Microsoft AZ-204 Developing Solutions for Microsoft Azure Exam Dumps and Practice Test Questions Set 7 Q121-140

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Question 121:

You are developing an Azure Function that processes messages from Event Hubs. You need high throughput and 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 ensure parallel processing while maintaining message order within each partition. Each consumer handles a dedicated partition, which supports high-volume workloads and scales horizontally. Checkpointing ensures at-least-once delivery, while retry policies handle transient failures. Dead-letter queues capture failed messages for manual remediation. This architecture is ideal for telemetry ingestion, IoT data, and financial event processing.

A single partition with one consumer preserves order but limits throughput, creating a bottleneck for high-volume workloads and increasing latency for real-time processing. When designing event-driven architectures using Azure Event Hubs, understanding how partitions and consumers interact is critical to achieving both high throughput and reliable, ordered processing. Event Hubs organizes messages into partitions, which act as independent, ordered streams of data. Properly aligning consumers with partitions ensures that messages are processed efficiently while preserving order and fault tolerance.

Option C, using multiple partitions with one consumer per partition, is the recommended approach for scenarios that require both high throughput and ordered message processing. Each partition can be processed independently by a dedicated consumer, ensuring that the sequence of events within the partition is maintained. At the same time, distributing partitions across multiple consumers allows for parallel processing, maximizing throughput and minimizing latency. This setup is especially important for high-volume event streams, such as telemetry data from IoT devices, financial transaction streams, or large-scale logging pipelines. Azure Event Hubs, combined with consumer groups and checkpointing, allows each consumer to track its progress independently, providing fault tolerance. If a consumer fails, another instance can resume processing from the last checkpoint without data loss, maintaining at least once delivery guarantees.

Option A, using a single partition with one consumer, simplifies the architecture and guarantees order, but it severely limits throughput. All messages are processed sequentially by a single consumer, creating a bottleneck under high event volume. For applications with millions of events per second, a single partition cannot handle the load efficiently, leading to increased latency and slower system responsiveness. This approach is only suitable for low-volume workloads where ordering is critical but high throughput is not required.

Option B, multiple partitions without mapping consumers to specific partitions, can lead to problems with ordering and potential race conditions. Event Hubs relies on each partition being processed by a designated consumer to maintain sequence. If consumers are not mapped properly, multiple consumers could compete for messages from the same partition, leading to duplicate processing, out-of-order handling, and inconsistent state in the application. This approach undermines the very guarantees that Event Hubs partitions are designed to provide, making it unreliable for critical workloads that require ordered processing.

Option D, batch processing while ignoring partitions, may seem appealing for efficiency, but it sacrifices order guarantees and increases the risk of errors. By batching messages without respecting partition boundaries, events can be processed out of sequence, which is problematic for workflows that depend on strict ordering, such as financial transactions or time-series data analysis. Additionally, ignoring partitions complicates checkpointing and failure recovery because batches may contain messages from multiple partitions, making it harder to track which events have been processed successfully.

In summary, using multiple partitions with one consumer per partition is the best practice for high-throughput, low-latency, and ordered event processing in Azure Event Hubs. It provides scalability, ensures message order within partitions, allows parallel processing, and supports fault-tolerant recovery. Other approaches either limit performance, compromise reliability, or introduce complexity and potential errors in message handling. By leveraging dedicated consumers per partition, applications can achieve optimal throughput and maintain data integrity while fully utilizing Event Hubs’ distributed architecture.

Multiple partitions without mapping consumers risk unordered processing, as multiple consumers may read the same partition concurrently, disrupting device-level sequence.

Batch processing, ignoring partitions, increases throughput but sacrifices ordering. Events may be processed out of sequence, causing analytics errors or incorrect alerts.

Question 122:

You are building an Azure App Service API that frequently reads data from Cosmos DB. You want low-latency reads and reduced RU costs. 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 read latency and RU consumption. Cached items can be refreshed automatically or on demand to maintain data consistency, improving performance for read-heavy workloads like dashboards, telemetry, or product catalogs. This approach aligns with serverless best practices.

Automatic indexing improves query performance but does not reduce RU consumption or repeated reads. It does not provide caching benefits.

Multi-region writes enhance write availability but do not improve read latency in a single region. They also add replication complexity and cost.

TTL automatically deletes documents after a set interval, which is useful for ephemeral data but does not improve read performance or caching for frequently accessed items.

Question 123:

You are configuring an Azure Function to process messages from Service Bus Queues. You need reliable processing and avoid duplicates. 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 manually retry
D) Multiple consumers with the ReceiveAndDelete mode

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

Explanation:

Peek-lock mode locks messages temporarily while processing, removing them only after successful completion. Duplicate detection ensures messages are not processed more than once. Dead-letter queues capture messages that fail multiple times, allowing manual inspection. This ensures at-least-once delivery and is suitable for critical workloads such as telemetry ingestion, IoT pipelines, or financial transactions.  When building reliable, fault-tolerant systems that consume messages from Azure Service Bus queues or topics, the choice of message-processing mode is critical. Azure Service Bus provides two primary modes for receiving messages: ReceiveAndDelete and Peek-Lock. Each approach has trade-offs in terms of reliability, fault tolerance, and operational complexity. Understanding these trade-offs is essential for designing production-ready applications that process messages correctly, avoid data loss, and handle failures gracefully.

Option B, using peek-lock mode with duplicate detection enabled, is the recommended and most robust approach for critical workloads. In peek-lock mode, when a consumer reads a message, the message is temporarily locked and remains in the queue. This prevents other consumers from processing the same message simultaneously. The consumer then processes the message and explicitly completes it. If processing succeeds, the message is removed from the queue. If processing fails or the consumer crashes before completion, the lock eventually expires, and the message becomes available again for reprocessing. This mechanism ensures at-least-once delivery, providing a reliable way to handle transient errors and maintain message integrity.

Duplicate detection further enhances reliability by preventing the same message from being processed multiple times. In distributed systems, transient network issues, retries, or client-side errors can sometimes result in the same message being sent more than once. Enabling duplicate detection ensures that only one copy of a message is processed within a configurable time window, reducing the risk of inconsistent application state or duplicated operations. Combined, peek-lock and duplicate detection allow applications to achieve reliable, idempotent processing while minimizing the risk of message loss or duplication.

Option A, ReceiveAndDelete mode with a single consumer, immediately removes messages from the queue upon retrieval. While this approach is simpler and may increase throughput, it is risky for critical workloads because messages are lost if the consumer fails during processing. Any transient error during message handling can result in permanent data loss. This mode may be acceptable for scenarios where occasional message loss is tolerable, such as logging or non-critical telemetry, but it is unsuitable for workflows that require guaranteed message delivery, such as financial transactions or order processing.

Option C, ignoring message locks and manually retrying, introduces significant complexity and increases the likelihood of errors. Implementing custom retry logic without using peek-lock mode requires tracking which messages have been successfully processed. Mistakes can lead to duplicate processing, missed messages, or inconsistent application state. It also bypasses the native Service Bus mechanisms, such as automatic lock renewal and dead-letter queues, making the system harder to maintain and monitor.

Option D, multiple consumers with the ReceiveAndDelete mode, increases throughput but further amplifies the risk of data loss. Messages are immediately removed from the queue, so if any consumer fails during processing, those messages are lost permanently. This approach may seem appealing for high-throughput scenarios, but it sacrifices reliability and is generally unsuitable for production workloads where message integrity is essential.

In conclusion, peek-lock mode with duplicate detection enabled provides a reliable, fault-tolerant, and maintainable approach for processing messages in Azure Service Bus. It ensures at-least-once delivery, prevents duplication, and allows for safe error recovery, making it the optimal choice for production systems that require guaranteed message processing and operational robustness.

ReceiveAndDelete mode removes messages immediately. If processing fails, messages are lost, making this approach unsafe for critical workloads.

Ignoring message locks and manually retrying adds operational complexity and increases the risk of duplicates. Developers must implement checkpointing and retry logic themselves.

Multiple consumers with the ReceiveAndDelete mode allow parallel processing but compromise reliability. Messages could be lost or processed more than once, making this option unsuitable for enterprise workloads.

Question 124:

You are building a Logic App triggered by new blob uploads in Azure Blob Storage. You need real-time execution and exactly-once processing. 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 executes immediately upon blob creation. Built-in concurrency controls and deduplication ensure exactly-once processing, making it suitable for invoice processing, document approvals, and automated file transformations. Logic Apps integrates retry policies and error handling to maintain reliability.

Recurrence triggers poll at intervals, introducing latency and potential duplicate executions, making them unsuitable for real-time processing.

HTTP triggers require an external system to invoke the workflow, adding complexity and additional failure points.

Service Bus queue triggers respond to queued messages rather than blob events. Using a queue adds operational overhead and latency compared to a direct blob trigger.

Question 125:

You are configuring Azure API Management (APIM) for internal APIs. You need secure authentication and auditable logging. 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 users can access APIs. Diagnostic logging captures request and response details, headers, and payloads, which can be sent to Log Analytics, Event Hubs, or Storage for auditing, monitoring, and compliance. This solution meets enterprise security and aligns with AZ-204 best practices.

Anonymous access with logging captures requests but does not enforce authentication, leaving APIs exposed to unauthorized users. When developing an API that exposes sensitive financial or business-critical data, securing access and ensuring auditability are top priorities. Unauthorized access to the API could result in data breaches, compliance violations, financial loss, and reputational damage. Additionally, for regulatory compliance and operational transparency, every API request must be logged in detail. These requirements make it essential to choose an authentication and logging approach that provides robust security, granular access control, and comprehensive monitoring.

Option D, using OAuth 2.0 with Azure Active Directory (Azure AD) and diagnostic logging, is the most secure and reliable choice. OAuth 2.0 is an industry-standard authorization protocol that enables secure token-based access. Instead of passing user credentials directly with each API request, the client receives a time-bound access token from Azure AD. This token contains information about the client’s identity, permissions (scopes), and expiration. By validating the token, the API ensures that only authorized applications or users can access protected resources. Azure AD serves as a centralized identity provider, allowing organizations to manage user and application identities, enforce policies, and revoke access if needed. This provides strong identity-based access control and eliminates the risks associated with managing passwords or API keys manually.

Diagnostic logging is a critical complement to OAuth 2.0. By enabling logging, every request made to the API—including headers, payloads, response codes, and caller identity—is recorded. This creates an audit trail that supports compliance with regulations such as PCI DSS, GDPR, HIPAA, or SOX. Centralized logging also allows IT and security teams to monitor access patterns, detect anomalies, investigate incidents, and generate reports. Logs can be sent to Azure Monitor, Log Analytics, or Event Hubs for real-time monitoring, retention, and analysis. This combination of strong authentication and comprehensive logging provides both security and operational oversight.

Other options are significantly less secure or effective. Option A, anonymous access with logging, allows anyone to call the API. While logging can record access attempts, it does not prevent unauthorized users from accessing sensitive data, leaving the system vulnerable to attacks. Option B, basic authentication with local logs, requires clients to send usernames and passwords with each request. This approach is less secure because credentials can be intercepted if not properly encrypted, and storing logs locally increases the risk of data loss or incomplete audit records. Option C, IP restrictions only, can limit access to specific networks but does not enforce user-level authentication or authorization. Attackers within allowed IP ranges could still access the API, and IP filtering provides no insight into who performed what actions.

In summary, using OAuth 2.0 with Azure AD and diagnostic logging ensures secure, identity-based access control while capturing detailed records for auditing and compliance. OAuth 2.0 provides token-based authentication, Azure AD centralizes identity management, and diagnostic logging enables monitoring and compliance reporting. This combination protects sensitive data, supports regulatory requirements, and delivers enterprise-grade security and operational transparency, making it the most suitable choice for any production-grade API exposing critical information.

Basic authentication with local logs is less secure, lacks centralized management, and does not provide comprehensive auditing. Managing credentials is complex.

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

Question 126:

You are developing an Azure Function to process messages from Event Hubs. You want parallel processing and 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 ensure that each consumer processes a dedicated partition, maintaining message order within that partition while enabling high throughput. Azure Functions automatically scales instances to handle partition distribution, providing parallel processing without compromising per-device sequence. Checkpointing guarantees at-least-once delivery, and retry policies address transient failures. Dead-letter queues capture messages that fail repeatedly. This architecture is ideal for telemetry ingestion, IoT devices, or real-time analytics where order is crucial.

A single partition with one consumer preserves order but limits throughput. High-volume workloads may experience bottlenecks and latency issues.

Multiple partitions without mapping consumers risk unordered message processing, as multiple consumers could read from the same partition concurrently, leading to inconsistent data.

Batch processing, ignoring partitions, increases throughput but sacrifices order, potentially causing incorrect analytics or alerts if device events are processed out of sequence.

Question 127:

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, reducing read latency and RU consumption. Cached items can be refreshed automatically or on demand to maintain consistency. This approach is ideal for read-heavy workloads such as dashboards, telemetry, or product catalogs, aligning with serverless best practices.

Automatic indexing improves query performance for complex queries but does not reduce RU consumption or repeated reads, nor does it provide caching.

Multi-region writes enhance write availability across regions but do not optimize read latency in a single region, and add complexity and cost.  When designing applications that rely on Azure Cosmos DB, performance and cost optimization are key considerations, especially for workloads with frequent read operations. Cosmos DB offers multiple features to help improve performance and reduce operational costs, including automatic indexing, multi-region writes, TTL (Time-to-Live), and integrated caching. Among these options, integrated Cosmos DB caching is particularly important for scenarios where low-latency reads are critical, such as dashboards, reference data queries, or high-traffic applications.

Option D, integrated Cosmos DB caching, provides an in-memory cache layer that stores frequently accessed data, also known as “hot” data. By retrieving data from memory rather than querying the database for every request, applications can significantly reduce read latency and improve responsiveness. This is especially beneficial for read-heavy workloads where the same data is accessed repeatedly, such as product catalogs, configuration settings, or user profiles. Integrated caching also reduces the consumption of Request Units (RUs), which are the currency for Cosmos DB operations. Lower RU usage translates into cost savings while maintaining high throughput, making it an efficient approach for frequently queried data.

In addition to performance improvements, integrated caching handles cache expiration and refresh policies automatically. This ensures that applications receive fresh data without the need for complex cache invalidation logic in the application code. It simplifies architecture by allowing developers to focus on core business logic rather than building and maintaining a separate caching layer. Furthermore, integrated caching works seamlessly with Cosmos DB’s partitioned data model and supports scaling alongside your database, ensuring that the cache remains performant even as the workload grows.

Option A, automatic indexing, is a feature where Cosmos DB automatically indexes all document properties, enabling efficient querying without requiring a schema. While automatic indexing improves query performance, it does not directly reduce read latency for frequently accessed data, as each query still needs to access the database. Automatic indexing is more relevant for flexible and dynamic querying, but repeated access to the same dataset does not benefit as much as it does from caching.

Option B, multi-region writes, is a feature that allows applications to perform writes in multiple Azure regions simultaneously. This capability is critical for globally distributed applications requiring low-latency writes and high availability. Multi-region writes improve write performance and resilience but do not reduce the time it takes to read frequently accessed data. While multi-region writes complement caching in distributed systems, caching directly addresses the need for low-latency reads for frequently accessed items.

Option C, TTL (Time-to-Live), automatically deletes documents after a specified duration, helping manage storage and maintain efficient queries by removing stale data. TTL is useful for temporary or ephemeral data, such as session information or temporary logs. However, TTL does not provide a performance boost for repeated reads, as it only manages the lifecycle of data rather than optimizing retrieval speed.

In summary, integrated Cosmos DB caching (Option D) is the most effective solution for reducing read latency and improving performance for frequently accessed data. Automatic indexing improves query flexibility, multi-region writes optimize global write performance, and TTL helps manage ephemeral data, but only caching provides fast, in-memory access for hot data. By leveraging integrated caching, developers can build responsive, cost-efficient applications that handle frequent read requests efficiently while reducing database load and overall RU consumption.

TTL deletes documents after a specified interval. While useful for ephemeral data, it does not improve read performance or caching for frequently accessed items.

Question 128:

You are configuring an Azure Function to process messages from Service Bus Queues. You need reliable processing and avoid duplicates. 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 manually retry
D) Multiple consumers with the ReceiveAndDelete mode

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

Explanation:

Peek-lock mode temporarily locks messages while processing, removing them only after successful completion. Duplicate detection prevents the same message from being processed multiple times. Dead-letter queues capture messages that fail multiple times, allowing manual remediation. This ensures at-least-once delivery and is suitable for critical workloads like telemetry ingestion, IoT pipelines, or financial transactions. When designing a reliable system to process messages from Azure Service Bus queues or topics, it is critical to choose the appropriate message-handling mode to ensure fault tolerance, message integrity, and operational resilience. Azure Service Bus provides two primary message-receiving modes: ReceiveAndDelete and Peek-Lock. Each mode offers different guarantees regarding message delivery and processing reliability, and understanding the trade-offs is essential for building production-grade applications.

Option B, using peek-lock mode with duplicate detection enabled, is the recommended approach for reliable, fault-tolerant message processing. In peek-lock mode, a consumer reads a message from the queue but does not immediately remove it. Instead, the message is temporarily locked, preventing other consumers from processing it simultaneously. The consumer then processes the message and explicitly completes it. If processing succeeds, the message is deleted from the queue. If processing fails or the consumer crashes before completing the message, the lock eventually expires, making the message visible again for reprocessing. This mechanism ensures at-least-once delivery, allowing transient failures to be handled without losing messages.

Duplicate detection further enhances reliability. In distributed systems, messages can sometimes be sent multiple times due to network issues, retries, or client-side errors. Enabling duplicate detection ensures that only one copy of a message is processed within a configurable time window. This prevents inconsistent application state caused by repeated processing of the same message. Combined, peek-lock and duplicate detection provide a highly reliable solution that supports fault-tolerant processing while minimizing the risk of message loss or duplication.

Option A, ReceiveAndDelete mode with a single consumer, simplifies message processing by removing messages immediately upon retrieval. While this approach increases throughput and reduces complexity, it is risky for critical workloads because messages are permanently deleted even if the consumer fails during processing. Any transient error could result in permanent data loss. This mode may be acceptable for non-critical scenarios like logging or telemetry, where occasional message loss is tolerable, but it is unsuitable for applications requiring guaranteed message delivery.

Option C, ignoring message locks and manually retrying, adds significant complexity and increases the risk of errors. Developers would need to track which messages have been successfully processed, manage retries, and ensure idempotency manually. Mistakes could lead to duplicate processing, lost messages, or inconsistent data. This approach also bypasses Service Bus features such as automatic lock renewal and dead-lettering, making the system harder to maintain and monitor.

Option D, multiple consumers with the ReceiveAndDelete mode, increases throughput but compounds the risk of message loss. Since messages are deleted immediately upon retrieval, any failure in processing results in lost messages. This approach may seem attractive for parallel processing, but it sacrifices reliability, making it unsuitable for critical or high-value workloads.

In conclusion, peek-lock mode with duplicate detection enabled provides a reliable, fault-tolerant, and maintainable approach for Azure Service Bus message processing. It ensures at-least-once delivery, prevents duplicate processing, and allows safe recovery from transient failures, making it the optimal choice for production systems that require guaranteed message integrity and operational resilience.

ReceiveAndDelete mode immediately removes messages. If processing fails, messages are lost, making this approach unsafe for critical workloads.

Ignoring message locks and manually retrying adds operational complexity and increases the risk of duplicates. Developers must implement checkpointing and retry logic themselves.

Multiple consumers with the ReceiveAndDelete mode allow parallel processing but compromise reliability. Messages could be lost or processed multiple times, making this option unsuitable for enterprise workloads.

Question 129:

You are building a Logic App triggered by new blob uploads in Azure Blob Storage. You need real-time execution and exactly-once processing. 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 executes immediately when a new blob is created. Built-in concurrency controls and deduplication ensure exactly-once processing, making it suitable for invoice processing, document approvals, or automated file transformations. Logic Apps integrates retry policies and error handling to maintain workflow reliability.

Recurrence triggers poll at intervals, introducing latency and possible duplicate executions, making them unsuitable for real-time processing.

HTTP triggers require an external system to invoke the workflow, adding complexity and additional points of failure.

Service Bus queue triggers respond to queued messages rather than blob uploads. Using a queue adds operational overhead and latency compared to a direct blob trigger.

Question 130:

You are configuring Azure API Management (APIM) for internal APIs. You need secure authentication and auditable logging. 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 identity-based access control, allowing only authenticated and authorized users to access APIs. Diagnostic logging captures detailed request and response data, headers, and payloads, which can be sent to Log Analytics, Event Hubs, or Storage for auditing, monitoring, and compliance. This solution meets enterprise security requirements and aligns with AZ-204 best practices.

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

Basic authentication with local logs lacks centralized management, token-based security, and comprehensive auditing. Managing credentials and rotation becomes complex.

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

Question 131:

You are developing an Azure Function that ingests telemetry events from Event Hubs. You want parallel processing and to maintain 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 ensure parallel processing while maintaining order for messages within each partition. Each consumer handles its assigned partition, allowing high-throughput processing without breaking the per-device sequence. Azure Functions supports automatic scaling, distributing partitions among instances efficiently. Checkpointing guarantees at-least-once delivery, and retry policies handle transient failures. Dead-letter queues capture failed messages, preventing processing interruptions. This setup is ideal for IoT telemetry, sensor data, and real-time analytics.

A single partition with one consumer preserves order but significantly limits throughput. All messages are processed sequentially, which creates bottlenecks in high-volume scenarios, delaying downstream processing.

Multiple partitions without mapping consumers risk unordered processing, as multiple consumers may read from the same partition concurrently. This can lead to inconsistent analytics and incorrect device-level data processing.

Batch processing, ignoring partitionsss may increase throughput but sacrifices ordering. Events from the same device may be processed out of sequence, leading to inaccurate analytics or false alerts.

Question 132:

You are building an Azure App Service API that frequently reads 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, reducing RU consumption and improving read latency. Cached items can be refreshed automatically or on demand to maintain consistency. This approach is ideal for read-heavy workloads such as dashboards, telemetry, and product catalogs. It also aligns with serverless and scalable design best practices.

Automatic indexing improves query performance but does not reduce RU consumption or repeated reads, and does not provide in-memory caching.

Multi-region writes improve write availability across regions but do not enhance read latency in a single region. They also introduce added cost and complexity.

TTL deletes documents after a specific time, useful for ephemeral data, but it does not improve read performance or caching for frequently accessed items.

Question 133:

You are configuring an Azure Function to process messages from Service Bus Queues. You need reliable processing and avoid duplicates. 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 manually retry
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 and removes them only after successful completion. Duplicate detection prevents the same message from being processed multiple times. Dead-letter queues capture messages that fail repeatedly, ensuring at-least-once delivery. This approach is suitable for critical workloads such as telemetry ingestion, IoT pipelines, or financial transactions.

ReceiveAndDelete mode removes messages immediately, which is risky. If processing fails, messages are lost, making it unsafe for critical workloads.

Ignoring message locks and manually retrying increases operational complexity and risks processing duplicates. Developers would need to implement checkpointing and retry logic manually.

Multiple consumers with the ReceiveAndDelete mode allow parallel processing but compromise reliability. Messages could be lost or processed multiple times, making this unsuitable for enterprise-grade workloads.

Question 134:

You are building a Logic App triggered by new blob uploads in Azure Blob Storage. You need real-time execution and exactly-once processing. 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 fires immediately when a new blob is uploaded. Built-in concurrency controls and deduplication ensure exactly-once processing, making it suitable for automated workflows like invoice processing, file transformations, or approvals. Logic Apps provides retry policies and error handling for reliability.

Recurrence triggers poll periodically, introducing latency and potential duplicate execution, making them unsuitable for real-time workflows.

HTTP triggers require an external system to invoke the Logic App, adding complexity and potential failure points.

Service Bus queue triggers respond to messages rather than blob uploads. Using a queue requires extra logic to post a message for each blob, increasing overhead and latency compared to direct blob triggers.

Question 135:

You are configuring Azure API Management (APIM) for internal APIs. You need secure authentication and auditable logging. 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, allowing only authenticated and authorized users to access APIs. Diagnostic logging captures request and response details, headers, and payloads, which can be sent to Log Analytics, Event Hubs, or Storage for auditing, monitoring, and compliance. This approach meets enterprise security standards and aligns with AZ-204 best practices.

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

Basic authentication with local logs lacks centralized management, token-based security, and detailed auditing. Managing credentials and rotation becomes complex and error-prone.

IP restrictions limit access based on network location but do not verify identity. Users within allowed networks can still access APIs without authentication, making this insufficient for internal APIs requiring strict security.

Question 136

You are designing a high-throughput ingestion pipeline using Azure Event Hubs and Azure Functions. You must process events in parallel, maintain ordering per partition, and allow the system to scale automatically. Which architecture should you choose?

A) Single partition with one consumer
B) Random consumers reading all partitions
C) Multiple partitions with Azure Functions using built-in Event Hub triggers
D) Stateless functions polling a storage account

Answer: C) Multiple partitions with Azure Functions using built-in Event Hub triggers

Explanation:

Using multiple partitions with Azure Functions’ Event Hub trigger ensures that each function instance is automatically assigned a partition, allowing high-volume event processing while maintaining per-partition ordering. Azure Functions automatically handles scaling, checkpointing, and lease management, ensuring reliability during traffic spikes. This architecture is ideal for IoT telemetry and large-scale streaming workloads, as it balances output across Function instances.

A single partition with one consumer severely limits throughput. Event Hubs is designed for parallelism, and using only one partition prevents scaling. High-volume workloads will overwhelm the consumer and lead to delays or dropped events.

Random consumers reading from all partitions break ordering and introduce concurrency issues. Two consumers might read the same partition simultaneously, causing out-of-order execution, duplicate reads, and inconsistent state. This design also lacks coordinated checkpointing across consumers.

Stateless functions polling blobs do not work with real-time ingestion. Blob polling introduces latency, has no ordering guarantees, and provides no built-in scaling based on event volume. It is not suitable for streaming ingestion.

Question 137

You are building an API using Azure App Service that requires sub-millisecond reads from Cosmos DB while minimizing RU charges. Which feature provides the best performance?

A) Automatic indexing
B) Multi-master writes
C) Integrated distributed cache (Cosmos DB integrated cache)
D) Georedundant storage

Answer: C) Integrated distributed cache

Explanation:

Integrated Cosmos DB cache allows frequently accessed (hot) data to be served from an in-memory cache instead of the main database, dramatically reducing RU consumption and improving read latency. This is ideal for read-heavy applications such as product catalogs, dashboards, or telemetry analytics. It also provides an automatic refresh mechanism that keeps cached data consistent.

Automatic indexing improves query execution time but does not reduce the RU cost of repeated reads. It optimizes queries but does not reduce the overall load on the main database, especially for frequently accessed items.

Multi-master writes help with global write latency and multi-region synchronization, but do not improve read performance for local requests. It also increases cost and is used primarily for write optimization rather than read performance.

Georedundant storage is irrelevant to Cosmos DB performance. It improves disaster recovery but does not accelerate reads or reduce RU usage in typical API scenarios.

Question 138

You are building a message-driven workflow using Azure Service Bus and Azure Functions. You must guarantee that messages are not lost and duplicates are not processed. Which configuration should you use?

A) ReceiveAndDelete mode
B) Multiple consumers without lock renewal
C) Peek-lock mode with duplicate detection enabled
D) Stateless Functions reading via HTTP

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

Explanation:

Peek-lock mode ensures the message is locked while processing and only removed after successful completion. Combined with duplicate detection, this prevents accidental double-processing if the client retries. Azure Functions automatically renews locks during long operations, ensuring messages are never prematurely unlocked. This combination provides reliability and at least once processing.

ReceiveAndDelete mode does not lock messages. As soon as the message is received, it is removed from the queue. If the Function crashes during processing, the message is permanently lost. This makes it unsuitable for critical workflows.

Multiple consumers without lock renewal increases the chance of timeouts. If a message lock expires before processing finishes, it reappears on the queue, causing duplicate execution or triggering dead-lettering. This design is unreliable.

Stateless HTTP-triggered Functions do not integrate with Service Bus and cannot guarantee reliable message delivery or ordering. They lack lock management, retries, and deduplication features found in native queue triggers.

Question 139

You want a Logic App to immediately respond whenever a new file is uploaded to Azure Blob Storage. The workflow must run with exactly-once execution. Which trigger should you select?

A) Recurrence trigger
B) HTTP webhook trigger
C) Blob Storage trigger “When a blob is created”
D) Queue-based trigger

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

Explanation:

The Blob Storage trigger fires instantly when new blobs are added, providing real-time execution. Logic Apps maintain internal metadata to prevent duplicate triggers, ensuring exactly-once processing. This trigger is ideal for workflows involving file validation, document ingestion, or OCR pipelines.

Recurrence triggers introduce polling intervals, causing delays and increasing the chance of missing or duplicating events. They are not suitable for real-time event processing.

HTTP webhooks require custom external services to call the Logic App whenever a file is uploaded. This adds unnecessary complexity and eliminates the built-in guarantee of reliable, exactly-once triggering.

Queue-based triggers only activate when messages are pushed to a queue. This requires extra implementation to generate messages for each blob upload. It adds latency and increases operational overhead.

Question 140

You are securing a set of internal APIs using Azure API Management (APIM). The APIs require identity-based access and full audit logging. What should you implement?

A) IP filtering only
B) Basic authentication
C) Anonymous access + logging
D) OAuth 2.0 with Azure AD + diagnostic logs

Answer: D) OAuth 2.0 with Azure AD + diagnostic logs

Explanation:

OAuth 2.0 with Azure AD ensures secure, identity-based authentication. APIM will validate tokens, enforce scopes, and ensure only authorized users or applications can access internal APIs. Combined with diagnostic logging, organizations can collect request metadata, response details, authentication logs, latency metrics, and error traces for compliance.

IP filtering alone restricts access by network location but does not authenticate users or applications. Anyone within the network boundary could access the APIs without identity validation.

Basic authentication relies on static credentials and is insecure. Password rotations, credential exposure, and lack of token-based authorization make it unsuitable for enterprise APIs.

Anonymous access with logging allows full visibility but no authentication, meaning any request—legitimate or malicious—can reach the API. Logging alone does not provide security.