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| Section | Weight | Objectives |
|---|---|---|
| Secure, monitor, and optimize AI solutions | 20% | - Manage security and configuration
|
| Develop AI solutions using Azure data services | 30% | - Design and optimize data access and retrieval
|
| Integrate backend services and build event-driven architectures | 25% | - Build serverless APIs and workflows
|
| Develop containerized AI solutions on Azure | 25% | - Implement container hosting environments
|
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NEW QUESTION # 14
You store embeddings in a property named embedding in an Azure Cosmos DB for NoSQL container.
You must construct a vector similarity query that orders documents by similarity to the input embedding and returns only the top five results.
You need to construct the similarity query.
What should you do?
Answer: B
Explanation:
The query should limit the number of returned results by using TOP 5 together with ORDER BY VectorDistance(...) . Azure Cosmos DB for NoSQL provides the VectorDistance() system function for comparing a stored vector with an input query vector. Microsoft documents that a vector similarity query should order the results by VectorDistance so that the nearest matches are returned first.
A representative query is:
SELECT TOP 5
c.id,
VectorDistance(c.embedding, @embedding) AS SimilarityScore
FROM c
ORDER BY VectorDistance(c.embedding, @embedding)
Microsoft specifically recommends using a TOP N clause for vector search queries because returning more results than necessary increases RU consumption and latency. In this scenario, TOP 5 directly satisfies the requirement to return only the five most similar documents.
GROUP BY is used for aggregation and does not rank vectors by similarity. DISTINCT removes duplicate projected values but does not restrict results to the five nearest vectors. Offset pagination is unnecessary and can introduce additional query cost when only the highest-ranked five results are required.
Therefore, the correct action is to limit the number of results .
Study Guide references: Azure Cosmos DB for NoSQL # VectorDistance() ; vector search; ORDER BY ; TOP N ; RU and latency optimization.
NEW QUESTION # 15
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: D
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 # 16
You are developing an AI application that retrieves database credentials from Key Vault by using the Azure SDK for Python.
The application must use managed identity for authentication.
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:
Verified Answer: Yes; Yes; No.
Detailed Explanation: The get_secret call supplies an explicit version value, so it retrieves that particular version of dbPassword. DefaultAzureCredential can authenticate to Key Vault by using the Azure-hosted application's managed identity without embedding a client secret in the application. Because the code requests a fixed version, later secret rotation creates a newer version but subsequent executions of this exact code continue to request the specified old version. To follow the latest version automatically, the version parameter must be omitted.
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 | Managed identities for Azure resources | Use Key Vault references for App Service and Functions
NEW QUESTION # 17
Hotspot Question
You deploy a private container image from Azure Container Registry (ACR) to App Service.
App Service must authenticate to ACR to pull the image.
The solution must NOT store static registry credentials.
You need to configure the secure image pull authentication.
Which configurations should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Assign the Container Registry Reader role to a managed identity.
To securely deploy a private container image from Azure Container Registry (ACR) to an Azure App Service without managing passwords, you should configure a Managed Identity on the App Service and grant it the AcrPull role on the ACR.
Box 2: Use managed identity with role assignment.
you should use a Managed Identity combined with an Azure Role-Based Access Control (RBAC) role assignment to meet the requirement.
Reference:
https://medium.com/@miketobicarter/deploying-a-net-app-to-azure-using-docker-acr-app-service-and-github-actions-step-by-step-3ed6ae8a4d0b
NEW QUESTION # 18
You are designing an Azure Function app that exposes a public API.
The solution must:
- Validate incoming request data and return results immediately to the
caller.
- Support Microsoft Entra ID authentication.
- Guarantee idempotent processing when the same request is retried.
- Scale automatically under variable load.
- Avoid duplicate processing.
You need to implement a trigger.
Which trigger should you implement?
Answer: B
Explanation:
An HTTP Trigger combined with Azure Durable Functions (Asynchronous Http Start pattern) and a backing database is the most appropriate architecture to fulfill all your requirements.
Immediate validation: HTTP triggers allow synchronous request-response communication, enabling you to validate payload data and return a 200 OK or 400 Bad Request to the caller instantly.
Entra ID authentication: Azure Functions native App Service Authentication (Easy Auth) integrates directly with HTTP endpoints to enforce Microsoft Entra ID token validation before your code even runs.
Automatic scaling: HTTP-triggered functions scale dynamically out-of-the-box using the Azure Functions Consumption or Premium hosting plans based on incoming web traffic.
Reference:
https://github.com/microsoft/woodgrove-auth-api/blob/main/README.md
NEW QUESTION # 19
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