Microsoft AZ-204 Developing Solutions for Microsoft Azure Exam Dumps and Practice Test Questions Set 1 Q1-20

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Question 1:

You are developing an Azure Function that will process messages from an Azure Storage Queue. The function must scale automatically based on the number of messages in the queue. Which hosting plan should you choose?

A) Consumption Plan
B) Premium Plan
C) App Service Plan
D) Dedicated Plan

Answer: A) Consumption Plan

Explanation:

The Consumption Plan is specifically designed for serverless Azure Functions that need automatic scaling based on demand. When using a Storage Queue trigger, the platform dynamically provisions compute resources to handle incoming messages, ensuring the function scales up as the queue grows and scales down when idle. This plan is cost-efficient because you only pay for execution time and resources consumed by your function, not for idle time.

In contrast, the Premium Plan provides additional features like VNET integration, longer execution times, and enhanced performance, but it is primarily suited for enterprise-grade workloads with higher and predictable usage patterns. The App Service Plan and Dedicated Plan are more traditional hosting models that allocate a fixed set of resources to your app, which means scaling is manual or based on pre-defined rules.

The Consumption Plan also supports a maximum of 1.5 GB memory per function and execution timeout limits (default 5 minutes, can extend to 10 minutes). It is ideal for event-driven workloads where scalability and cost efficiency are critical. Features like provisioned concurrency are not available in the Consumption Plan but can be addressed in the Premium Plan if cold-start latency becomes an issue.

Azure Functions integrate seamlessly with Azure Storage Queues, Event Hubs, Cosmos DB Change Feed, and HTTP triggers, making them suitable for microservices and asynchronous processing. When designing your function, you should also handle transient errors, implement retries, and consider dead-letter queues for failed messages to ensure reliability. Monitoring is available via Azure Application Insights, allowing you to track execution counts, failures, and performance.

Understanding the differences between hosting plans is crucial for AZ-204 exam candidates because it affects scalability, cost, performance, and integration options. Choosing the Consumption Plan aligns with the requirement to scale automatically in response to the queue load.

Question 2:

You are building an API using Azure App Service. The API must authenticate users using Azure Active Directory (AAD) and restrict access to specific roles. Which approach is best?

A) Implement OAuth 2.0 with AAD and use role-based access control (RBAC)
B) Use API keys stored in App Settings
C) Use basic authentication with username and password
D) Use client-side JWT tokens only

Answer: A) Implement OAuth 2.0 with AAD and use role-based access control (RBAC)

Explanation:

For enterprise-grade applications hosted in Azure App Service, integrating Azure Active Directory (AAD) using OAuth 2.0 / OpenID Connect provides secure authentication and authorization. OAuth 2.0 allows the API to issue access tokens that can be validated on the server side, enabling secure communication without storing credentials.

RBAC ensures that authenticated users have the correct permissions. You can define roles in AAD (e.g., Admin, Reader) and assign them to users or groups. The API can then inspect the token claims to enforce authorization. This approach is superior to storing API keys or using basic authentication, which is less secure, cannot easily integrate with AAD, and does not support roles efficiently.

Client-side JWT tokens alone (option D) are insecure if the API does not validate them against a trusted issuer. AAD integration ensures tokens are signed, validated, and include necessary claims like roles and group membership.

When implementing this solution, the developer should:

Register the API in Azure AD App Registrations.

Configure required permissions and scopes.

Implement token validation middleware in the API code.

Map roles and claims in AAD to API roles.

This approach also enables features like single sign-on (SSO), conditional access policies, and multi-factor authentication. For AZ-204, understanding how to secure APIs using AAD and role-based access is essential.

Question 3:

You are designing a solution that stores large amounts of unstructured data in Azure. You need a storage option that provides hot, cool, and archive tiers for cost optimization. Which Azure storage service should you use?

A) Azure Blob Storage
B) Azure Files
C) Azure Table Storage
D) Azure Queue Storage

Answer: A) Azure Blob Storage

Explanation:

Azure Blob Storage is the primary service for storing unstructured data, including text, images, videos, and backups. One of its critical features is tiered storage: Hot, Cool, and Archive. These tiers allow organizations to optimize costs based on access patterns.

Hot Tier: Designed for data accessed frequently. It has higher storage costs but lower access costs. Use it for active applications and workloads requiring immediate access.

Cool Tier: Suitable for infrequently accessed data. Storage costs are lower than Hot, but access operations are more expensive. Use it for short-term backup or infrequently read files.

Archive Tier: Extremely low-cost storage for rarely accessed data. Data retrieval can take hours, making it ideal for compliance, long-term backups, or archival purposes.

Azure Blob Storage supports block blobs, page blobs, and append blobs. Block blobs are ideal for large files such as media content; page blobs support random read/write operations, often used for VHDs; append blobs are designed for logging.

Other options:

Azure Files provides SMB/NFS shares, best for lifting and shifting on-premises file shares. It does not provide tiered storage options like Blob Storage.

Azure Table Storage is for NoSQL structured data with key-value access. It does not support hot/cool/archive tiers.

Azure Queue Storage is designed for messaging and decoupled communication between services, not for storing unstructured data.

Additional considerations:
Blob Storage integrates with Azure CDN, enabling faster content delivery. You can also implement Lifecycle Management policies to automatically move blobs between tiers based on age or access patterns. Security features include encryption at rest, SAS tokens, and role-based access.

For AZ-204, developers must know how to choose storage solutions based on data type, cost, and performance requirements. Blob Storage is ideal for unstructured, tiered, and large datasets.

Question 4:

You need to implement an Azure Logic App that triggers when a new file is uploaded to OneDrive, processes the file, and sends a notification. Which trigger should you use?

A) Recurrence trigger
B) HTTP Request trigger
C) OneDrive trigger “When a file is created”
D) Service Bus trigger

Answer: C) OneDrive trigger “When a file is created”

Explanation:

Azure Logic Apps is a serverless workflow automation service designed to integrate apps, data, services, and systems. The most efficient way to react to events like a file upload is to use a built-in connector trigger specific to the service—in this case, OneDrive.

The OneDrive “When a file is created” trigger monitors the specified folder in real time and triggers the Logic App workflow when a new file is uploaded. The workflow can then process the file (e.g., transform data, extract information, or copy it to another storage account) and send notifications via email, Teams, or other connectors.

Other triggers:

Recurrence trigger runs on a schedule, not event-driven. It would poll for changes instead of reacting in real time.

The HTTP Request trigger is for workflows initiated by an HTTP call, unsuitable for OneDrive events.

Service Bus trigger listens to messages on a queue or topic, not files in OneDrive.

Key considerations for the AZ-204 exam:

Logic Apps support both polling triggers and push triggers. Push triggers (like “When a file is created”) are efficient and cost-effective because they execute only when events occur.

Connectors can require authentication; for OneDrive, OAuth 2.0 authentication is used.

Logic Apps also support conditions, loops, error handling, and approvals, enabling complex workflows without writing full code.

For high-volume workloads, integration accounts may be needed for XML, B2B, or enterprise integration scenarios.

Understanding how to select and configure triggers is crucial for exam scenarios related to integration and automation.

Question 5:

You are developing an Azure App Service web app that will store sensitive customer data. Which practice is most suitable for securing secrets and connection strings in your application?

A) Store secrets directly in code
B) Use Azure Key Vault and reference secrets in App Service
C) Store secrets in a local configuration file on the App Service instance
D) Encrypt secrets with a hard-coded key

Answer: B) Use Azure Key Vault and reference secrets in App Service

Explanation:

Azure Key Vault is a managed service for securely storing secrets, keys, and certificates. It provides encryption, access control, and auditing capabilities. Referencing secrets in App Service ensures that your application never stores sensitive data in code or configuration files, reducing the risk of accidental exposure.

Key Vault features:

Access policies and RBAC: Control which users, groups, or applications can access secrets.

Managed identities: App Service can use a system-assigned managed identity to securely retrieve secrets without embedding credentials in code.

Versioning and auditing: Track secret versions and access for compliance.

Storing secrets directly in code (A) is highly insecure and violates security best practices.

Local configuration files (C) are prone to theft if the VM is compromised or if the app is misconfigured.

Hard-coded encryption keys (D) are insecure and difficult to rotate, leading to potential breaches.

For AZ-204 candidates, it is essential to know how to integrate Azure Key Vault with App Service, retrieve secrets at runtime, and implement secure identity-based access without exposing credentials. Best practices include rotating secrets regularly, enabling soft-delete and purge protection, and monitoring secret usage through Azure Monitor.

Question 6:

You are developing an Azure Function that connects to a SQL Database. The function must retry transient failures automatically. Which approach is most suitable?

A) Implement retry logic in the function using exponential backoff
B) Ignore errors and let the function fail
C) Use a static sleep loop without retry policies
D) Implement retries only in SQL Server

Answer: A) Implement retry logic in the function using exponential backoff

Explanation:

Cloud applications often experience transient failures, such as network timeouts, throttling, or temporary unavailability of services. Implementing retry logic ensures resilience and reliability. Exponential backoff is a recommended pattern: it retries operations after increasing delays (e.g., 1s, 2s, 4s) to avoid overwhelming the service.

Azure SDKs often provide built-in retry mechanisms, but developers can implement custom policies for fine-grained control. Considerations include:

Maximum number of retries

Initial and maximum delay intervals

Logging and alerting failed attempts.

Differentiating between transient vs. non-transient errors

Ignoring errors or using static sleep loops is inefficient and prone to failure. Implementing retries only in SQL Server is impossible because transient errors occur at the client-service level, not just on the server.

For AZ-204, candidates must understand how to handle transient faults in Azure services, using patterns like exponential backoff, circuit breakers, and idempotent operations. Libraries like Polly (for .NET) or built-in SDK retry policies simplify implementation.

Question 7:

You are designing an Azure API Management (APIM) solution for internal APIs. You need to restrict access to only users from your organization and log all requests for auditing. Which features should you use?

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

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

Explanation:

Azure API Management (APIM) provides secure, scalable API gateways. To restrict internal access:

OAuth 2.0 with Azure Active Directory (AAD) ensures that only authenticated users from your organization can access APIs. APIM validates tokens and enforces access policies.

Diagnostic logging allows tracking of all API requests, including headers, payload, response times, and errors. Logs can be sent to Azure Monitor, Log Analytics, or Event Hubs for auditing and analysis.

Anonymous access does not restrict users.

Basic authentication is less secure and does not integrate with AAD.

IP restrictions alone cannot enforce identity-based access or auditing.

For AZ-204, understanding APIM security policies, authentication integration, logging, and monitoring is critical. Implementing OAuth 2.0 with AAD and enabling diagnostic logging ensures secure, auditable, and compliant API management.

Question 8:

You are developing a serverless application using Azure Functions. The function needs to process messages from an Azure Service Bus queue and must ensure at-least-once delivery. Which setting should you use in your function?

A) Enable peek-lock mode on the Service Bus trigger
B) Use ReceiveAndDelete mode on the Service Bus trigger
C) Disable message sessions on the queue
D) Set the maxConcurrentCalls to 1

Answer: A) Enable peek-lock mode on the Service Bus trigger

Explanation

In Azure Service Bus, ensuring at-least-once delivery means that a message is not removed from the queue until the application explicitly completes it. The peek-lock mode is the default and recommended mechanism for this scenario. When a message is received in peek-lock mode, it is temporarily locked for processing, and if the function successfully processes the message, it calls Complete() to remove it from the queue. If processing fails or the function crashes before completing, the message lock eventually expires, and the message becomes available for reprocessing. This guarantees that the message will be processed at least once, even in the event of transient failures.

Option B, ReceiveAndDelete, immediately removes the message from the queue upon receipt. If the function fails after receiving the message, it is lost, violating the at-least-once delivery requirement.

Option C, disabling message sessions, is irrelevant for the delivery guarantee. Sessions are used to guarantee message ordering for related messages, not for delivery semantics.

Option D, setting maxConcurrentCalls to 1, only controls the concurrency of message processing. While it can reduce the risk of message processing overlap, it does not inherently ensure at-least-once delivery.

By enabling peek-lock mode and implementing proper exception handling and retries in your Azure Function, you ensure reliable processing and maintain the integrity of your message workflow, meeting the requirements for at-least-once delivery.

Question 9:

You are building an Azure App Service web app that interacts with Azure Cosmos DB. You want to minimize costs while maintaining low-latency reads for frequently accessed data. Which feature should you enable?

A) Cosmos DB Automatic Indexing
B) Cosmos DB Multi-region Writes
C) Cosmos DB Change Feed
D) Cosmos DB Integrated Cache

Answer: D) Cosmos DB Integrated Cache

Explanation

Azure Cosmos DB Integrated Cache is specifically designed to reduce latency and minimize costs for read-heavy workloads. When enabled, frequently accessed items are cached in-memory within Cosmos DB, allowing your application to serve reads without querying the underlying storage repeatedly. This reduces request units (RUs) consumed, directly lowering operational costs while improving response times for frequently requested data.

Option A – Automatic Indexing: While automatic indexing improves query performance by maintaining indexes on all properties, it does not reduce read costs or provide caching for frequently accessed data. It is more relevant for query efficiency rather than cost reduction.

Option B – Multi-region Writes: This feature allows writes to occur in multiple regions to improve availability and latency for write operations. While it enhances global scalability, it does not address low-latency reads or cost minimization for frequently accessed items.

Option C – Change Feed: Change Feed allows you to track changes in Cosmos DB for downstream processing or event-driven architectures. It is useful for real-time analytics or replication, but does not provide caching or reduce read latency.

Option D – Integrated Cache: By storing frequently accessed documents in memory, Integrated Cache provides faster read performance and reduces the number of RU charges per request. It is fully managed, transparent to the application, and configurable in size, making it an optimal choice for cost-efficient, low-latency access.

From a best-practice perspective, combining Integrated Cache with proper indexing and partitioning strategies ensures your Cosmos DB workload is both cost-effective and performant. Applications such as dashboards, user profiles, or product catalogs that frequently access the same data benefit most from caching.

In summary, enabling Cosmos DB Integrated Cache optimizes read latency for hot data while minimizing operational costs, making it the correct choice for this scenario.

Integrated Caching. Integrated caching provides an in-memory cache for frequently read items, reducing direct requests to Cosmos DB and lowering RU consumption. It improves latency, enabling millisecond-level reads even under heavy load. Cached items can have expiration policies, and the cache can be regionally distributed. This is cost-effective for read-heavy workloads and avoids unnecessary scaling of Cosmos DB throughput.

Automatic indexing improves query performance but does not reduce RU consumption significantly. Every query still hits the underlying database, so it doesn’t provide the same performance boost as caching for hot data. Multi-region writes improve availability and write latency globally but increase costs due to replication. This feature doesn’t reduce read latency for frequently accessed items in a single region, making it inefficient for cost-sensitive scenarios.

TTL automatically deletes items after a set duration, which is useful for temporary data. However, it does 

Question 10

You are designing an Azure Function that triggers on blob uploads. The function must process files in parallel but preserve the order of file processing within each blob container. Which approach should you choose?

A) Use multiple queues per container to distribute processing
B) Use a single queue per blob container and process messages sequentially with a single consumer
C) Use parallel consumers on any queue without controlling assignment
D) Poll blobs sequentially without queues

Answer: B) Use a single queue per blob container and process messages sequentially with a single consumer

Explanation 

Maintaining message order while processing files from a blob container is a common requirement in event-driven architectures. Azure Functions can be triggered by blob storage events, but to reliably enforce ordering, you need to control the message flow.

Option B is the correct approach. By creating a single queue per blob container, you ensure that all events from that container are pushed into the same message stream. Processing messages sequentially with a single consumer guarantees that each blob is handled in the exact order it was uploaded. This preserves data consistency and ensures workflows that depend on ordering execute correctly. While this limits parallelism per container, it provides correctness and is a widely recommended pattern for ordered processing scenarios.

Option A – Multiple queues per container may allow some parallelism, but it breaks the natural sequence of messages. Events pushed to different queues could be processed out of order, leading to inconsistencies in workflows that require ordered execution.

Option C – Parallel consumers with any queue introduces race conditions. If multiple consumers process messages from the same queue without coordination, the order in which messages are handled becomes unpredictable. Azure Functions can scale out dynamically, so relying on multiple consumers without queue partitioning or ordering guarantees will likely violate the sequence requirement.

Option D – Sequential polling without queues is inefficient. Polling blob storage directly does not scale well for large numbers of blobs, and there is a risk of missed events if uploads occur faster than the polling interval. Queues provide reliable delivery, scaling, and built-in retry mechanisms, making them the preferred method for event-driven processing in Azure Functions.

To preserve order per container while processing files, the recommended design is to use one queue per container and process messages sequentially with a single consumer. This pattern ensures ordered execution, supports reliable event handling, and aligns with best practices for Azure serverless workflows.

Question 11: 

You need to develop an API in Azure that exposes sensitive financial data. You want to ensure that only authenticated applications can call the API and that all requests are logged. Which approach should you choose?

A) OAuth 2.0 authentication with Azure AD and enable diagnostic logging in API Management
B) Allow anonymous access and log activity in Azure Monitor
C) Use Basic authentication and store logs locally on the API server
D) Enforce IP restrictions only

Answer: A) OAuth 2.0 authentication with Azure AD and enable diagnostic logging in API Management

Explanation 

When exposing sensitive data via an API, the two main requirements are strong authentication and auditable logging. Using OAuth 2.0 with Azure Active Directory (Azure AD) ensures that only applications or users with valid access tokens can call the API. Azure AD handles identity management, token issuance, expiration, and revocation, providing a robust and standardized authentication mechanism. This approach enforces application-level identity, which is critical for controlling access to sensitive financial data.

Diagnostic logging in Azure API Management complements authentication by capturing details of every API request. This includes request headers, payload, responses, timestamps, and caller identity. Logs can be streamed to Azure Monitor, Log Analytics, or Storage Accounts, enabling auditing, compliance verification, troubleshooting, and anomaly detection. Together, OAuth 2.0 and diagnostic logging provide both access control and accountability.

Option B – Anonymous access and Azure Monitor are insecure. While logging requests in Azure Monitor provides some visibility, anonymous access allows anyone to call the API, potentially exposing sensitive financial data. Logging alone cannot prevent unauthorized access.

Option C – Basic authentication and local logs are not recommended. Basic authentication transmits credentials encoded in base64, which can be intercepted if TLS is not enforced. Storing logs locally is unreliable for compliance or auditing because logs may be lost if the server is restarted or scaled down.

Option D – IP restrictions only provide a network-level control but do not enforce identity verification. Attackers on allowed networks could bypass security, and there is no mechanism to track which application or user made a request. This does not satisfy security or audit requirements for sensitive financial data.

The best practice for securing an Azure API exposing sensitive financial data is to use OAuth 2.0 authentication integrated with Azure AD for strong, identity-based access control and enable diagnostic logging in API Management to ensure every request is tracked. This approach meets both security and auditing requirements while leveraging managed Azure services for reliability and compliance.

Question 12: 

You are developing an Azure Function that reads messages from Event Hubs. You want to ensure high throughput and low-latency processing. Which configuration is best?

A) Use a single partition for all messages
B) Implement manual batching of events
C) Use multiple partitions and scale out consumers based on partition count
D) Ignore partitions and process messages in any order

Answer: C) Use multiple partitions and scale out consumers based on partition count

Explanation

Azure Event Hubs is a partitioned, high-throughput messaging service. Partitions are the primary mechanism that enables parallel processing and scalability. Each partition represents an ordered sequence of events, and consumers read messages from partitions independently. To achieve high throughput and low latency, you must leverage multiple partitions and scale consumers to match the number of partitions. This allows multiple consumers to process messages concurrently, distributing the load and ensuring low-latency processing.

Option C is the recommended pattern. By aligning consumer instances with partition count, you maximize throughput while maintaining ordering within each partition. This ensures that messages from the same partition are processed in sequence, satisfying ordering guarantees, while still allowing parallel processing across partitions for performance.

Option A – A Single partition is a bottleneck. All events funnel through one partition, meaning only one consumer can read sequentially. While ordering is preserved, throughput is limited and latency increases under high load, making it unsuitable for high-volume scenarios.

Option B – Manual batching can reduce the overhead of processing individual messages. However, batching introduces trade-offs: larger batches can increase latency, and batching alone does not scale automatically. It cannot fully leverage Event Hubs’ partitioned architecture for parallelism, limiting throughput.

Option D – Ignore partitions is incorrect. Partitioning ensures both scalability and ordering guarantees. Ignoring partitions may result in out-of-order processing and uneven load distribution. Event Hub consumers must respect partition assignments to guarantee reliability and correct processing sequences.

For high-throughput, low-latency processing with Azure Event Hubs, the best practice is to use multiple partitions and scale out consumers based on partition count. This approach leverages Event Hubs’ architecture for parallelism, maintains ordering within partitions, and ensures efficient, reliable processing of streaming data in Azure Functions.

Question 13:

You are developing an Azure Function that processes HTTP requests. You want to secure it so that only requests from specific client applications can access it, without manually managing API keys. Which approach should you use?

A) Use function-level API keys and distribute them to clients manually
B) Use Azure AD App Registration and validate OAuth 2.0 access tokens in the function
C) Enable IP restrictions on the function app
D) Use Basic Authentication with usernames and passwords

Answer: B) Use Azure AD App Registration and validate OAuth 2.0 access tokens in the function

Explanation:

Option B – Azure AD App Registration + OAuth 2.0  Using Azure AD App Registrations allows you to register client applications and enforce OAuth 2.0 authentication. Each client application receives an access token when calling your function. The function validates the token against Azure AD to ensure it comes from a trusted client. This approach removes the need to manually manage API keys, improves security, and integrates seamlessly with Azure identity services. It also supports role-based access control (RBAC) and token expiration policies.

Option A – API keys stored in App Settings
Storing API keys in App Settings is less secure. Keys can be exposed if code or configurations are compromised, and you must rotate them manually. It does not provide identity-based authentication or fine-grained access control.

Option C – Basic authentication
Basic authentication with usernames and passwords is insecure, especially over HTTP if not properly encrypted. It also lacks centralized identity management and cannot enforce access based on Azure AD roles.

Option D – Anonymous access
Anonymous access leaves the function open to anyone. Even if you combine it with logging, it does not prevent unauthorized clients from invoking the function, making it unsuitable for production.

Question 14

You are designing a serverless workflow using Azure Logic Apps to process invoices uploaded to an Azure Blob Storage container. You need to trigger the workflow immediately when a new file arrives. Which trigger should you use?

A) Recurrence trigger to check the blob container every few minutes
B) HTTP request trigger to manually invoke the workflow
C) Blob Storage trigger “When a file is created”
D) Manual trigger that requires user intervention

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

Explanation:

Option C – Blob Storage “When a file is created.”  This trigger allows the Logic App to respond instantly when a file is uploaded to the specified container. It is a push-based trigger, so it does not rely on periodic polling, which reduces latency and execution cost. It integrates directly with Azure Blob Storage and supports subsequent workflow actions such as data extraction, validation, or notifications.

Option A – Recurrence trigger
A recurrence trigger executes based on a schedule, such as every 5 minutes. While it could detect new files, it introduces latency and unnecessary execution cost because the workflow runs even when no files are present.

Option B – HTTP Request trigger
HTTP triggers require an external caller to invoke the workflow. In this scenario, blob uploads are the event, so HTTP triggers are unnecessary and add complexity.

Option D – Service Bus trigger
Service Bus triggers listen to queue or topic messages. Since files are uploaded to Blob Storage rather than sent to Service Bus, this trigger is irrelevant for the scenario.

Question 15:

You are developing a microservices-based application that stores sensitive information in Azure SQL Database. You want to ensure encryption of data in transit and at rest, as well as centralized key management. Which approach should you implement?

A) Enable Transparent Data Encryption (TDE) with Azure Key Vault integration and enforce TLS connections
B) Use row-level security and store keys locally in the application
C) Enable column-level encryption only without TLS
D) Rely on application-level encryption and ignore database encryption

Answer: A) Enable Transparent Data Encryption (TDE) with Azure Key Vault integration and enforce TLS connections

Explanation:

Option A – TDE + Azure Key Vault + TLS  Transparent Data Encryption (TDE) encrypts the database at rest. By integrating with Azure Key Vault, you can manage and rotate encryption keys centrally. Enforcing TLS ensures that data in transit is encrypted between client applications and the database. This combination satisfies security best practices for cloud applications, including compliance requirements for GDPR, HIPAA, and PCI-DSS.

Option B – Encrypt columns manually without TDE
Column-level encryption can protect specific data but is complex to manage, requires manual key handling, and does not encrypt the entire database at rest. It also lacks seamless key rotation and integration with Azure services.

Option C – Use IP firewall rules only
IP firewall rules restrict access to specific networks but do not encrypt data at rest or in transit, leaving sensitive information vulnerable to interception or unauthorized access.

Option D – Store encrypted data in the application only
Encrypting data at the application level provides partial security but shifts key management responsibilities to the developer. It does not provide built-in database-level encryption or seamless integration with Azure security services, making it error-prone and non-compliant for many scenarios.

Question 16:

You are developing an Azure Function that integrates with Azure Storage Queues. You want to automatically retry processing messages in case of transient failures. Which approach is best?

A) Implement retry policies with exponential backoff in the function
B) Delete failed messages immediately to avoid reprocessing
C) Manually track failed messages in a database for retry
D) Disable retries and rely on client applications to resend messages

Answer: A) Implement retry policies with exponential backoff in the function

Explanation: 

Option A – Retry policies with exponential backoff. Retry policies are critical for handling transient failures like network glitches, throttling, or temporary service unavailability. Exponential backoff increases the delay between retries (e.g., 1s, 2s, 4s), reducing the risk of overwhelming the service and improving reliability. Azure SDKs and Functions allow configuring max retry counts, intervals, and error types to fine-tune resilience. This is considered a cloud-native best practice and aligns with AZ-204 exam objectives.

Option B – Ignore failures
Ignoring failures risks message loss, breaking the reliability requirement. It violates cloud design patterns and is unsuitable for production workloads.

Option C – Static sleep loops
Static sleep loops retry after fixed intervals, which may lead to inefficient processing, resource waste, or throttling under high load. Exponential backoff is more effective because it adapts to repeated failures.

Option D – Implement retries only in Storage Queues
Azure Storage Queues do not handle retries on the consumer side automatically. While they support visibility timeouts, the function itself must implement retry logic to ensure transient fault handling.

Question 17:

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

A) Use OAuth 2.0 with Azure AD and enable diagnostic logging
B) Allow anonymous access and rely on basic IP filtering
C) Use Basic Authentication and store logs locally on the APIM instance
D) Restrict access with IP restrictions only

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

Explanation:

Option A – OAuth 2.0 + Diagnostic Logging: OAuth 2.0 with Azure AD ensures that only authenticated users or applications within your organization can access the APIs. Diagnostic logging in APIM captures requests, responses, headers, and metadata, which can be sent to Log Analytics or Event Hubs for auditing. This combination provides both security and compliance, which is a key exam objective.

Option B – Anonymous access + Azure Monitor
Anonymous access does not restrict API calls, leaving sensitive APIs exposed. Logging via Azure Monitor alone cannot compensate for a lack of authentication.

Option C – Basic authentication + local logging
Basic authentication is less secure, especially without TLS, and local logging is unreliable for auditing and long-term storage. It is not recommended for production APIs.

Option D – IP restrictions only
IP restrictions prevent requests from certain networks but do not verify user identity. Malicious actors within allowed networks could still access sensitive APIs.

Question 18:

You are developing an Azure Function App that will read messages from multiple Service Bus queues. You want to process messages in parallel while ensuring that each queue’s messages are processed in order. Which approach should you implement?

A) Use a single function instance to read from all queues simultaneously
B) Create one function instance per queue and use peek-lock mode
C) Use ReceiveAndDelete mode for faster processing
D) Ignore queue ordering and process messages with multiple consumers randomly

Answer: B) Create one function instance per queue and use peek-lock mode

Explanation:

Option B – One Function instance per queue + peek-lock. By creating one function instance per queue, each queue’s messages are handled independently, preserving order within the queue. Using peek-lock mode ensures at-least-once delivery. Messages are locked when retrieved, processed by the function, and explicitly completed upon success. If the function fails, the lock expires, and the message becomes available again. This architecture allows parallel processing across queues while maintaining message order for each queue. It is scalable, reliable, and aligns with best practices for serverless Azure applications.

Option A – Single function for all queues
A single function handling multiple queues may receive messages from different queues in an unpredictable order. Parallel processing could mix message order, violating the requirement to maintain sequence per queue.

Option C – ReceiveAndDelete mode for all queues
ReceiveAndDelete removes messages immediately from the queue upon reception. Any failure during processing causes permanent message loss and cannot guarantee message order if retries are needed.

Option D – Poll queues manually
Manual polling is inefficient and does not leverage Azure Functions’ trigger-based scaling. It introduces unnecessary latency and complicates error handling, making it unsuitable for production workloads that require reliability and scalability.

Question 19:

You are building a microservices solution using Azure Kubernetes Service (AKS). One of your services must store session state data for users and needs fast read/write access. Which Azure service is best suited for this scenario?

A) Azure SQL Database
B) Azure Blob Storage
C) Azure Cache for Redis
D) Azure Data Lake

Answer: C) Azure Cache for Redis

Explanation:

Option C – Azure Cache for Redis  Azure Cache for Redis is an in-memory data store providing sub-millisecond latency and high throughput, making it ideal for session state management. Redis supports key-value storage, pub/sub, and data persistence if required. By storing session data in Redis, multiple AKS pods can access the same user state quickly, enabling stateless microservices. Redis also supports replication, clustering, and geo-replication for high availability and disaster recovery.

Option A – Azure SQL Database
While SQL Database provides persistent storage, it is slower than in-memory caches for high-frequency read/write operations like session management. Using SQL could increase latency and RU consumption, impacting performance.

Option B – Azure Blob Storage
Blob Storage is designed for unstructured data like files, images, or logs. It is not suitable for high-speed read/write operations or session management, as it has higher latency and lacks in-memory capabilities.

Option D – Azure Table Storage
Table Storage provides scalable NoSQL key-value storage but is disk-backed, meaning access times are slower than in-memory Redis. While it can store session-like data, it cannot meet low-latency requirements for real-time user interactions.

Question 20:

You are designing an Azure Function to process IoT device telemetry sent via Event Hubs. You need to ensure high throughput, low latency, and fault-tolerant processing. Which configuration should you choose?

A) Use multiple partitions and scale out function consumers per partition
B) Use a single partition for all events
C) Implement manual batching without partitioning
D) Ignore partitions and process events randomly

Answer: A) Use multiple partitions and scale out function consumers per partition

Explanation:

Option A – Multiple partitions + scale-out consumers
Event Hubs partitions allow parallel processing of streaming data while maintaining ordering within a partition. By scaling out function consumers, each partition is processed independently, ensuring high throughput and low latency. Fault tolerance is achieved because if a consumer fails, the partition can be reassigned to another instance. Azure Functions can handle checkpointing to track processed messages, ensuring at-least-once delivery. This approach aligns with cloud-native patterns for event-driven, scalable telemetry processing and is recommended for the AZ-204 exam.

Option B – Single partition
Using a single partition limits throughput because only one consumer can process events sequentially. This creates a bottleneck for large volumes of IoT telemetry and increases latency.

Option C – Batch processing without partitions
Batching improves efficiency but cannot scale horizontally effectively. Processing large batches may introduce latency spikes, and ordering within batches is not guaranteed.

Option D – Ignore partitions. Ignoring partitions breaks the ordering guarantees and prevents scaling. Event Hubs relies on partitioning to achieve parallel processing while maintaining sequence, so this approach is unsuitable for high-throughput scenarios.