access with logging captures requests but does not restrict access, leaving APIs exposed. Logging alone records activity but does not provide security.

Basic authentication with local logs provides credentials-based access, but lacks centralized identity management, making auditing and credential rotation more complex.

IP restrictions limit network access but do not verify individual user identity. Users within allowed IP ranges can still access APIs without authentication, making this insufficient for internal-only secure access. When developing an API in Azure that exposes sensitive financial data, security and auditing are paramount. Protecting sensitive data requires ensuring that only authorized and authenticated applications or users can access the API. Additionally, detailed logging of requests is essential for auditing, compliance, and troubleshooting purposes. Choosing the correct security and logging mechanism is therefore critical.

Option D, using OAuth 2.0 with Azure Active Directory (Azure AD) and diagnostic logging, is the recommended solution. OAuth 2.0 is a robust, industry-standard protocol for authorization. It allows applications to securely obtain access tokens that prove their identity and permissions without sharing credentials directly. By integrating OAuth 2.0 with Azure AD, organizations can leverage a centralized identity provider to manage user and application identities, enforce policies, and control access. This integration ensures that only applications or users that are registered and authorized within Azure AD can successfully call the API, mitigating the risk of unauthorized access. Azure AD also provides capabilities such as token expiration, scope-based permissions, and conditional access, which further strengthen security by enforcing granular, context-aware access control.

Diagnostic logging complements OAuth 2.0 by capturing detailed information about every request made to the API. This includes metadata such as timestamps, request headers, payload details, responses, and the identity of the caller. Logging all API interactions enables organizations to perform thorough audits, detect suspicious activity, and maintain compliance with regulatory requirements such as GDPR, PCI DSS, or SOX. In Azure, diagnostic logs can be integrated with services such as Azure Monitor, Log Analytics, and Event Hubs, allowing for centralized monitoring, long-term retention, and alerting based on anomalies or failed authentication attempts.

Other options are less suitable for protecting sensitive financial APIs. Option A, anonymous access with logging, is highly insecure. While logging records of who accesses the API, it does not prevent unauthorized users from making requests in the first place, leaving the API exposed to potential abuse, data leaks, or malicious activity. Option B, basic authentication with local logs, provides minimal security because credentials are sent with each request and must be managed manually. Storing logs locally is also not reliable for long-term auditing, compliance, or centralized monitoring, and credentials could be compromised if servers are misconfigured or attacked. Option C, IP restrictions only, can limit access by network location, but it does not authenticate the caller. Attackers from allowed IP ranges could still access the API, and IP restrictions do not provide any identity-based access control or detailed logging.

In summary, combining OAuth 2.0 with Azure AD and diagnostic logging provides a secure, scalable, and auditable approach to protecting sensitive financial APIs. OAuth 2.0 ensures that only authenticated and authorized applications or users can access the API, while diagnostic logging captures comprehensive request information for auditing and compliance. This combination addresses both security and governance requirements, reduces operational risk, and leverages Azure’s built-in identity and monitoring capabilities, making it the most suitable choice for enterprise-grade, sensitive API deployments.

Question 46:

You are developing an Azure Function that processes messages from Azure Service Bus Queues. You need to ensure reliable message processing and avoid duplicates. Which approach should you use?

A) ReceiveAndDelete mode with a single consumer
B) Peek-lock mode with duplicate detection enabled
C) Ignore message locks and retry manually
D) Multiple consumers with the ReceiveAndDelete mode

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

Explanation:

Peek-lock mode temporarily locks messages for processing. The message is only removed after successful processing, allowing the system to handle transient errors. Duplicate detection prevents processing the same message multiple times, ensuring at-least-once delivery with minimal duplication. This approach is recommended for high-reliability message-driven applications and aligns with AZ-204 serverless messaging best practices.

ReceiveAndDelete mode removes messages immediately upon receipt. If a failure occurs during processing, the message is lost, which reduces reliability. It is suitable only for low-criticality workloads where occasional message loss is acceptable.

Ignoring message locks and retrying manually can cause duplicate processing or missed messages if the function crashes or retries are mishandled. This approach adds complexity and reduces fault tolerance.

Using multiple consumers with the ReceiveAndDelete mode allows parallel processing, but messages are deleted immediately, increasing the risk of lost messages and inconsistent processing. It does not provide duplication prevention or checkpointing.

Question 47:

You are building an Azure Logic App to automate invoice approvals. Some connectors may fail due to temporary issues. You want automatic retries and error notifications. Which configuration should you implement?

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

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

Explanation:

Built-in retry policies in Logic Apps automatically handle transient failures such as network interruptions or throttling. Configuring run-after allows subsequent actions like notifications or compensating operations to execute when persistent failures occur. This creates resilient, maintainable workflows suitable for enterprise automation.

Recurrence triggers execute workflows on a schedule. They do not respond immediately to events and cannot handle transient connector failures automatically, reducing workflow reliability. In Azure Logic Apps, designing workflows that interact with external systems or services requires careful attention to reliability and error handling. Many external dependencies, such as REST APIs, databases, or storage services, can experience transient failures like network timeouts, temporary unavailability, throttling, or short-lived outages. If these failures are not properly handled, workflow actions may fail, leading to incomplete processing, inconsistent data, or the need for manual intervention. To address this, Logic Apps provides native mechanisms for retrying failed actions, which are more maintainable and robust than implementing custom retry logic manually.

Option C, using built-in retry policies and run-after configuration, is the recommended approach for handling transient failures. Built-in retry policies allow developers to configure automatic retries for actions that fail due to transient issues. These policies include parameters such as maximum retry attempts, delay between retries, and backoff strategy. Exponential backoff, for example, increases the delay between retry attempts progressively, which prevents overwhelming downstream services during temporary outages and allows systems to recover gracefully. Run-after configuration complements retry policies by controlling the execution of subsequent actions based on the outcome of previous steps. Developers can specify whether an action should execute after a previous action succeeds, fails, is skipped, or times out. This ensures that workflows can respond dynamically to errors without requiring complex manual error handling. By combining retry policies and run-after conditions, Logic Apps workflows can achieve fault-tolerant execution while remaining clean, readable, and maintainable.

Option A, recurrence triggers only, is insufficient for handling failures within a workflow. A recurrence trigger schedules the workflow to run at fixed intervals, but it does not provide any mechanism to handle transient failures in actions. If a workflow action fails during execution, the workflow does not automatically retry the failed step, potentially leading to missed operations or inconsistent states. Manual intervention or re-running the workflow is required, which increases operational overhead.

Option B, HTTP triggers without retry, allows workflows to be initiated by external systems on demand. However, without retry mechanisms, any transient failure in subsequent actions could cause incomplete processing. While the workflow starts successfully, downstream actions may fail silently, and manual monitoring and intervention are required to ensure all steps are completed successfully. This approach is not suitable for workflows that require reliable integration with external services.

Option D, manual retry logic for each action, can work but introduces significant complexity and maintenance challenges. Developers would need to implement loops, conditionals, and error-handling logic for each step that may fail. This approach increases workflow complexity, reduces readability, and is prone to human errors, especially when workflows evolve. It also makes scaling, monitoring, and debugging more difficult compared to using built-in features.

In conclusion, using built-in retry policies and run-after configuration is the most efficient and reliable way to handle transient failures in Azure Logic Apps. This approach ensures workflows are fault-tolerant, maintainable, and capable of recovering automatically from temporary issues, while minimizing operational overhead and maximizing reliability.

HTTP triggers require external services to invoke the workflow. Without retries, failures in downstream actions interrupt the workflow and require custom handling, increasing complexity.

Manual retry logic for each action increases development overhead and introduces potential errors. As workflows grow, maintaining consistency becomes challenging. Built-in retries simplify error handling and improve reliability.

Question 48:

You are developing an Azure App Service API that reads data from Azure Cosmos DB. You want to reduce read latency and minimize RU consumption for frequently accessed data. 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 data in memory, reducing repeated database queries. This improves response times and lowers RU consumption, optimizing cost and performance. Cached data can have configurable expiration policies to ensure freshness while serving high-throughput read-heavy workloads efficiently.

Automatic indexing optimizes query performance by indexing documents automatically. While it reduces query execution time, it does not reduce the number of read operations or RU usage for frequently accessed items.

Multi-region writes improve write availability and latency globally but do not optimize read performance in a single region. They also increase operational costs and do not directly reduce RU consumption for hot data.

TTL automatically deletes documents after a configured time, useful for temporary or expiring data. It does not cache frequently accessed items and does not improve read latency for repeated queries.

Question 49:

You are designing an Azure Function to process high-volume telemetry events from Event Hubs. You want parallel processing while maintaining message order per device. Which approach should you choose?

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 one consumer per partition allow parallel processing across partitions while ensuring that events in the same partition maintain order. Each consumer handles its assigned partition, with checkpointing ensuring at-least-once delivery. Retry policies support transient failures. This design is fault-tolerant, scalable, and suitable for telemetry or IoT workloads, matching AZ-204 best practices.

A single partition with one consumer guarantees order but limits throughput, creating a bottleneck for high-volume streams.

Multiple partitions without consumer mapping can cause out-of-order processing, as consumers may handle messages from multiple partitions, leading to inconsistent results.

Batch processing, ignoring partitions, improves throughput but sacrifices message order. Events from the same device could be processed out of sequence, potentially disrupting downstream analytics.

Question 50:

You are configuring an Azure API Management (APIM) instance for internal APIs. You want to enforce authentication and capture request logs for auditing. 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:

OAuth 2.0 with Azure AD ensures only authenticated internal users can access APIs. Diagnostic logging captures request/response headers, metadata, and bodies, which can be stored in Log Analytics, Event Hubs, or Storage for auditing and compliance. This configuration provides secure, auditable, and scalable API management, fully aligned with AZ-204 exam objectives.

Anonymous access with logging captures requests but does not restrict API usage. APIs remain exposed, and logging alone does not enforce security.

Basic authentication with local logs provides credentials-based access but lacks centralized identity management. Credential rotation and auditing are more complex in this setup, making it less suitable for enterprise environments.

IP restrictions block access from unauthorized networks but do not validate individual user identity. Users within allowed networks could still access APIs without proper authentication, which fails internal security requirements.

Question 51:

You are developing an Azure Function that processes Service Bus Topic messages. You want reliable processing and to avoid duplicate message handling. Which approach should you implement?

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

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

Explanation:

Peek-lock mode temporarily locks a message during processing. It ensures that if the function fails, the message becomes available for reprocessing. Duplicate detection prevents the same message from being processed multiple times, maintaining at-least-once delivery with minimal duplication. This approach is suitable for critical workloads that require reliability and fault tolerance.

ReceiveAndDelete mode immediately removes messages, which risks message loss if a processing failure occurs. It is only suitable for low-criticality or idempotent workloads.

Ignoring message locks and relying on visibility timeouts can result in multiple consumers processing the same message or messages being lost. This approach lacks fine-grained control and reliability.

Using multiple consumers with the ReceiveAndDelete mode allows parallel processing but removes messages immediately, increasing the chance of message loss and inconsistent processing. Duplicate detection is not available in this setup.

Question 52:

You are designing an Azure Logic App to handle customer orders. Some connectors may fail occasionally. You want automatic retries and notification on persistent failures. Which configuration should you implement?

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

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

Explanation:

Built-in retry policies allow automatic retries for transient failures like network or service throttling. Run-after configuration enables subsequent actions, such as sending notifications or compensating logic, if persistent failures occur. This ensures resilient, maintainable workflows that meet enterprise-grade reliability standards.

Recurrence triggers run workflows on a schedule and cannot respond immediately to events. They do not handle transient failures automatically and may delay processing.

HTTP triggers start workflows via external calls but do not provide retry mechanisms natively. Any downstream failure could interrupt the workflow, requiring additional custom handling.

Manual retry logic increases developer effort, adds potential errors, and is difficult to maintain across complex workflows. Built-in retry policies simplify management and ensure consistent behavior.

Question 53:

You are developing an Azure App Service API that reads frequently accessed data from 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 stores frequently accessed documents in memory, reducing the number of direct queries. This lowers latency and minimizes RU consumption, improving cost efficiency and performance for read-heavy workloads. Expiration policies ensure cached data is fresh.

Automatic indexing improves query performance by creating indexes, but it does not reduce repeated read requests or RU usage.

Multi-region writes enhance write availability globally but do not optimize read latency for a single region. They also increase costs due to replicated writes.

TTL deletes documents automatically after a configured duration. While useful for temporary data, TTL does not cache frequently accessed items or improve read performance.

Question 54:

You are building an Azure Function that ingests telemetry from Event Hubs. You need high throughput while maintaining event order per device. Which approach should you implement?

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 a dedicated consumer per partition allow parallel processing and preserve message order within each partition. Each consumer processes its assigned partition independently. Checkpointing ensures at-least-once delivery, and retry policies handle transient failures. This design is scalable, fault-tolerant, and suitable for telemetry or IoT workloads.

A single partition with one consumer guarantees order but limits throughput, creating a processing bottleneck.

Multiple partitions without mapping consumers may cause unordered processing, as multiple consumers could pick up messages from different partitions.

Batch processing, ignoring partitions, improves throughput but sacrifices ordering. Messages from the same device may be processed out of sequence, which can disrupt downstream applications.

Question 55:

You are configuring Azure API Management (APIM) for internal APIs. You want to enforce authentication and capture requests for auditing. 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:

OAuth 2.0 with Azure AD enforces identity-based access control, allowing only authenticated internal users to access 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 ensures secure, auditable, and scalable API management aligned with AZ-204 exam objectives.

Anonymous access with logging captures requests but does not enforce security. APIs remain exposed, and logging alone cannot prevent unauthorized access.

Basic authentication with local logs allows credential-based access but lacks centralized identity management. Auditing and key rotation are more complex, reducing maintainability.

IP restrictions block access based on network location but do not verify user identity. Users within allowed IPs could access APIs without authentication, making this insufficient for enterprise-grade internal APIs.

Question 56:

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

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

Answer: A) Retry policies with exponential backoff

Explanation:

Retry policies with exponential backoff handle transient errors efficiently. The wait time between retries increases exponentially, preventing the function from overwhelming the service. This ensures at-least-once delivery, fault tolerance, and consistent processing for high-volume workloads.

Ignoring failures can result in lost messages and reduced reliability.

Static sleep loops retry at fixed intervals but do not adapt to repeated failures, which can lead to resource inefficiency and throttling.

Relying solely on queue visibility timeouts does not provide controlled retry intervals or logging, limiting reliability and observability. Retry policies offer fine-grained control and fault-tolerant processing.

Question 57:

You are building a Logic App that triggers on new files in Azure Blob Storage. You want the workflow to trigger immediately and process each file only once. Which configuration should you use?

A) Recurrence trigger with polling
B) HTTP trigger called externally
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” is push-based. It responds immediately when a new blob is uploaded, ensuring low latency and processing each file only once. Concurrency controls prevent duplicate executions and maintain workflow consistency.

Recurrence triggers poll the storage on a schedule, introducing latency and extra cost, since workflows run even when no new files exist.

HTTP triggers rely on external systems to invoke the workflow, requiring additional orchestration. They do not natively react to blob events.

Service Bus triggers process messages in a queue, not blob uploads. Using a queue would require extra logic to send blob notifications, adding complexity.

Question 58:

You are developing an Azure App Service API that reads frequently accessed Cosmos DB data. You want low-latency reads and reduced RU consumption. Which feature should you enable?

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

Answer: D) Integrated Cosmos DB caching

Explanation:

Integrated Cosmos DB caching stores frequently accessed items in memory, reducing database queries. This improves read performance, lowers RU consumption, and decreases costs. Cached data can be refreshed automatically, ensuring data freshness while handling high-throughput workloads efficiently.

Automatic indexing improves query speed but does not reduce repeated read operations or RU consumption.

Multi-region writes optimize write availability across regions but do not improve read latency in a single region. They also increase operational costs.

TTL deletes documents automatically after a set duration. While suitable for ephemeral data, TTL does not cache frequently accessed items or optimize read performance.

Question 59:

You are building an Azure Function to process telemetry events from Event Hubs. You want parallel processing while preserving message order per device. Which approach should you implement?

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 a dedicated consumer per partition ensure parallel processing and maintain message order within each partition. Checkpointing ensures at-least-once delivery, and retry policies handle transient failures. This architecture is scalable, reliable, and suitable for telemetry or IoT workloads, matching AZ-204 best practices.

A single partition with one consumer limits throughput. All messages are processed sequentially, creating a bottleneck.

Multiple partitions without mapping consumers can result in unordered processing, as multiple consumers may compete for messages across partitions.

Batch processing, ignoring partition, improves throughput but sacrifices ordering, which can disrupt downstream analytics or business logic.

Question 60:

You are configuring an Azure API Management (APIM) instance for internal APIs. You want to enforce authentication and capture request logs 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 enforces identity-based access control, ensuring only authorized internal users can access APIs. Diagnostic logging captures request and response details, which can be stored in Log Analytics, Event Hubs, or Storage for auditing and compliance. This configuration is secure, auditable, and enterprise-ready, fully aligned with AZ-204 exam standards.

Anonymous access with logging captures requests but does not enforce security, leaving APIs exposed.

Basic authentication with local logs lacks centralized identity management. Credential rotation and auditing are more complex, reducing maintainability.

IP restrictions block unauthorized networks but do not verify user identity. Users within allowed networks could access APIs without authentication, making it insufficient for internal-only secure APIs.