AZ-204 Practice Test 40 — 15 Questions

Question 1

You are querying Azure Monitor metrics using Kusto Query Language (KQL). The query is supposed to return average metric values per hour per resource provider, but it returns no results. What is the most likely issue?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

The table name should be 'AzureMetrics' instead of 'metrics'.. C is correct because the Azure Monitor metrics table is named 'AzureMetrics', not 'metrics'. When querying Azure Monitor metrics using KQL, referencing the wrong table name will result in no results being returned, as the query engine cannot find the specified table.

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The ORDER BY clause should be before GROUP BY.

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The 'bin' function is used incorrectly; it should be 'bin(TimeGenerated, 1h)' without the alias.

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The GROUP BY clause cannot include a computed column like 'bin'.

Question 2

You are reviewing a lifecycle management rule configured on an Azure Storage account. The rule is defined as shown in the exhibit. You notice that blobs tagged with project=temp are not being moved to the Archive tier as expected. What is the most likely cause?

Accepted Answer

The condition uses an incorrect operator for age.. The lifecycle management rule uses the condition 'age > 30' which is an incorrect operator. Azure lifecycle management rules require the operator 'daysElapsedSinceModificationGreaterThan' or similar, not a generic 'age >' syntax. This invalid operator causes the rule to fail to evaluate correctly, so blobs tagged with project=temp are not moved to Archive tier.

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The rule does not include a filter for blob index tags.

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The Archive tier is not supported for this storage account type.

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Block blobs cannot be moved to the Archive tier.

Question 3

A company uses Azure API Management to expose APIs. They need to enforce rate limiting per subscription key and also allow a burst of requests for a short period. Which policy should they apply?

Accepted Answer

rate-limit-by-key. The `rate-limit-by-key` policy is correct because it allows rate limiting based on a specified key, such as a subscription key, and supports a burst configuration via the `renewal-period` and `retry-after` attributes. This policy enforces a per-key counter that resets after a defined period, enabling a burst of requests (e.g., 10 requests in 60 seconds) while still enforcing an overall limit.

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limit-concurrency

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rate-limit (per product)

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rate-limit-by-ip

Question 4

You are developing a web application that will be deployed to Azure App Service. You need to configure automatic scaling based on CPU usage. Which TWO settings should you configure?

Accepted Answer

Configure a scale in condition based on CPU percentage.. Configuring a scale-in condition based on CPU percentage allows the App Service plan to automatically reduce the number of instances when CPU usage drops below a defined threshold, which is essential for cost optimization. Option E is correct because configuring a scale-out condition based on CPU percentage enables the platform to automatically add instances when CPU usage exceeds a threshold, ensuring the application can handle increased load. Together, these two settings define the autoscale rules that react to CPU metrics, which is the core requirement for CPU-based automatic scaling.

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Configure authentication for the scaling endpoint.

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Set the minimum and maximum instance count.

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Set the Always On setting to On.

Question 5

Your Azure Container Apps solution uses Dapr for microservices communication. Which THREE Dapr building blocks are essential for service-to-service invocation and state management?

Accepted Answer

Service Invocation. Service Invocation (A) is correct because Dapr's service invocation building block enables direct, secure service-to-service communication using gRPC or HTTP, with built-in mTLS, retries, and observability. State Management (E) is correct because it provides a key-value store abstraction for managing state across microservices, supporting pluggable state stores like Redis, Cosmos DB, or SQL Server. Pub/Sub (C) is correct because it enables asynchronous event-driven communication between services, decoupling producers and consumers via message brokers like Kafka, RabbitMQ, or Azure Service Bus.

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Bindings

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Actors

Question 6

Your application uses Azure Service Bus topics. You need to ensure that messages are processed in the order they were sent within a session. What must you configure?

Accepted Answer

Enable sessions on the topic and set the SessionId property on messages.. Azure Service Bus sessions provide strict message ordering and exactly-once processing within a session. By enabling sessions on the topic and setting the SessionId property on each message, all messages with the same SessionId are processed in FIFO order, ensuring they are received in the exact sequence they were sent.

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Enable duplicate detection.

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Enable partitioning on the topic.

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Set the MessageId property to a GUID.

Question 7

You are developing a background job that runs every hour to process data. You choose Azure Functions with a timer trigger. What is the correct format for the cron expression to run at the start of every hour?

Accepted Answer

0 0 * * * *. In Azure Functions timer triggers, the cron expression uses six fields: {second} {minute} {hour} {day} {month} {day-of-week}. To run at the start of every hour (i.e., at minute 0 and second 0 of every hour), the expression must be '0 0 * * * *'. Option C correctly sets second to 0, minute to 0, and hour to '*' (every hour), with the remaining fields as '*' (every day, every month, every day-of-week).

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0 * * * * *

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* 0 * * * *

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0 0 0 * * *

Question 8

Your Azure App Service web app experiences slow response times during peak hours. You suspect the App Service plan is under-provisioned. You need to scale out the app automatically based on CPU usage. What should you configure?

Accepted Answer

Configure autoscale rules in the App Service plan. Autoscale rules in the App Service plan allow you to automatically scale out (increase instance count) based on metrics like CPU usage. This is the correct solution because it dynamically adjusts capacity in response to demand during peak hours without manual intervention.

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Configure scheduled scaling in the app

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Manually increase instance count

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Configure scale up in the App Service plan

Question 9

You are developing an Azure App Service web app that must authenticate users via Microsoft Entra ID. Which TWO components are required to set up authentication?

Accepted Answer

Client ID and Client Secret. To authenticate users via Microsoft Entra ID in an Azure App Service web app, you must register the app in Entra ID (Option C) to establish an identity and configure authentication. The Client ID and Client Secret (Option B) are then used as credentials in the OAuth 2.0 authorization code flow to verify the app's identity and obtain tokens. These two components are mandatory for the standard OpenID Connect authentication flow.

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A managed identity

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An Azure AD B2C tenant

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Azure Front Door

Question 10

Which TWO of the following are valid reasons to use Azure Table Storage instead of Azure Cosmos DB?

Accepted Answer

Simpler API and no need for throughput provisioning. Azure Table Storage offers a simpler REST API based on OData and does not require explicit throughput provisioning (RU/s). In contrast, Azure Cosmos DB requires you to configure request units per second for each container, which adds operational complexity. For simple key-value workloads, Table Storage's pay-per-query model with no reserved capacity is more straightforward.

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Global distribution with multi-master writes

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Lower latency and higher throughput

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Support for complex queries with indexing

Question 11

Your organization runs a critical e-commerce application on Azure App Service. The application uses a Standard App Service plan with three instances. During a flash sale, traffic spikes cause some requests to fail with HTTP 503 errors. The operations team reports that the app's CPU usage reaches 95% during spikes. You need to ensure the application remains responsive without manual intervention. The solution must minimize cost while handling unpredictable traffic patterns. You evaluate the following options: A) Enable autoscale to scale out based on CPU usage threshold of 70%, with a maximum of 10 instances. B) Change the App Service plan to Premium v3 and enable zone redundancy. C) Implement a queue-based load leveling pattern using Azure Queue Storage and a background process. D) Use Azure Front Door with a Web Application Firewall (WAF) policy to distribute traffic. Which option should you recommend?

Accepted Answer

Enable autoscale to scale out based on CPU usage threshold of 70%, with a maximum of 10 instances.. Enabling autoscale on the Standard App Service plan allows the application to automatically scale out from 3 to up to 10 instances when CPU exceeds 70%, handling traffic spikes without manual intervention. This minimizes cost by only adding instances when needed, and the Standard plan supports autoscale natively, making it the most cost-effective solution for unpredictable traffic patterns.

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Use Azure Front Door with a Web Application Firewall (WAF) policy to distribute traffic.

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Implement a queue-based load leveling pattern using Azure Queue Storage and a background process.

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Change the App Service plan to Premium v3 and enable zone redundancy.

Question 12

You are developing an Azure Functions app that uses Durable Functions to orchestrate a long-running workflow. The workflow involves calling multiple external APIs. You need to ensure that the orchestration can survive a function app restart. Which feature should you use?

Accepted Answer

Use the default checkpointing and replay mechanism.. Durable Functions inherently use a checkpointing and replay mechanism to persist the orchestration state to a storage backend (Azure Storage queues, tables, and blobs). This ensures that after a function app restart, the orchestrator function can replay from the last checkpoint, restoring the exact execution context and continuing the workflow without data loss.

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Log orchestration state to Application Insights.

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Implement retry policies on the activity functions.

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Set a high timeout on the orchestration.

Question 13

Which TWO actions can help you reduce latency for a globally distributed web application? (Choose two.)

Accepted Answer

Use Azure Traffic Manager with performance routing. Azure Traffic Manager with performance routing directs user traffic to the closest endpoint based on the lowest network latency, reducing response times for globally distributed users. Option E is correct because Azure Front Door uses anycast and global edge points of presence (PoPs) to route traffic to the nearest region, providing both latency reduction and application acceleration.

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Use Azure Application Gateway with Web Application Firewall

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Scale up the App Service plan to a higher tier

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Enable multi-region writes on Azure Cosmos DB

Question 14

You are running an Azure App Service web app on the Basic tier. Users report slow initial responses due to cold starts. You need to keep the app warm without upgrading the hosting plan. Which feature should you enable?

Accepted Answer

Enable 'Always On' in the App Service configuration.. The 'Always On' feature prevents the App Service from being unloaded after periods of inactivity, eliminating cold starts by keeping the application loaded in memory. This is available on the Basic tier and above, so it solves the problem without requiring a plan upgrade.

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Upgrade to a Premium plan to get pre-warmed instances.

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Implement an auto-scaling rule to maintain a minimum instance count.

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Reduce the web app's idle timeout via application code.

Question 15

Which TWO of the following are valid use cases for Azure Queue Storage? (Choose TWO.)

Accepted Answer

Building a reliable messaging layer between microservices.. Azure Queue Storage provides a reliable, persistent message queue that enables asynchronous communication between microservices. It guarantees at-least-once delivery and supports message visibility timeouts, making it ideal for decoupling components in a distributed architecture.

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Broadcasting messages to multiple subscribers.

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Storing large JSON documents for later retrieval.

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Streaming high-volume telemetry data for real-time analytics.