Microsoft AZ-204 Developing Solutions for Microsoft Azure Exam Dumps and Practice Test Questions Set 6 Q101-120

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Question 101:

You are developing an Azure Function that ingests high-volume telemetry data from Event Hubs. You need parallel processing 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 order within each partition. Each consumer processes only its assigned partition, preventing out-of-order processing that could occur with multiple consumers per partition. Azure Functions automatically supports scaling out as the number of partitions increases, improving throughput without compromising order. Checkpointing ensures at-least-once delivery, and retry policies handle transient failures gracefully. Dead-letter queues provide a mechanism for handling poisoned messages, enhancing fault tolerance. This approach is ideal for IoT telemetry, financial transactions, and real-time analytics where event sequence matters.

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

Multiple partitions without mapping consumers risk unordered message processing, as multiple consumers may read messages from partitions unpredictably. This can break device-specific sequences, causing inconsistent analytics or incorrect state updates.

Batch processing, ignoring partitions, may improve throughput but sacrifices order. Events from the same device could be processed out of sequence, which is unacceptable for use cases like telemetry aggregation, alerting systems, or financial transaction pipelines.

Question 102:

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, delivering millisecond-level reads. This reduces repeated database queries, lowers RU consumption, and minimizes operational cost. Cached data can be refreshed automatically or on demand to maintain consistency. This approach is ideal for read-heavy workloads like dashboards, telemetry, and product catalogs. Integration with Azure App Service allows developers to use caching seamlessly in serverless and scalable scenarios.

Automatic indexing improves query performance by creating indexes for documents. While it optimizes complex queries, it does not reduce RU consumption or repeated reads, nor does it provide in-memory caching. When building applications on Azure Cosmos DB, optimizing performance, cost, and scalability is critical, especially for workloads that require low-latency reads and high throughput. Cosmos DB provides several features to help developers achieve these goals, including automatic indexing, multi-region writes, TTL (Time-to-Live), and integrated caching. Each feature has specific benefits and trade-offs depending on the workload and access patterns.

Option A, automatic indexing, is one of the core capabilities of Cosmos DB. By default, Cosmos DB automatically indexes all properties of documents without requiring explicit schema definitions. This feature enables developers to perform queries efficiently without manually creating indexes, reducing development overhead and ensuring fast retrieval of data. Automatic indexing is particularly useful for dynamic workloads where query patterns may change over time, as the database continuously updates indexes in the background. However, while automatic indexing improves read performance, it can increase write costs because every write operation also updates the index. For high-write workloads, careful tuning of indexing policies may be necessary to balance query performance and storage or RU (Request Unit) costs.

Option B, multi-region writes, enables globally distributed applications to perform writes in multiple Azure regions simultaneously. This feature is crucial for scenarios that require low-latency writes across geographies, active-active architectures, and high availability. Multi-region writes allow applications to write data close to the user, reducing latency and ensuring that the system can tolerate regional failures without service interruption. However, while multi-region writes improve availability and write performance, they add complexity in terms of conflict resolution and consistency models. Developers must choose an appropriate consistency level, such as strong, bounded staleness, or eventual consistency, based on application requirements, to ensure correct data propagation across regions.

Option C, TTL (Time-to-Live), is a mechanism that automatically removes documents after a specified duration. TTL is particularly useful for workloads where data is only relevant for a limited time, such as session information, telemetry, or temporary cache entries. By automatically deleting expired documents, TTL reduces storage costs and ensures that queries only operate on relevant data, improving query performance. TTL can be applied at the container or item level and works seamlessly with indexing and query operations. However, TTL does not directly improve read latency for frequently accessed data; it primarily helps with cost optimization and data lifecycle management.

Option D, integrated Cosmos DB caching, addresses the need for low-latency reads, especially for frequently accessed data. Cosmos DB provides an integrated caching layer that stores hot or frequently requested data in memory, reducing the need to query the database for every request. Caching improves performance by minimizing read latency, reducing RU consumption, and offloading traffic from the database. This is particularly beneficial for workloads with repeated reads of the same data, such as product catalogs, user profiles, or reference data. Integrated caching works seamlessly with Cosmos DB and automatically manages cache expiration and refresh policies. Unlike TTL, caching does not remove data permanently; it keeps copies in memory for fast access, while TTL only deletes data after it expires.

In summary, each feature addresses a different aspect of performance, cost, and scalability in Cosmos DB. Automatic indexing improves query performance but may increase write costs. Multi-region writes enhance availability and reduce write latency across geographies but require careful consistency management. TTL helps optimize storage and query efficiency for ephemeral data. Integrated caching provides low-latency access for frequently read data, improving responsiveness and reducing request costs. Together, these features allow developers to design highly performant, cost-efficient, and globally available applications tailored to specific workloads.

Multi-region writes enhance write availability across regions but do not optimize read latency in a single region. It also increases replication costs and complexity, making it unsuitable for read-heavy workloads.

TTL automatically deletes documents after a defined interval. It helps with ephemeral data management but does not cache frequently accessed items or improve read performance. TTL is primarily for data lifecycle management, not performance optimization.

Question 103:

You are configuring an Azure Function to process messages from Service Bus Queues. You need reliable message processing to avoid duplicates and to handle poison messages. 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 temporarily locks messages while the function processes them. Messages are removed from the queue only after successful processing. Duplicate detection ensures the same message is not processed multiple times, and dead-letter queues handle messages that repeatedly fail. This approach guarantees at least once delivery, high reliability, and operational safety. Azure Functions integrates retry policies and checkpointing to handle transient failures without compromising message order. This is ideal for critical workloads such as financial transactions, telemetry ingestion, or IoT pipelines.

ReceiveAndDelete mode immediately removes messages upon retrieval. Failed processing causes message loss, making this unsafe for critical workloads. It is only suitable for idempotent or non-critical scenarios.

Ignoring message locks and retrying manually increases complexity and risk. Developers must implement checkpointing, duplicate detection, and retries themselves, which is operationally challenging.

Multiple consumers with the ReceiveAndDelete mode allow parallel processing but compromise reliability. Messages are deleted immediately, so failures result in lost messages, and duplicates may occur. This approach prioritizes throughput over correctness.

Question 104:

You are building a Logic App that triggers when invoices 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 is a push-based event trigger that executes immediately when a blob is created. 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 robust and maintainable. This trigger is ideal for workflows such as invoice processing, document approvals, and file transformations. It reduces operational complexity and aligns with serverless and event-driven design principles in AZ-204.

Recurrence triggers poll storage at intervals, introducing latency and potential duplication. Workflows may run unnecessarily if no new blobs exist, increasing cost. When designing an automated workflow in Azure Logic Apps or Azure Functions to process files uploaded to Azure Blob Storage, choosing the correct trigger is critical for ensuring timely, efficient, and reliable execution. The choice of trigger determines how the workflow is initiated and how quickly it can respond to events, directly impacting overall system performance and operational efficiency.

Option C, using the Blob Storage trigger “When a blob is created,” is the recommended approach for this scenario. This trigger is event-driven, meaning the workflow starts automatically as soon as a new blob is uploaded to the container. Unlike scheduled or polling mechanisms, event-driven triggers eliminate unnecessary delays and resource overhead because the workflow does not need to continuously check for new files. This ensures near-real-time processing of uploaded files, which is particularly important in scenarios such as invoice processing, telemetry ingestion, or document workflow automation where immediate action is required. The Blob Storage trigger integrates seamlessly with Azure Functions and Logic Apps, supporting robust features such as retry mechanisms, error handling, and scalable parallel processing.

Option A, recurrence trigger with polling, involves scheduling the workflow to run at fixed intervals, such as every minute or every five minutes, and then checking the blob container for new files. While this approach can eventually detect new uploads, it introduces latency because files are only processed when the workflow executes. Additionally, frequent polling can increase operational costs, as the workflow consumes compute and storage resources even when no new files are present. This approach is less efficient and less responsive compared to an event-driven trigger that reacts immediately to new files.

Option B, an HTTP trigger called externally, allows workflows to be initiated on demand by external applications or systems via an HTTP request. While this approach is useful when external systems control the workflow, it is less suitable for automatically processing newly uploaded files in Blob Storage. The workflow would require an additional layer or service to detect uploads and call the HTTP endpoint, adding complexity and potential points of failure. It also introduces additional latency and operational overhead compared to a native Blob Storage trigger.

Option D, a Service Bus queue trigger, is appropriate for processing messages in a queue rather than files in Blob Storage. Service Bus triggers provide event-driven processing for messaging scenarios and can guarantee at-least-once delivery. However, they are not designed to detect new files directly in blob storage. Using a queue trigger would require additional integration, such as sending a message to a queue every time a file is uploaded, adding complexity without the benefit of a native, direct event-driven workflow.

In conclusion, the Blob Storage trigger “When a blob is created” is the optimal choice for workflows that need to process files immediately upon arrival in a storage container. It ensures low-latency, event-driven execution, integrates seamlessly with Azure serverless technologies, and supports robust scaling and error-handling capabilities. Other triggers either introduce unnecessary latency, additional complexity, or are not designed for direct file-based event detection. By using this trigger, organizations can build responsive, efficient, and reliable automated workflows for file processing in Azure.

HTTP triggers rely on an external service to initiate the workflow, adding complexity, latency, and additional failure points.

Service Bus queue triggers respond to queued messages rather than blob uploads. Extra logic is needed to send a message per blob, increasing latency and operational overhead.

Question 105:

You are configuring an Azure API Management (APIM) instance for internal APIs. You need enforced authentication and auditable request logs. 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, including headers, payloads, and metadata. Logs 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 traceable and auditable API access, meeting operational and compliance requirements. This is aligned with AZ-204 exam objectives for secure internal APIs.

When developing an API that exposes sensitive financial data, two primary concerns must be addressed: ensuring that only authorized applications or users can access the API, and capturing detailed logs for auditing and compliance purposes. Failure to properly secure the API or maintain comprehensive logging can result in unauthorized access, data breaches, regulatory violations, and operational challenges. Therefore, selecting the correct authentication and monitoring strategy is critical for protecting sensitive data in a production environment.

Option D, using OAuth 2.0 with Azure Active Directory (Azure AD) and diagnostic logging, is the recommended solution. OAuth 2.0 is an industry-standard protocol for authorization, allowing applications to securely obtain access tokens that verify their identity and access permissions without transmitting user credentials directly. By integrating OAuth 2.0 with Azure AD, organizations can enforce centralized identity and access management. Only applications or users registered in Azure AD and granted the required permissions can successfully call the API. This ensures that unauthorized parties cannot access sensitive data, reducing the risk of data leakage or malicious activity. Azure AD also provides advanced features such as token expiration, scopes, roles, and conditional access policies, which allow for fine-grained control over API access.

Diagnostic logging complements OAuth 2.0 authentication by capturing detailed information about every API request. This includes the caller’s identity, timestamps, request headers, payloads, response codes, and any errors encountered. Comprehensive logging provides an audit trail, which is essential for regulatory compliance with frameworks such as PCI DSS, GDPR, or SOX. Logs can be centrally collected in services such as Azure Monitor, Log Analytics, or Event Hubs, enabling real-time monitoring, alerting, and post-incident analysis. With this approach, organizations can quickly detect and respond to suspicious activity, track usage patterns, and maintain accountability for all API interactions.

Other options are less effective for securing sensitive APIs. Option A, anonymous access with logging, allows anyone to call the API while only recording requests. Although logging may capture who accessed the API, it does not prevent unauthorized access, leaving sensitive financial data exposed. Option B, basic authentication with local logs, requires transmitting usernames and passwords with each request. Credentials are often encoded but not encrypted, making them vulnerable to interception. Local logging is also insufficient for enterprise-grade auditing and may be lost if the server restarts or encounters failures. Option C, IP restrictions only, limits access based on network addresses but does not provide user or application-level identity verification. Attackers within allowed IP ranges could still access the API, and IP restrictions do not offer granular permission control or comprehensive audit logging.

In summary, combining OAuth 2.0 with Azure AD for authentication and enabling diagnostic logging provides a secure, scalable, and auditable approach for exposing sensitive APIs. This configuration ensures that only authorized users and applications can access the API while maintaining a complete and centralized record of all API interactions. It is the most suitable choice for enterprise-grade API security, protecting sensitive financial data and supporting regulatory compliance requirements.

Anonymous access with logging captures requests but does not enforce security, 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. Credential rotation and access monitoring are more complex.

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

Question 106:

You are developing an Azure Function to process messages from Event Hubs. You need high throughput 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:

Using multiple partitions with one consumer per partition ensures parallel processing while maintaining message order within each partition. Each consumer handles only its assigned partition, allowing scalable, high-throughput processing. Azure Functions manages checkpointing to guarantee at-least-once delivery, and dead-letter queues handle messages that repeatedly fail. This architecture is ideal for IoT telemetry, financial transactions, or any scenario requiring per-device sequence integrity.

A single partition with one consumer guarantees order but creates a bottleneck for high-volume workloads. All messages are processed sequentially, limiting throughput and increasing latency for real-time analytics.

Multiple partitions without mapping consumers can lead to unordered message processing, because multiple consumers might read from partitions randomly. This can break device-specific sequences and lead to inconsistent analytics or monitoring errors.

Batch processing, ignoring partitions, increases throughput but sacrifices ordering. Messages from the same device may be processed out of sequence, making it unsuitable for workloads requiring strict event ordering.

Question 107:

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 low-latency reads and reducing RU consumption. Cached data can be refreshed automatically on demand, maintaining consistency while improving performance. This is ideal for read-heavy workloads like dashboards, telemetry, and product catalogs.

Automatic indexing improves query performance for complex queries but does not reduce repeated reads or RU consumption. Indexing does not provide caching benefits. When building applications on Azure Cosmos DB, optimizing read performance while controlling costs is a primary concern, especially for workloads that frequently access the same data. Azure Cosmos DB provides several features to enhance performance, including automatic indexing, multi-region writes, TTL (Time-to-Live), and integrated caching. Among these options, integrated Cosmos DB caching is particularly important for minimizing latency and improving the responsiveness of applications that rely on frequently accessed data.

Option D, integrated Cosmos DB caching, is a mechanism designed to store frequently accessed (“hot”) data in memory for fast retrieval. By caching popular items, applications reduce the need to query the underlying Cosmos DB storage repeatedly, which significantly decreases read latency and reduces Request Unit (RU) consumption. This is especially beneficial for read-heavy workloads such as product catalogs, dashboards, session data, or frequently queried reference tables. Integrated caching works seamlessly with Cosmos DB and automatically manages cache consistency and expiration, ensuring that applications receive up-to-date information without manually implementing caching logic. Caching also improves scalability because it reduces the load on Cosmos DB, allowing the database to handle more requests efficiently and lowering operational costs associated with high read throughput.

Option A, automatic indexing, improves query performance by maintaining indexes on all document properties without requiring explicit schema definitions. While automatic indexing accelerates queries and makes searching more efficient, it does not directly reduce read latency for frequently accessed items. Indexing is primarily beneficial for complex queries and dynamic data structures, but repeated reads of the same data still require hitting the database unless caching is implemented. Additionally, automatic indexing increases write costs because every write operation must also update the index, which can impact performance for write-heavy workloads.

Option B, multi-region writes, enhances availability and reduces write latency by allowing data to be written in multiple regions simultaneously. This feature is crucial for globally distributed applications that require low-latency writes close to end-users. Multi-region writes also provide resilience against regional failures. However, this feature primarily optimizes write performance and global availability rather than improving the speed of read operations. While multi-region writes can complement caching in a distributed application, they do not reduce read latency for frequently accessed data.

Option C, TTL (Time-to-Live), is a mechanism for automatically deleting documents after a specified duration. TTL is useful for managing ephemeral data such as session states, telemetry, or temporary cache entries. By removing expired documents automatically, TTL helps optimize storage costs and keeps queries efficient by reducing the dataset size. However, TTL does not enhance read performance for frequently accessed data; it only controls data lifecycle management and storage optimization.

In summary, integrated Cosmos DB caching (Option D) is the most effective approach for minimizing read latency and improving performance for frequently accessed data. It complements features like automatic indexing, multi-region writes, and TTL by focusing specifically on reducing the need for repeated database queries, decreasing RU consumption, and providing fast, in-memory access to hot data. Automatic indexing improves query efficiency, multi-region writes optimize write latency and availability, and TTL manages data lifecycle, but none of these features directly address the performance improvements that caching provides for repetitive reads. Leveraging integrated caching allows developers to build highly responsive, cost-efficient applications that can scale seamlessly without compromising on latency or throughput.

Multi-region writes improve write availability globally but do not optimize read latency in a single region. They also increase replication costs and complexity.

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

Question 108:

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 messages during processing and deletes them only after successful completion. Duplicate detection prevents the same message from being processed multiple times, and retry policies handle transient failures. Dead-letter queues handle messages that repeatedly fail, ensuring reliability and consistency.

ReceiveAndDelete mode removes messages immediately. Any failure results in lost messages, making it unsafe for critical workloads.

Ignoring message locks and manually retrying adds complexity and increases the risk of errors. Developers must implement checkpointing and duplicate detection themselves.

Multiple consumers with the ReceiveAndDelete mode allow parallel processing but sacrifice reliability. Messages may be lost on failure, and duplicate detection is not supported.

Question 109:

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 fires immediately when a new blob is uploaded, providing low-latency, real-time execution. Concurrency controls and built-in deduplication ensure exactly-once processing, making it ideal for invoice processing or automated file transformations.

Recurrence triggers poll storage at intervals, introducing latency and potential duplicate executions.

HTTP triggers rely on external services to initiate workflows, adding complexity and potential points of failure.

Service Bus queue triggers respond to messages, not blob uploads. Using a queue requires additional logic to send messages for each upload, increasing latency and operational overhead.

Question 110:

You are configuring an Azure API Management (APIM) instance for internal APIs. You need enforced authentication and auditable request logs. 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 users can access APIs. Diagnostic logging captures detailed request and response information, headers, and payloads, which can be sent to Log Analytics, Event Hubs, or Storage for auditing and monitoring. This approach meets enterprise security and compliance requirements.

Anonymous access with logging captures requests but does not enforce authentication. APIs remain exposed to unauthorized users.

Basic authentication with local logs is less secure and lacks centralized management and auditing. Credential rotation and monitoring are complex.

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

Question 111:

You are developing an Azure Function that ingests messages from Event Hubs. You want automatic scaling to handle varying traffic while maintaining message order per partition. Which architecture should you implement?

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

Answer: B) Multiple partitions with one consumer per partition

Explanation:

Using multiple partitions with one consumer per partition ensures that each consumer handles a specific partition, maintaining order within that partition while allowing parallel processing across partitions. Azure Functions automatically scales out function instances based on load, and each instance can claim ownership of partitions to balance processing. Checkpointing ensures at-least-once delivery, and transient failures are retried. This architecture is ideal for telemetry ingestion, IoT devices, and real-time analytics where ordering is critical.

A single partition with one consumer preserves order but limits throughput. High-volume workloads may become bottlenecked, increasing latency for processing and downstream systems.

Multiple partitions with multiple consumers without mapping consumers risks out-of-order processing, because multiple consumers may read from the same partition concurrently. This can lead to inconsistent telemetry or state calculations.

Batch processing, ignoring partitions,simproves throughput but sacrifices ordering guarantees. Messages from the same device may be processed out of sequence, causing errors in analytics or monitoring pipelines.

Question 112:

You are building an Azure App Service API that frequently reads data from Cosmos DB. You want fast reads and lower 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 on-demand or automatically, ensuring consistency while improving performance for read-heavy workloads like dashboards, telemetry, and product catalogs. This solution aligns with serverless best practices and reduces operational costs by minimizing database requests.

Automatic indexing improves query efficiency but does not reduce RU consumption or repeated reads. It does not provide in-memory caching for hot data.

Multi-region writes improve write availability but do not improve read latency in a single region. They increase replication costs and complexity, making them unsuitable for optimizing read-heavy workloads.

TTL deletes documents after a specific time, which is useful for ephemeral data management but does not help with read performance or caching of frequently accessed data.

Question 113:

You are configuring an Azure Function to process messages from Service Bus Queues. You need reliable message processing to avoid duplicate processing. Which approach should you use?

A) ReceiveAndDelete mode with one 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 locks messages temporarily while the function processes them. The message is deleted only after successful processing. Enabling duplicate detection ensures that the same message is not processed multiple times within a time window. Dead-letter queues capture messages that consistently fail, allowing manual remediation. This approach ensures at least once delivery and operational reliability for critical workloads like financial transactions or telemetry ingestion.

ReceiveAndDelete mode immediately removes messages, making it unsafe if processing fails, as messages may be lost. This is suitable only for non-critical or idempotent workloads.

Ignoring message locks and retrying manually adds operational complexity and increases the risk of processing 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-grade workflows.

Question 114:

You are building a Logic App triggered by new blob uploads in 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 fires immediately when a new blob is uploaded. Built-in concurrency controls and deduplication ensure exactly-once processing, making it suitable for invoice processing, file transformations, or document approvals. Logic Apps integrates retry policies and error handling for robust workflow execution.

Recurrence triggers poll storage periodically, introducing latency and potential duplicates, which is inefficient for real-time processing.

HTTP triggers rely on external systems to initiate the workflow, adding complexity and extra points of failure.

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

Question 115:

You are configuring Azure API Management (APIM) for internal APIs. You need secure authentication and detailed 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 authorized users can 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 configuration meets enterprise security requirements and aligns with AZ-204 best practices for secure internal APIs.

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

Basic authentication with local logs lacks centralized management, token-based security, and robust auditing. Credential rotation and monitoring are complex.

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

Question 116:

You are developing an Azure Function to process messages from Event Hubs. You want high throughput and per-device ordering. 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 across partitions while maintaining message order within each partition. Each consumer handles its assigned partition, allowing scaling to handle high-volume events. Checkpointing ensures at-least-once delivery, while retry policies handle transient failures. Dead-letter queues capture failed messages, preventing them from blocking other events. This design is ideal for IoT telemetry or financial event processing where order matters.

A single partition with one consumer guarantees order but limits throughput, creating bottlenecks for high-volume workloads. Processing is sequential, which increases latency for real-time analytics.

Multiple partitions without mapping consumers risk unordered message processing. Multiple consumers may read from the same partition unpredictably, potentially causing inconsistent device-level data.

Batch processing, ignoring partition,s increases throughput but sacrifices ordering. Events from the same device could be processed out of sequence, leading to inaccurate analytics or alerts.

Question 117:

You are building an Azure App Service API that reads frequently accessed data from Cosmos DB. You want low-latency reads and reduced RU usage. 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 low-latency reads and reducing RU consumption. Cached items can be automatically refreshed or invalidated to maintain data consistency, improving performance for read-heavy workloads such as 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 hot data.

Question 118:

You are configuring an Azure Function to process messages from Service Bus Queues. You need reliable processing and duplicate prevention. 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, and messages are removed only after successful completion. Duplicate detection prevents the same message from being processed multiple times. Dead-letter queues capture messages that repeatedly fail, allowing for manual inspection. This ensures at least once delivery and is suitable for critical workloads like telemetry ingestion or financial transactions.

ReceiveAndDelete mode immediately removes messages from the queue. Any processing failure results in message loss, making it unsafe for critical workloads.

Ignoring message locks and manually retrying increases complexity and risk. Developers must implement checkpointing and duplicate detection themselves.

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

Question 119:

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 invoice processing, document approvals, or file transformations. Logic Apps supports retry policies and error handling to maintain workflow reliability.

Recurrence triggers poll at intervals, introducing latency and possible duplicate processing.

HTTP triggers require external systems to invoke the workflow, adding complexity and potential failure points.

Service Bus queue triggers respond to messages instead of blob uploads. Using a queue requires additional logic to post messages for each blob, increasing overhead and latency.

Question 120:

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 request and response details, headers, and payloads, which can be sent to Log Analytics, Event Hubs, or Storage for auditing and monitoring. This meets enterprise security and compliance requirements.

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 rotations becomes complex.

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