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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Develop containerized AI solutions on Azure | 25% | - Monitor and troubleshoot containerized workloads
|
| Topic 2: Develop AI solutions using Azure data services | 30% | - Design and optimize data access and retrieval
|
| Topic 3: Integrate backend services and build event-driven architectures | 25% | - Implement messaging and event systems
|
| Topic 4: Secure, monitor, and optimize AI solutions | 20% | - Implement observability and reliability
|
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NEW QUESTION # 143
You have an existing AKS cluster and a container image stored in Azure Container Registry.
You must deploy a new version by using a Deployment manifest that supports rolling updates without interrupting traffic.
You need to perform a rolling update by using a manifest file.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation:
NEW QUESTION # 144
A semantic search application queries Azure Database for PostgreSQL and stores document embeddings and metadata in a table with the following columns:
* embedding (pgvector)
* department
* created_at
The application must return the top five most similar documents for a given query embedding only from the finance department. You need to implement semantic retrieval with metadata filtering.
Which query components should you select? To answer, move the appropriate query components to the correct requirements. You may use each query component once, more than once, or not at all. You may need to move the split bar between panes or scroll to view content.
Answer:
Explanation:
Explanation:
Verified answer: Filter: `WHERE department = ' finance ' `. Rank/top five: `ORDER BY embedding < = > query_embedding LIMIT 5`.
Detailed Explanation: The metadata predicate must restrict the candidate rows to the Finance department, so the `WHERE department = ' finance ' ` component supplies the required filter. The pgvector cosine-distance operator ` < = > ` orders rows by vector distance to the supplied query embedding, and `LIMIT 5` keeps only the five nearest matches. Ordering by creation date would rank recency rather than semantic similarity, and a wildcard department filter would not satisfy the Finance-only requirement.
Study Guide Alignment: AI data-management workloads: Cosmos DB, PostgreSQL, caching, vector storage, vector retrieval, consistency, and connection optimization.
Official Microsoft Learn References: AI-200 Study Guide | Vector similarity search with Azure PostgreSQL
NEW QUESTION # 145
You store embeddings in Redis by using keys formatted as doc:(id). Some embeddings are accessed frequently. Others are rarely used.
You need to implement a caching strategy that keeps only frequently accessed embeddings in memory.
What should you use?
Answer: C
Explanation:
To implement this strategy, you should use allkeys-lru combined with the EXPIRE command as a secondary fallback.
Primary Mechanism: Configure your Redis maxmemory-policy to allkeys-lru.
Secondary Mechanism: Apply the EXPIRE command to your keys as a safety net.
Why allkeys-lru is the Best Choice
Memory Management: It automatically evicts the Least Recently Used (LRU) keys across your entire dataset when Redis hits its memory limit.
Frequent Access: It guarantees that frequently accessed embeddings stay in memory, regardless of when they were created.Prefix Independent: It scans all keys, making it perfect for your doc:(id) format Reference:
https://rahulchowdhury.in/blog/redis-caching-patterns-every-mern-dev
NEW QUESTION # 146
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals.
You are preparing a production deployment for an Azure Function app. The app will run across multiple environments.
The solution must support environment-specific configuration and prevent secrets from being stored in source control.
You need to develop the solution.
Solution: Store connection strings in the Function app application settings configured in the Azure Portal.
Does the solution meet the goal?
Answer: B
Explanation:
Azure Functions application settings are the correct mechanism for keeping environment-specific configuration outside the application source code. Microsoft documents that application settings, including connection strings and other configuration values, are defined separately for each function app . This allows development, test, staging, and production deployments to use different values without modifying the application code.
At runtime, Function app settings are exposed to the application as environment variables , so code can retrieve the required connection string or configuration value without embedding it in a configuration file committed to source control. This directly satisfies the requirement to prevent secrets from being stored in the Git repository.
For production-grade secret management, Azure Key Vault references are often preferable because they provide centralized secret lifecycle management and can be combined with managed identities. However, the question asks only whether the proposed solution meets the stated goals. Storing connection strings in Azure Function application settings does satisfy both: values can vary by deployed environment and remain outside source-controlled application files.
Study Guide references: Azure Functions configuration; application settings; environment variables; secure application configuration.
NEW QUESTION # 147
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.
Hotspot Question
You need to configure image build automation based on the technical requirements.
Which settings should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Scenario, 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.
Box 1: Commit
Use a Commit trigger for this configuration
Immediate Validation: Builds continuous integration (CI) pipelines automatically when a developer submits a pull request or pushes code.
Gatekeeper Function: Prevents unvalidated code or broken container images from merging into your main branch.
Workflow Alignment: Supports microservices development by isolating tests to the specific code updates being introduced.
Box 2: GitHub workflow
To implement automatic image builds and validation before code updates are merged into the main branch, you should use a GitHub workflow.
Branch gating: GitHub Actions can trigger specifically on pull_request events targeting the main branch, allowing you to validate code and builds before merging.
Automation control: It orchestrates the entire continuous integration (CI) pipeline, including linting, running unit tests, building Docker images, and scanning them for vulnerabilities.
Integration capacity: It can seamlessly hand off the successfully validated image to Azure Container Registry (ACR) or utilize ACR Tasks under the hood as part of the pipeline steps.
Reference:
https://docs.github.com/en/actions/tutorials/publish-packages/publish-docker-images
NEW QUESTION # 148
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