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| Section | Objectives |
|---|---|
| Topic 1: Connect to and consume Azure services | - Integrate Azure services
|
| Topic 2: Develop containerized solutions on Azure | - Implement containerized applications
|
| Topic 3: Secure, monitor, troubleshoot Azure solutions | - Operate AI cloud solutions
|
| Topic 4: Develop AI solutions by using Azure data management services | - Work with Azure data platforms for AI workloads
|
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NEW QUESTION # 133
You are using Azure Monitor Application Insights to collect dependency data.
You must be able to:
- Correlate failed requests with dependency calls during the last hour.
- Calculate the average dependency duration per operation.
You need to construct the KOL query by using the minimum number of statements.
Which three operators should you use? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: A,C,E
Explanation:
To satisfy both requirements with the minimum number of statements using the Kusto Query Language (KQL), you should use the following KQL operators:
where: Filters data by time range (ago(1h)) and request status (success == false).
join: Correlates the requests and dependencies tables using a unique identifier (usually operation_Id).
summarize: Computes the average (avg()) dependency duration grouped by the specific operation (operation_Name).
Reference:
https://learn.microsoft.com/en-us/azure/azure-monitor/app/application-insights-faq
NEW QUESTION # 134
Hotspot Question
You are reviewing the Python tracing configuration for an application that must send distributed traces to Azure Monitor.
The following code configures OpenTelemetry tracing:
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Yes
Yes, the code configures the tracer provider before any spans are created.
While the tracer provider initialization sequence is structurally correct, no spans can be captured yet at the moment the tracer instance is fetched because the processor and exporter are attached too late in the execution flow.
Box 2: No
No, this configuration does not export traces synchronously.
The code utilizes BatchSpanProcessor, which batches completed spans and exports them asynchronously on a background thread to prevent telemetry collection from blocking your main application code.
Box 3: Yes
Yes, this configuration successfully enables the export of distributed traces to Azure Monitor.
The code provided correctly initializes and wires up the native OpenTelemetry SDK components with the official Azure Monitor exporter library.
Reference:
https://learn.microsoft.com/en-us/python/api/overview/azure/monitor-opentelemetry-exporter-readme
NEW QUESTION # 135
You need to deploy a batch retraining workload.
How should you complete the scaling configuration? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
* Trigger scaling based on queue depth: Azure Service Bus scaler
* Deallocate instances when the message queue is empty: Minimum replicas = 0
* Prevent uncontrolled burst scaling: Maximum replicas = 10
The batch retraining workload must scale according to queue length , so the correct KEDA trigger is the Azure Service Bus scaler . KEDA integrates with Azure Service Bus and monitors queue or subscription metrics, allowing AKS workloads to scale according to the number of pending messages rather than CPU utilization or HTTP request concurrency. Microsoft documents KEDA as the event-driven autoscaling mechanism for AKS and specifically supports Azure-based scalers for queue-driven workloads.
To meet the requirement to deallocate workload instances when no messages remain, configure minimum replicas = 0 . KEDA supports scale-to-zero for event-driven workloads, which is particularly appropriate for intermittent batch or machine-learning processing because no pods need to remain running while the queue is empty. Microsoft specifically identifies scale-to-zero as a KEDA capability and recommends it for sporadic ML workloads where cost optimization is important.
Finally, configure maximum replicas = 10 . A finite maximum replica count establishes an explicit upper scaling boundary, preventing large queue bursts from creating an uncontrolled number of worker replicas and protecting both infrastructure cost and downstream services.
Study Guide references: AKS # KEDA; Azure Service Bus event-driven scaling; minReplicaCount; maxReplicaCount; scale-to-zero; autoscaling limits.
NEW QUESTION # 136
Hotspot Question
You are creating an Azure Functions app project in your local development environment by using Azure Functions Core Tools.
You must create the project in either Python or C# without using a template.
You need to specify the command and its parameter required to create the Azure Functions app project.
Which command and parameter should you specify? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: func init
To initialize a new Azure Functions app project from the command line using Azure Functions Core Tools without using a specific code template, use the func init command combined with the -
-worker-runtime parameter.
Box 2: --worker-runtime
Reference:
https://r-vm.com/python-on-azure-functions
NEW QUESTION # 137
A company is developing a solution that allows smart refrigerators to send temperature information to a central location.
The solution must receive and store messages until they can be processed. You create an Azure Service Bus instance by providing a name, pricing tier, subscription, resource group, and location.
You need to complete the configuration.
Which Azure CLI or PowerShell command should you run?
Answer: D
NEW QUESTION # 138
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