Microsoft Developing AI Cloud Solutions on Azure : AI-200 exam

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Microsoft AI-200 Exam Syllabus Topics:

Microsoft Developing AI Cloud Solutions on Azure Sample Questions:

Question 1

An AI platform uses App Configuration for feature flags and endpoint routing.
The platform stores secrets alongside configuration data and does NOT support dynamic refresh.
The solution must support dynamic configuration refresh while ensuring that secrets are NOT stored in App Configuration.
You need to enable secure dynamic configuration management for the platform.
Which three actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

A. Use managed identity for both App Configuration and Key Vault access.
B. Use a service principal secret for both App Configuration and Key Vault access.
C. Allow configuration updates with a polling interval.
D. Store configuration values in environment variables.
E. Store API keys in Key Vault.

Question 2

Case Study 1 - Fabrikam Inc.
Background
Fabrikam Inc. is a global retail analytics company that provides AI-driven demand forecasting and product recommendation services to online retailers. The company is modernizing its solution to run entirely on Microsoft Azure.
The platform ingests transaction data, generates embeddings for semantic retrieval, performs vector similarity search, and returns product recommendations through containerized microservices. Developers use Python and Azure SDKs. Operations teams manage container orchestration, scaling, monitoring, and security.
The solution must meet strict performance, scalability, and security requirements.
Current environment
Application architecture
The Recommendation engine is a customer-facing HTTP API running as a containerized Python application. The engine is deployed to Azure Container Apps (ACA).
Embeddings are stored in Azure Database for PostgreSQL by using pgvector.
Semantic retrieval uses metadata filtering combined with vector similarity search.
Azure Managed Redis is used as a caching layer.
Front-end and API workloads are deployed to Azure Container Apps (ACA).
Batch model retraining workloads run in Azure Kubernetes Service (AKS).
Container and CI/CD
Container images are stored in Azure Container Registry (ACR).
CI/CD uses ACR Tasks to build images on commit.
ACA environments support revision management.
AKS workloads are deployed by using Kubernetes manifest files stored in Git.
Monitoring
Logs are collected in Azure Monitor.
Teams inspect container logs and Kubernetes events when troubleshooting.
Developers write KQL queries to analyze latency spikes.
Business requirements
Customer experience: Maintain a seamless, low-latency recommendation experience for end- users, even during unpredictable seasonal traffic spikes.
Operational cost efficiency: Minimize compute expenditures by deallocating resources during periods of inactivity and by preventing runaway scaling costs.
Data integrity and freshness: Ensure that product recommendations always reflect the most current catalog metadata and pricing to prevent customer dissatisfaction.
Security and compliance: Adhere to a Zero Trust security model by eliminating long-lived credentials and centralizing the management of all sensitive secrets.
Global scalability: Support the rapid ingestion of millions of new product embeddings daily without degrading query performance for existing retailers.
Technical requirements
Performance: Semantic search latency must remain under 200 milliseconds at peak load.
Database optimization: Use pgvector for embeddings and implement metadata filtering to reduce compute overhead. Configure compute and memory appropriately for vector workloads to ensure high-dimensional index residency in RAM and efficient mathematical throughput. Vector similarity calculations must be performed only against products that satisfy mandatory metadata constraints.
Database performance: Database connections must support high concurrency with minimal latency through the implementation of connection optimization.
Data load strategy: To ensure maximum ingestion throughput, secondary indexes must be applied only after bulk loading of embeddings is complete.
Caching: Redis cache entries must expire automatically after 10 minutes. Implement a reactive mechanism to invalidate cache entries upon metadata updates.
Identity: Use managed identities for all service-to-service and service-to-database authentication.
Plain-text credentials in configuration files are strictly prohibited.
Secret management: All secrets must be stored centrally. Secrets must be rotated automatically by using a centralized lifecycle policy.
Scaling: Use Kubernetes event-driven autoscaling (KEDA) for event-driven scaling. The Recommendation API must scale based on HTTP traffic, while batch jobs must scale based on queue length and support scale-to-zero.
CI/CD: All images must be stored in Azure Container Registry. Use ACR Tasks to automate image builds triggered by source code commits.
Monitoring: Use KQL to analyze performance telemetry and troubleshoot microservice connectivity failures. Inspect logs and events when troubleshooting AKS and ACA.
Hotspot Question
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.

Question 3

You need to secure an Azure OpenAI resource so that it is only reachable from your virtual network and not from the public internet. What should you configure?

A. API key rotation
B. Azure AI Content Safety
C. Cross-Origin Resource Sharing (CORS) rules
D. A private endpoint with Azure Private Link

Question 4

An AI application uses a database. The database credential rotates every 30 days.
The application currently requires a manual update each time the credential rotates.
You need to ensure that the application always uses the latest secret version without manual updates.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

A. Configure a Key Vault rotation policy.
B. Cache the secret for 30 days.
C. Retrieve secrets by specifying a version identifier.
D. Retrieve secrets without specifying a version identifier.

Question 5

Drag and Drop Question
You are configuring sampling for a distributed application that sends traces to Azure Monitor.
The solution must
- Preserve upstream sampling decisions across distributed traces.
- Capture all spans during local testing.
- Sample 10 percent of traces in production.
You need to apply the appropriate sampling configuration for each requirement.
What should you do? To answer, move the appropriate configurations to the correct requirements. You may use each configuration 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.

Solutions: