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| Section | Objectives |
|---|---|
| Topic 1: Develop containerized solutions on Azure | - Implement containerized applications
|
| Topic 2: Secure, monitor, troubleshoot Azure solutions | - Operate AI cloud solutions
|
| Topic 3: Develop AI solutions by using Azure data management services | - Work with Azure data platforms for AI workloads
|
| Topic 4: Connect to and consume Azure services | - Integrate Azure services
|
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NEW QUESTION # 112
A python web API uses OpenTelemetry for tracing. The call downstream service function makes an outbound HTTP request by using the requests library. The following code is the only OpenTelemetry configuration in the application:
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selectin is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 113
A development team wants to compare multiple prompt variations against the same test dataset and visualize which prompt performs best. What should they use in Azure AI Foundry?
Answer: D
Explanation:
Prompt flow variants allow you to define multiple versions of a prompt/node, run them against the same evaluation dataset, and compare results side-by-side to identify the best-performing variant.
NEW QUESTION # 114
A RAG application ' s retrieval step is returning documents that are topically related but not precisely relevant to the user ' s question.
You need to improve retrieval precision without retraining any model.
What should you do?
Answer: A
Explanation:
Enable semantic ranking in Azure AI Search . Semantic ranking is a query-time secondary re-ranking stage that evaluates the initial candidate results using Microsoft language-understanding models and promotes documents that are more semantically aligned with the user ' s actual query intent. It can operate over BM25 results, hybrid results, and the textual content associated with vector-search results.
This directly addresses the stated problem. Vector similarity retrieval often has strong recall but can return passages that are broadly related rather than specifically useful for answering the question. Microsoft ' s RAG guidance explains that reranking improves precision by taking the retrieved candidate set and reordering it so that the most query-relevant chunks are placed first. This reduces irrelevant context passed to the generation model and improves grounding quality without retraining either the embedding model or the LLM.
Increasing embedding dimensionality alone does not guarantee better relevance. Disabling vector search sacrifices semantic retrieval capability, while reducing the index size arbitrarily removes potentially useful content rather than improving ranking quality.
Therefore, semantic re-ranking is the correct precision-improvement mechanism .
Study Guide references: Azure AI Search # semantic ranker; RAG information retrieval; hybrid/vector search; secondary ranking; relevance optimization.
NEW QUESTION # 115
Hotspot Question
You are using Python SDK to develop a containerized AI application that retrieves the value of a runtime setting stored in an Azure App Configuration resource. The application will run in Azure in the security context of a managed identity.
The application must work in a local development environment without any code changes.
You need to complete the code that implements the retrieval.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: DefaultAzureCredential
This class from the azure-identity library provides an automated, sequenced token authentication flow. When running locally, it automatically falls back to your local development environment credentials (such as Azure CLI, environment variables, or VS Code login). Once deployed to Azure, it seamlessly switches to the environment's managed identity without requiring any code changes.
Box 2: get_configuration_setting
This method belongs to the AppConfigurationClient in the azure-appconfiguration SDK. It is used to fetch a specific configuration key-value pair using its key name ("FeatureX") and an optional label.
Reference:
https://learn.microsoft.com/en-us/azure/developer/python/tutorial-containerize-deploy-python-web-app-azure-04
NEW QUESTION # 116
A container in an AKS cluster repeatedly restarts.
Pod events show probe failures, although node-level CPU and memory metrics are normal.
You need to diagnose the cause of the repeating restarts.
What should you do first?
Answer: C
Explanation:
The first troubleshooting action should be to inspect the pod events and container logs . Microsoft's AKS troubleshooting guidance identifies Kubernetes events and application logs as primary diagnostic sources when a pod repeatedly restarts or enters states such as CrashLoopBackOff. kubectl describe pod < pod-name
> exposes events including failed liveness or readiness probes, restart reasons, scheduling problems, and container termination information. kubectl logs < pod-name > provides application-level output, while kubectl logs < pod-name > --previous is particularly useful when the container has already restarted because it retrieves logs from the previous terminated container instance.
The scenario already states that node CPU and memory are normal, making node resource pressure less likely.
Probe failures can instead result from incorrect probe paths, ports, timeouts, dependency checks, slow application initialization, or application errors. Microsoft specifically recommends reviewing health probe configuration and application behavior before modifying infrastructure.
Scaling replicas does not identify the root cause. Decreasing initialDelaySeconds can actually make a slow- starting application fail sooner. Draining or rebooting the node is unjustified without evidence of a node-level fault.
Study Guide references: AKS troubleshooting # Kubernetes events, container logs, liveness/readiness probes, kubectl describe, kubectl logs --previous.
NEW QUESTION # 117
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