Microsoft AZ-204 Developing Solutions for Microsoft Azure Exam Dumps and Practice Test Questions Set 5 Q81-100

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Question 81:

You are developing an Azure Function that processes high-volume telemetry data from Event Hubs. You need parallel processing while preserving message order per device. Which architecture 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:

Using multiple partitions with one consumer per partition is the recommended approach for high-throughput, order-sensitive workloads. Each partition can be processed independently in parallel while maintaining message order within that partition. Azure Functions supports automatic scaling, checkpointing, and retry policies, ensuring at-least-once delivery. By assigning one consumer per partition, you eliminate the risk of multiple consumers reading the same messages and causing ordering issues. This architecture is ideal for IoT telemetry, device state monitoring, or financial transactions where sequence integrity is critical. Dead-letter queues handle poisoned messages, further improving reliability.

A single partition with one consumer guarantees ordering but limits throughput because messages are processed sequentially. This becomes a bottleneck in high-volume scenarios, delaying telemetry processing and affecting downstream analytics.

Multiple partitions without mapping consumers can lead to unordered message processing. If multiple consumers read from different partitions without control, device-specific event sequences can be broken, resulting in inconsistent data for analytics or operational decision-making.

Batch processing, ignoring partitions, increases throughput but sacrifices ordering guarantees. Events from the same device could be processed out of sequence, which is unacceptable for use cases where correct sequencing is required, such as telemetry aggregation, fraud detection, or financial event processing.

Question 82:

You are building an Azure App Service API that reads frequently accessed documents from Cosmos DB. You want low-latency reads and reduced 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 data in memory, allowing millisecond-level reads. This significantly reduces repeated database queries, which lowers RU consumption and operational costs. Cached data can be refreshed automatically or on demand, ensuring data consistency and performance. It is particularly suitable for high-throughput scenarios like dashboards, telemetry processing, or product catalogs. This approach aligns with serverless best practices, as it improves scalability while reducing the cost and load on the database.

Automatic indexing improves query performance by creating indexes on all documents, which helps complex queries execute faster. However, it does not reduce RU consumption or repeated reads for hot data. Indexing optimizes query execution but does not provide caching benefits for frequently accessed items.

Multi-region writes enhance write availability and latency across regions, but they do not improve read performance in a single region. They also increase operational costs due to replication. Multi-region writes are suitable for global write-heavy workloads but not for read optimization.

TTL (Time-to-Live) deletes documents automatically after a configured duration. While useful for ephemeral data or temporary logs, TTL does not reduce RU consumption or improve read latency for frequently accessed items. It is primarily a data lifecycle management feature, not a performance optimization tool.

Question 83:

You are configuring an Azure Function to process messages from Service Bus Queues. You need reliable processing, avoid duplicate message handling, and handle transient failures. 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 implement manual retries
D) Multiple consumers with the ReceiveAndDelete mode

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

Explanation:

Peek-lock mode temporarily locks messages during processing, preventing other consumers from reading them. The message is removed only after successful completion. Duplicate detection ensures the same message is not processed multiple times within a specified window, enabling at-least-once delivery without duplication. Combined with Azure Functions’ built-in retry policies and checkpointing, this approach ensures high reliability, fault tolerance, and consistency, making it suitable for critical workloads such as financial processing, telemetry ingestion, or IoT systems. Dead-letter queues handle messages that repeatedly fail, providing a clear mechanism for troubleshooting and reprocessing.

ReceiveAndDelete mode removes messages immediately upon retrieval. While simpler, it is unsafe for critical applications, as message loss occurs if the function fails mid-processing. It is suitable only for non-critical or idempotent workloads.

Ignoring message locks and manually retrying increases complexity and the likelihood of errors. Developers must implement custom checkpointing, retry logic, and duplicate detection, which increases operational risk and maintenance overhead.

Multiple consumers with the ReceiveAndDelete mode allow parallel processing but sacrifice reliability. Messages are deleted immediately, so failures result in lost messages, and duplicate detection is not supported. This approach prioritizes throughput over correctness, making it unsuitable for enterprise-critical applications.

Question 84:

You are building a Logic App that triggers when invoices are uploaded to Azure Blob Storage. You want real-time processing and exactly-once execution. Which trigger 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 a push-based event trigger that responds immediately to new blobs. It ensures low-latency processing and prevents duplicate executions with built-in concurrency controls, ensuring exactly-once processing. It integrates seamlessly with Logic App actions, allowing error handling, retries, and run-after configurations. This makes it ideal for workflows like invoice processing, document approvals, and automated file transformations. It also aligns with serverless and event-driven design principles, which are emphasized in AZ-204 exam objectives.

Recurrence triggers poll storage at defined intervals, introducing latency and potentially processing multiple blobs at once. They also increase operational costs because workflows may run unnecessarily when no new files exist.

HTTP triggers require an external service to detect blob uploads and trigger the workflow. This adds complexity, latency, and potential points of failure.

Service Bus queue triggers respond to queued messages, not blob creation events. Using a queue requires additional logic to send a message for each blob upload, adding overhead and latency compared to a direct blob trigger.

Question 85:

You are configuring an Azure API Management (APIM) instance for internal APIs. You need authentication enforcement and auditable request logs. 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, allowing only authenticated and authorized users to access APIs. Diagnostic logging captures detailed request and response information, including headers, payloads, and metadata. Logs can be sent to Log Analytics, Event Hubs, or Storage for auditing, monitoring, and compliance. This setup ensures enterprise-grade security, token-based authentication, and role-based access control. OAuth integration also provides centralized identity management and traceable access, meeting both operational and compliance requirements, which is critical for internal APIs.

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 provides credential-based access but lacks centralized management, token-based authentication, and robust auditing. Credential rotation, access tracking, and role-based permissions are complex to manage.

IP restrictions limit access by network location but do not verify individual user identities. Users within allowed networks could still access APIs without authentication, making it insufficient for secure internal APIs.

Question 86:

You are developing an Azure Function to process messages from Azure Storage Queues. You need automatic retries, fault tolerance, and at-least-once delivery. 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 the recommended approach for handling transient failures in Azure Functions triggered by Storage Queues. When a function fails temporarily due to network issues, throttling, or service unavailability, the function retries processing after a delay that increases exponentially with each attempt. This prevents overloading resources and avoids consuming unnecessary compute or queue resources. Exponential backoff ensures retries are spaced appropriately, reducing the likelihood of repeated failures during transient outages. By configuring maximum retries, intervals, and error handling, Azure Functions guarantees at-least-once delivery and aligns with serverless best practices. Dead-letter queues handle messages that consistently fail, enabling monitoring and remediation of persistent issues.

Ignoring failures and letting the function fail is risky. Messages may be lost, leading to data inconsistency or missing telemetry. Without retries, developers must manually intervene for each failure, increasing operational overhead.

Static sleep loops retry at fixed intervals but do not adapt to the type or frequency of failures. This can result in either too-frequent retries, causing resource exhaustion, or too-slow retries, delaying message processing. Exponential backoff provides a dynamic and optimized approach for retry scheduling.

Relying only on queue visibility timeouts allows messages to reappear in the queue if not processed, but it offers no control over retry intervals or maximum attempts. This can result in inconsistent processing, duplicate message handling, or delays. It does not replace structured retry policies for production workloads.

Question 87:

You are designing a Logic App to process invoice approvals. Some connectors may fail intermittently. You want automatic retries and error notifications when failures persist. 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 in Logic Apps handle transient connector failures automatically. You can configure the number of retries, retry interval, and retry type (fixed or exponential), reducing the need for custom error handling. Run-after configurations allow actions to execute based on the success, failure, or timeout of previous steps. For example, if a connector fails after retries, you can send a notification email or invoke compensation logic. This combination provides robust, maintainable, and fault-tolerant workflows, suitable for business-critical automation such as invoice approvals. Using built-in policies ensures predictable behavior, reduces complexity, and aligns with enterprise-grade serverless architecture practices recommended in AZ-204.

Recurrence triggers operate on a schedule and poll for changes at intervals. They do not automatically handle transient failures or retries, and they introduce latency because workflows are only triggered periodically.

HTTP triggers respond to external calls but lack built-in retry policies for downstream actions. If connectors fail, you must implement custom retry logic, which increases complexity and risk of errors.

Manual retry logic in each step is error-prone and difficult to maintain. Each action requires custom code for retries, delays, and error handling, which complicates workflow management and increases operational overhead. Built-in retry and run-after mechanisms provide a cleaner, standardized approach.

Question 88:

You are building an Azure App Service API that reads frequently accessed data from Cosmos DB. You want low-latency reads and reduced 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, providing millisecond-level response times and reducing repeated queries. This decreases RU consumption, lowers operational costs, and improves overall performance. Cached data can be automatically refreshed or invalidated on a schedule, ensuring consistency while maintaining high throughput. This solution is ideal for read-heavy workloads, such as dashboards, telemetry, and product catalogs, and aligns with serverless and scalable architecture patterns. Integrated caching works seamlessly with Azure App Service and Functions, minimizing developer effort while improving performance.

Automatic indexing improves query execution by creating indexes for all documents. While helpful for complex queries, it does not reduce RU consumption for frequently accessed data and does not provide caching or low-latency reads.

Multi-region writes improve write availability globally but do not reduce latency for reads in a single region. They also increase cost due to cross-region replication. This feature is primarily intended for globally distributed write-heavy applications.

TTL (Time-to-Live) automatically deletes documents after a defined interval. While useful for ephemeral data, TTL does not cache frequently accessed items or reduce RU consumption. It is mainly a data lifecycle management feature, not a read optimization tool.

Question 89:

You are configuring an Azure Function that ingests telemetry events from Event Hubs. You need high throughput 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:

Using multiple partitions with one consumer per partition allows parallel processing while maintaining message order within each partition. Each consumer independently processes its partition, and checkpointing ensures at-least-once delivery. Retry policies handle transient errors without affecting ordering. This design scales horizontally, enabling high throughput for telemetry and IoT scenarios while preserving device-specific sequence integrity, which is critical for downstream analytics, monitoring, and state management. Azure Functions automatically balances load across partitions and scales consumers as needed, following serverless best practices.

A single partition with one consumer guarantees ordering but limits throughput, as all messages are processed sequentially. This can become a bottleneck in high-volume telemetry scenarios.

Multiple partitions without mapping consumers can result in unordered message processing, as multiple consumers may read from partitions unpredictably. This breaks device-specific ordering and can lead to inconsistent analytics or state updates.

Batch processing, ignoring partitions may increase throughput sacrifices order. Events from the same device could be processed out of sequence, causing errors in analytics, state management, or alerting workflows.

Question 90:

You are configuring an Azure API Management (APIM) instance for internal APIs. You need to enforce authentication and capture detailed 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 provides identity-based access control, ensuring only authenticated and authorized users can access APIs. Diagnostic logging captures request and response data, headers, and metadata, which can be sent to Log Analytics, Event Hubs, or Storage for auditing and monitoring. This configuration supports enterprise-grade security, token-based authentication, and role-based access control. OAuth ensures all API access is traceable and auditable, meeting both operational and compliance requirements for internal APIs. This aligns fully with AZ-204 exam objectives for secure API management.

Anonymous access with logging allows requests to be captured but does not enforce authentication, leaving APIs exposed. Logging alone cannot prevent unauthorized access.

Basic authentication with local logs provides credentials-based access but lacks centralized management, robust auditing, and token-based security. Maintaining credentials and tracking access is more complex and less scalable.

IP restrictions limit access to certain networks but do not verify user identity. Users within the allowed networks could still access APIs without authentication, making this insufficient for secure internal APIs.

Question 91:

You are developing an Azure Function that processes high-volume telemetry data from Event Hubs. You need parallel processing while preserving message order per device. Which architecture 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 one consumer per partition is the recommended architecture for high-volume, order-sensitive workloads. Each consumer processes only its assigned partition, ensuring that messages remain in sequence within the partition. Azure Functions supports automatic scaling across partitions, enabling high throughput while maintaining message order. Checkpointing ensures at-least-once delivery, and retry policies handle transient failures without disrupting ordering. Dead-letter queues capture poisoned messages for analysis and manual remediation, enhancing fault tolerance. This architecture is ideal for IoT telemetry, device state monitoring, and financial transaction processing, where sequence integrity is critical.

A single partition with one consumer guarantees ordering but limits throughput because all messages are processed sequentially. High-volume workloads would be delayed, creating a bottleneck and potentially impacting downstream analytics.

Multiple partitions without mapping consumers can result in unordered processing, as multiple consumers may read from different partitions unpredictably. Device-specific sequences can be broken, leading to inconsistent data in telemetry analytics, monitoring, or stateful processing.

Batch processing, ignoring partitions, can increase throughput but sacrifice ordering guarantees. Events from the same device could be processed out of sequence, causing errors in analytics, state management, or alerting workflows. This approach is unsuitable when message order is critical.

Question 92:

You are building an Azure App Service API that reads frequently accessed data from Cosmos DB. You need low-latency reads and reduced 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 data in memory, providing millisecond-level read performance. This reduces repeated database queries, lowering RU consumption and operational costs. Cached data can be refreshed automatically on demand, ensuring consistency while maintaining high throughput. This approach is ideal for read-heavy workloads, including dashboards, telemetry, and product catalogs. It aligns with serverless best practices, improving performance and scalability without increasing database load. Integration with Azure App Service and Functions allows seamless use in serverless and high-concurrency scenarios with minimal developer overhead.

Automatic indexing improves query execution by creating indexes for all documents. While beneficial for complex queries, it does not reduce RU consumption or repeated reads for frequently accessed data and does not provide caching benefits.

Multi-region writes enhance write availability and latency across regions but do not improve read performance in a single region. Additionally, they increase operational costs due to replication. Multi-region writes are suitable for globally distributed write-heavy applications,b ut are not a read optimization solution.

TTL (Time-to-Live) automatically deletes documents after a set interval. While useful for ephemeral data or logs, TTL does not improve read latency or reduce RU consumption for frequently accessed items. It primarily serves as a data lifecycle management feature, not a caching or performance optimization tool.

Question 93:

You are configuring an Azure Function to process messages from Service Bus Queues. You need reliable processing, avoid duplicates, and handle transient failures. 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 implement manual retries
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, preventing other consumers from reading it. The message is only removed after the function completes successfully. Duplicate detection ensures that the same message is not processed multiple times within a specified window, providing at-least-once delivery without duplication. Combined with Azure Functions’ retry policies and checkpointing, this ensures high reliability and fault tolerance. Dead-letter queues capture messages that repeatedly fail, allowing for investigation and manual remediation. This approach is suitable for critical workloads such as financial transactions, telemetry ingestion, and IoT processing.

ReceiveAndDelete mode immediately removes messages upon receipt. If processing fails, the message is lost. While simpler, this is unsafe for critical workloads and should only be used for non-critical or idempotent messages.

Ignoring message locks and manually retrying increases complexity and error risk. Developers must handle checkpointing, duplicate detection, and retries manually, which is operationally challenging and error-prone.

Multiple consumers with the ReceiveAndDelete mode allow parallel processing but sacrifice reliability. Messages are removed immediately, so failures result in lost messages, and duplicate detection is not supported. This approach prioritizes throughput over correctness, making it unsuitable for enterprise-critical applications.

Question 94:

You are building a Logic App that triggers when invoices are uploaded to Azure Blob Storage. You want real-time processing and exactly-once execution. Which trigger 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 an event-driven, push-based trigger that executes immediately when a new blob is uploaded. Concurrency controls and built-in deduplication prevent multiple executions for the same blob, ensuring exactly-once processing. It integrates seamlessly with Logic App actions, allowing error handling, retries, and run-after configurations. This makes it ideal for workflows like invoice processing, document approvals, and automated file transformations. Using this trigger reduces operational complexity and aligns with serverless and event-driven design principles recommended in AZ-204.

Recurrence triggers poll storage at fixed intervals, introducing latency and the potential to process multiple blobs simultaneously. They may also execute unnecessarily when no new blobs exist, increasing operational costs.

HTTP triggers rely on external systems to invoke the workflow. While flexible, this approach adds complexity and latency, and introduces additional points of failure because an external process must detect blob uploads.

Service Bus queue triggers react to queued messages, not blob creation events. Using a queue requires extra logic to send a message whenever a blob is uploaded, which adds operational overhead and latency compared to the direct blob trigger.

Question 95:

You are configuring an Azure API Management (APIM) instance for internal APIs. You need to enforce authentication and capture detailed 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 provides identity-based access control, ensuring that only authenticated and authorized users can access APIs. Diagnostic logging captures detailed request and response information, headers, and metadata, which can be routed to Log Analytics, Event Hubs, or Storage for auditing, monitoring, and compliance. This configuration supports enterprise-grade security, token-based authentication, and role-based access control. OAuth ensures all API access is traceable and auditable, meeting operational and compliance requirements. It is the recommended approach for internal APIs requiring secure, monitored access in line with AZ-204 objectives.

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

Basic authentication with local logs provides credentials-based access but lacks centralized management, token-based authentication, and robust auditing. Managing credentials and tracking access is complex and less scalable.

IP restrictions limit access to certain networks but do not verify user identity. Users within allowed networks could access APIs without authentication, making this approach insufficient for secure internal APIs.

Question 96:

You are developing an Azure Function that processes messages from Event Hubs. You need high throughput while maintaining message order per device. Which architecture 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:

Using multiple partitions with one consumer per partition ensures parallel processing across partitions while maintaining message order within each partition. Each consumer independently processes its assigned partition. Azure Functions supports automatic scaling and checkpointing to guarantee at-least-once delivery. Retry policies handle transient failures without disrupting the message sequence. This approach is ideal for IoT telemetry, financial transactions, or real-time analytics, where message order is critical for downstream processing. Dead-letter queues handle poisoned messages, improving reliability and allowing manual investigation. This architecture aligns with serverless and event-driven design patterns emphasized in AZ-204.

A single partition with one consumer guarantees order, but severely limits throughput because all messages are processed sequentially. High-volume workloads would experience bottlenecks and delayed processing.

Multiple partitions without mapping consumers can lead to unordered processing, as multiple consumers may read from partitions unpredictably. This breaks device-specific order, which can compromise telemetry analytics, state management, or alerting workflows.

Batch processing, singling ignoring parts improves throughput but sacrifices ordering guarantees. Events from the same device may be processed out of sequence, leading to inconsistent downstream analytics or errors in stateful applications.

Question 97:

You are building an Azure App Service API that reads frequently accessed data from Cosmos DB. You want low-latency reads and reduced 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 data in memory, providing millisecond-level read performance while reducing RU consumption. Cached data can be automatically refreshed or invalidated on a schedule, ensuring data consistency. This approach is ideal for high-concurrency, read-heavy workloads such as dashboards, telemetry, and product catalogs. Integrated caching improves scalability and reduces database load, aligning with serverless and high-performance application patterns in AZ-204.

Automatic indexing improves query performance by creating indexes on documents. It helps complex queries run faster, but does not reduce RU consumption for frequently accessed data. Indexing does not provide caching or low-latency reads for hot items.

Multi-region writes enhance write availability across global regions but do not improve read performance in a single region. They also increase operational costs due to replication, making them unsuitable for read optimization scenarios.

TTL (Time-to-Live) deletes documents automatically after a specified interval. While useful for ephemeral data, TTL does not provide caching, reduce RU consumption, or improve read latency for frequently accessed data. It is primarily a data lifecycle management feature.

Question 98:

You are configuring an Azure Function to process messages from Service Bus Queues. You need reliable message processing to avoid duplicates and handle poison messages gracefully. Which approach should you implement?

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

Answer: B) Peek-lock mode with duplicate detection and dead-lettering

Explanation:

Peek-lock mode locks messages temporarily during processing and deletes them only after successful completion. This prevents other consumers from reading the same message concurrently. Duplicate detection ensures the same message is not processed multiple times within a defined window. Dead-letter queues handle messages that repeatedly fail processing, allowing manual intervention and analysis. Combined with Azure Functions’ retry policies and checkpointing, this approach guarantees at-least-once delivery, fault tolerance, and operational reliability. It is ideal for critical workloads, including financial systems, telemetry ingestion, and IoT pipelines. When designing an Azure Function or any message-processing system that consumes messages from Azure Service Bus queues or topics, choosing the right message-handling mode is critical to ensure reliability, fault tolerance, and at-least-once delivery guarantees. Each approach for handling messages has trade-offs in terms of throughput, data safety, and operational complexity. Understanding these trade-offs is essential for designing production-grade, resilient systems.

Option B, using peek-lock mode with duplicate detection and dead-lettering, is the recommended approach for most reliable message-processing scenarios. In peek-lock mode, a message is read and temporarily locked by the consumer, preventing other consumers from processing it until the lock expires or the message is explicitly completed. This ensures that messages are not lost if the consumer encounters a failure during processing. If the function successfully processes the message, it sends a “complete” command to the Service Bus, which removes the message from the queue. If processing fails or the lock expires, the message becomes visible again for retry, enabling at-least-once delivery semantics.

Duplicate detection adds another layer of reliability by preventing the processing of messages that are accidentally sent multiple times, which can happen due to retries or network errors. Dead-lettering is also critical for robust message handling. Messages that fail to process after a certain number of retries, or that exceed system-defined thresholds, are moved to a dead-letter queue. This ensures that failed messages are captured for later inspection and reprocessing, without blocking the queue or causing repeated failures. Combined, peek-lock, duplicate detection, and dead-lettering provide a highly reliable system capable of handling transient failures, maintaining message integrity, and supporting operational monitoring.

Option A, using ReceiveAndDelete mode with a single consumer, is simpler and riskier. In this mode, messages are immediately removed from the queue as soon as they are read. If the consumer fails during processing, the message is lost permanently. While this mode may be acceptable for non-critical workloads where occasional message loss is tolerable, it is unsuitable for scenarios where reliable delivery and processing are required.

Option C, ignoring message locks and retrying manually, introduces complexity and increases the likelihood of errors. Implementing custom retry logic without using peek-lock semantics requires careful tracking of which messages have been processed successfully. Mistakes can result in duplicate processing, message loss, or inconsistent application state. This approach also bypasses the native capabilities of Azure Service Bus, such as automated lock renewal and dead-lettering, making the system harder to maintain and monitor.

Option D, using multiple consumers in the ith ReceiveAndDelete mode, increases throughput but also multiplies the risk of data loss. Messages are immediately removed from the queue and cannot be recovered if any consumer fails during processing. This approach may seem attractive for parallel processing scenarios, ut sacrifices reliability and is generally unsuitable for critical workloads.

In summary, peek-lock mode with duplicate detection and dead-lettering is the most reliable and maintainable approach for processing messages from Azure Service Bus queues. It ensures at least once delivery, supports fault-tolerant processing, prevents duplicates, and provides mechanisms to handle messages that repeatedly fail. Other approaches either compromise reliability, increase operational complexity, or risk message loss, making them less suitable for production environments where message integrity and fault tolerance are critical.

ReceiveAndDelete mode immediately removes messages from the queue. If processing fails, messages are lost, making it unsafe for critical workloads. This mode is suitable only for idempotent or non-critical operations.

Ignoring message locks and retrying manually is risky and complex. Developers must implement checkpointing, duplicate detection, and error handling themselves, increasing operational complexity and the likelihood of errors.

Multiple consumers with the ReceiveAndDelete mode allow parallel processing but compromise reliability. Messages are deleted immediately, so failed processing results in lost messages. Duplicate detection is also not supported, making this unsuitable for enterprise-critical workloads.

Question 99:

You are building a Logic App that triggers when files are uploaded to Azure Blob Storage. You need real-time processing and exactly-once execution. Which trigger 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 a push-based, event-driven trigger. It fires immediately when a new blob is uploaded, providing low-latency, real-time processing. Built-in concurrency controls prevent multiple executions for the same blob, ensuring exactly-once processing. Integration with Logic App actions allows error handling, retries, and run-after configurations, making workflows reliable and maintainable. This is ideal for invoice processing, document approvals, or file transformations. Using a push-based blob trigger minimizes operational overhead and aligns with serverless and event-driven architecture principles emphasized in AZ-204.

Recurrence triggers poll storage at scheduled intervals, introducing latency and possible duplication. Workflows may run unnecessarily when no new blobs exist, increasing cost and inefficiency.

HTTP triggers rely on an external system to invoke the workflow. While flexible, this adds complexity and latency, and creates additional points of failure since an external service must detect blob uploads.

Service Bus queue triggers respond to queued messages rather than blob creation events. To use a queue, each blob upload must be converted to a message, adding operational overhead and latency compared to a direct blob trigger.

Question 100:

You are configuring an Azure API Management (APIM) instance for internal APIs. You need to enforce authentication and capture detailed 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 provides identity-based access control, ensuring only authenticated and authorized users can access APIs. Diagnostic logging captures detailed request and response data, including headers, payloads, and metadata, which can be routed to Log Analytics, Event Hubs, or Storage for auditing, monitoring, and compliance. This configuration supports enterprise-grade security, token-based authentication, and role-based access control. OAuth ensures all access is traceable and auditable, satisfying both operational and compliance requirements. This is the recommended approach for internal APIs in line with AZ-204 best practices.

When designing an API in Azure that exposes sensitive financial data, it is essential to ensure that only authorized applications or users can access the API. At the same time, maintaining detailed logging for auditing and compliance purposes is critical. Unauthorized access or insufficient logging can result in data breaches, compliance violations, and operational risks. Therefore, selecting the correct authentication and logging mechanism is key to protecting sensitive information and ensuring regulatory compliance.

Option D, using OAuth 2.0 with Azure Active Directory (Azure AD) and diagnostic logging, is the recommended approach. OAuth 2.0 is an industry-standard protocol for authorization, allowing applications to securely obtain access tokens that prove their identity and permissions without exposing user credentials. By integrating OAuth 2.0 with Azure AD, organizations can centralize identity management and enforce strict access control policies. Azure AD ensures that only registered applications or users with valid credentials and proper permissions can call the API. This integration also supports granular access control through scopes and roles, allowing administrators to specify exactly which resources or operations each application or user can access. Additionally, OAuth tokens have a defined expiration, reducing the risk of long-lived credentials being compromised.

Diagnostic logging complements this approach by capturing detailed information about every request made to the API. Logs can include the caller’s identity, timestamps, request payloads, response codes, and other metadata. This provides a comprehensive audit trail for compliance with regulations such as PCI DSS, SOX, or GDPR. By using Azure Monitor, Log Analytics, or Event Hubs, logs can be centralized, retained for long periods, and used for real-time monitoring or alerts in case of suspicious activity. This combination of authentication and logging ensures both secure access and visibility into all API operations.

Other options are less effective for protecting sensitive APIs. Option A, anonymous access with logging, exposes the API to any requester. While logging records access attempts, it does not prevent unauthorized users from sending requests, making the API vulnerable to misuse or attacks. Option B, basic authentication with local logs, requires managing usernames and passwords manually. Credentials are often transmitted in plaintext or base64-encoded, which is less secure than token-based OAuth authentication. Local logs also present risks of data loss or incomplete auditing if the server is restarted or compromised. Option C, IP restrictions only, limits access to specific network ranges but does not provide identity-based control. Attackers within allowed IP ranges could still access the API, and IP restrictions cannot enforce user-level or application-level permissions.

In conclusion, using OAuth 2.0 with Azure AD and diagnostic logging ensures secure, identity-based access to sensitive financial APIs while providing comprehensive logging for auditing and compliance. This approach leverages Azure’s native security and monitoring capabilities, reduces operational risks, and enables centralized control over who can access critical resources. It is the most suitable choice for enterprise-grade, secure, and auditable API deployments.

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 provides credentials-based access but lacks centralized management, robust auditing, and token-based security. Credential management, rotation, and monitoring are more complex and less scalable.

IP restrictions limit access by network location but do not verify user identity. Users within allowed networks could still access APIs without authentication, making it insufficient for secure internal APIs.