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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Integrate backend services and build event-driven architectures | 25% | - Build serverless APIs and workflows
|
| Topic 2: Develop AI solutions using Azure data services | 30% | - Design and optimize data access and retrieval
|
| Topic 3: Secure, monitor, and optimize AI solutions | 20% | - Implement observability and reliability
|
| Topic 4: Develop containerized AI solutions on Azure | 25% | - Monitor and troubleshoot containerized workloads
|
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NEW QUESTION # 137
You are evaluating a fine-tuned Azure OpenAI model against the base model before promoting it to production.
Which Azure AI Foundry capability should you use?
Answer: C
Explanation:
Use Azure AI Foundry evaluation with a representative evaluation dataset and quality metrics to compare the fine-tuned model against the base model before production promotion. Among the available options, this corresponds to Prompt flow bulk testing / evaluation . Foundry evaluation supports systematic testing of model or application outputs across many test records rather than relying on manual inspection of isolated prompts. Microsoft provides built-in evaluators including Groundedness, Coherence, Relevance, Fluency, and Response Completeness , allowing teams to quantify quality consistently across candidate implementations.
For a valid comparison, run the same representative dataset against both the base and fine-tuned deployments and compare the resulting evaluation metrics. This creates objective evidence about whether fine-tuning improves the targeted behavior without materially degrading relevance, coherence, grounding, or other required quality dimensions. Prompt flow evaluation workflows also support batch execution, per-record scores, and aggregated metrics for comparing variants.
Application Insights is essential for production telemetry such as latency, failures, and usage, but it is not by itself a model-quality comparison framework. Content Safety configuration evaluates or controls harmful content rather than comparative model quality. Azure AI Search indexer scheduling manages search-index ingestion and is unrelated to model evaluation.
Therefore, A is the correct capability.
Study Guide references: Microsoft Foundry # evaluations; evaluation datasets; bulk/batch testing; Groundedness; Coherence; Relevance; comparative model assessment.
NEW QUESTION # 138
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: B
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 # 139
You must ensure an Azure OpenAI-powered application never exceeds a defined token-per-minute budget across all users to avoid runaway costs.
What should you configure?
Answer: D
Explanation:
Configure deployment-level Tokens-per-Minute (TPM) and Requests-per-Minute (RPM) rate limits for the Azure OpenAI model deployment. Azure OpenAI quota is allocated to individual model deployments in TPM units, and Microsoft documents that the TPM assigned to a deployment directly maps to the rate limit enforced for inference requests against that deployment. An associated RPM limit is also enforced according to the model ' s TPM-to-RPM ratio.
Because all application users invoke the same deployment, this enforcement occurs at the deployment boundary rather than independently for each end user. Once request traffic would exceed the configured capacity, Azure applies throttling rather than allowing unrestricted token consumption. Microsoft Foundry additionally supports explicit token-management controls through AI Gateway, where a TPM limit can be set for a model deployment; requests exceeding the limit receive HTTP 429 Too Many Requests responses.
Content filtering governs safety categories, not consumption. An Azure AI Search vector-index limit controls search storage rather than generated tokens. Temperature changes sampling randomness and has no enforcement role in rate or cost control.
Study Guide references: Azure OpenAI quota management; TPM/RPM rate limits; deployment capacity; throttling; Foundry AI Gateway token management.
NEW QUESTION # 140
Drag and Drop Question
You are configuring an Azure-hosted AI application that uses Azure App Configuration to manage its runtime settings.
The application must provide the following functionality:
- Support percentage-based, targeted rollouts.
- Maximize token limits per request.
- Store a key that is retrieved securely at runtime.
- Reference a list of supported deployment regions.
You need to configure the App Configuration features for the app.
Which features should you configure? To answer, move the appropriate App Configuration features to the correct configuration items. You may use each App Configuration features once, more than once, or not at all. You may need to move the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Feature flag
Use a Feature flag.
Azure App Configuration provides built-in feature management specifically designed to dynamically control application behavior without redeploying code.
Percentage-Based Rollouts: Feature flags support targeting filters (such as the standard Microsoft.Targeting filter), which natively handle percentage-based rollouts and specific audience targeting rules.
Dynamic Changes: You can safely toggle features or change the exposure percentage on the fly directly through the Azure portal or via APIs.
Box 2: Key-value
Key-value is the correct feature to use.
A standard key-value pair is the optimal choice for managing runtime settings like numeric thresholds or token limits.
Direct Data Injection: It allows you to store the specific maximum token limit (e.g., MaxTokensPerRequest = 4096) as an integer or string that your application can parse directly at runtime.
No Extra Overhead: Unlike the other options, a standard key-value pair maps directly to application configuration variables without requiring secondary lookups or conditional logic evaluation.
Box 3: Key Vault reference
Use an Azure Key Vault reference.
Azure App Configuration is optimized for non-sensitive runtime configurations like feature flags, environment names, and general application settings. Sensitive values (such as API keys for OpenAI/LLM models, database passwords, and connection strings) should always be stored natively in Azure Key Vault.
By creating a Key Vault Reference inside Azure App Configuration, you store only the URI pointing to the secret rather than the secret value itself. When your AI application requests configuration data at runtime, the App Configuration provider automatically contacts Key Vault, resolves the reference, and securely injects the actual secret directly into your application's memory pipeline.
Box 4: Key-value
Use a standard configuration key-value pair (or a JSON array within a key).
How to store it: Save the regions as a comma-separated string or a JSON array under a single key.
Key Example: Deployment:SupportedRegionsValue Example: ["eastus", "westeurope",
"southeastasia"]
Content Type: Set the content type to application/json so your application can automatically parse it into a list or array at runtime.
Reference:
https://learn.microsoft.com/en-us/azure/azure-app-configuration/manage-feature-flags
https://turbo360.com/blog/get-azure-key-value-pairs-from-app-configuration
https://learn.microsoft.com/en-us/azure/app-service/app-service-key-vault-references
NEW QUESTION # 141
You are using the 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:
Verified Answer: var1 = DefaultAzureCredential(); retrieval call = get_configuration_setting(...).
Detailed Explanation: DefaultAzureCredential is designed to support the same application code across local development and Azure-hosted environments. Locally it can use developer credentials, while in Azure it can use the workload's managed identity. The App Configuration client's get_configuration_setting method retrieves a specific key/label pair, which matches the code shown. Using ManagedIdentityCredential directly would not provide the same local-development credential chain without code or environment-specific handling.
Study Guide Alignment: Security and operations: Key Vault, App Configuration, managed identity, OpenTelemetry, Azure Monitor, and KQL-based troubleshooting.
Official Microsoft Learn References: AI-200 Study Guide | Azure App Configuration Python client | Managed identities for Azure resources
NEW QUESTION # 142
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