Microsoft Developing AI Cloud Solutions on Azure - AI-200 Exam Practice Test

You are developing an Azure Function that calls external APIs by providing an access token for the API. The access token is stored in a secret named token in an Azure Key Vault named mykeyvault. You need to ensure the Azure Function can access the token. Which value should you store in the Azure Functions app configuration?

Hotspot Question
A company uses Azure Monitor Application Insights to monitor application behavior, including incoming requests and dependencies.
You must identify failed requests from the last hour. You must also calculate the average duration of failed request dependency calls, grouped by operation name.
You need to analyze telemetry in Application Insights.
Which operators should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

You are developing an AI API deployed to ACA. The API requires database credentials that are stored in Key Vault. Key Vault is configured to use Azure RBAC for access control.
The database credentials are rotated periodically by the security team. The application must always use the latest version of each credential without being redeployed and without exposing secrets in code or configurations.
You need to implement a secure secret access strategy that prevents credential exposure and fetches the latest version of each secret at runtime without redeploying the container.
Which three actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

Case Study 2 - Proseware Inc.
Background
Proseware Inc. develops AI-powered knowledge management solutions for enterprise customers.
The company is modernizing its platform to support semantic search, intelligent document retrieval, and real-time partner integrations.
The engineering team uses Python and Azure SDKs. The architecture is being redesigned to support containerized microservices, vector search workloads, and serverless backend processing.
Planned Application Architecture
Microservices are containerized by using Docker.
Code for containerized microservices and Azure Function apps is developed locally but stored in a GitHub repository.
Custom images for containerized microservices are stored in Azure Container Registry (ACR).
Base images are stored in Docker Hub. Custom images must be rebuilt automatically whenever their base images are updated.
Azure Cosmos DB for NoSQL stores documents, metadata, and vector embeddings.
Azure Functions generate vector embeddings of Azure Cosmos DB for NoSQL-hosted documents and send messages to Service Bus to trigger search index updates.
Azure Container Apps (ACA) apps host backend API services that provide semantic search across Azure Cosmos DB for NoSQL documents. API services process Service Bus messages and update search indexes.
Azure Kubernetes Service (AKS) processes batch vector embedding regeneration for existing Azure Cosmos DB for NoSQL documents (whenever the embedding model is changed).
An extranet-facing containerized webhook allows business partners to submit documents to be processed by internal AI workflows for semantic search and retrieval.
Monitoring
Telemetry generated by Azure resources is sent to Azure Monitor.
A Log Analytics workspace is used to collect ACA apps logs, AKS container logs, and Azure Functions apps logs.
Monitoring of Azure Functions is currently implemented by using Azure Application Insights SDK instrumentation.
Business Requirements
Embeddings for new or updated Azure Cosmos DB for NoSQL-hosted documents must be automatically generated.
Backend API services must scale automatically during business hours.
Cold start delay of backend APIs must be minimized.
Secrets must be stored outside of container images.
Developers must be able to correlate telemetry across Azure Functions hosts and apps.
All tracing must be implemented by using OpenTelemetry SDK instrumentation.
Development efforts must be minimized.
Technical Requirements
Container images must be built automatically and validated before code updates are merged into the main branch.
Image build automation must run inside the Azure Container Registry, eliminating dependency on local developer machines and external build services.
Dependency of image builds on local developer machines must be eliminated.
Event-driven scaling in ACA must occur based on the number of pending messages in the Azure Service Bus queue.
Azure Cosmos DB for NoSQL RU consumption must be minimized.
Vector similarity search must use embeddings stored in Azure Cosmos DB for NoSQL.
The partner-facing containerized webhook service must run on Azure App Service.
Secrets must NOT be stored in container images, source control, or application configuration directly. They must be accessed securely at runtime.
All secrets must be stored centrally in Azure Key Vault and accessed at runtime through a managed identity.
Azure App Service must supply secrets at runtime without relying on external services.
Resources and workloads must be deployed by using Bicep templates through an automated, version-controlled pipeline. Local and command-line deployments must be eliminated to ensure repeatable, auditable deployments.
Known Issues
RU consumption spikes during vector similarity queries.
Drag and Drop Question
You need to configure event-driven scaling for the backend API services to meet the technical requirements.
Which settings should you use for each element? To answer, move the appropriate settings to the correct elements. You may use each setting 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.

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?

An application performs similarity search across 5 million embeddings stored in Azure Database for PostgreSQL with pgvector. Queries often filter by department before ranking by cosine distance.
P95 latency for vector similarity queries exceeds the SLA target. Monitoring shows sustained high CPU use during query execution.
You need to reduce P95 latency for filtered vector similarity queries.
What should you do?

Your organization requires that all prompts and completions sent to Azure OpenAI be retained for zero data logging beyond what's required for abuse monitoring, per contractual requirements.
What should you do?

You are developing an AI search API that caches semantic search results in Redis.
Search results must remain cached for 10 minutes. If the underlying data changes, cached entries must NOT be returned.
You need to implement a cache-aside strategy to ensure data consistency.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

You are choosing an embedding strategy for a RAG solution. Documents range from 2 to 200 pages. You need to preserve semantic coherence while staying within embedding model token limits. What should you do?

You configure ACR Tasks to automate image builds.
Container images must rebuild when:
- Application updates occur.
- Base image updates occur, such as when the underlying OS image is
updated.
- Regular scheduled rebuilds are required.
You need to configure ACR Tasks to support automated image rebuilds.
Which three triggers should you configure? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.