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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Secure, monitor, and optimize AI solutions | 20% | - Manage security and configuration
|
| Topic 2: Develop AI solutions using Azure data services | 30% | - Implement vector-enabled databases
|
| Topic 3: Integrate backend services and build event-driven architectures | 25% | - Implement messaging and event systems
|
| Topic 4: Develop containerized AI solutions on Azure | 25% | - Monitor and troubleshoot containerized workloads
|
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NEW QUESTION # 32
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.
Answer: A,B,C
Explanation:
To support automated container image rebuilds based on your requirements, you need to configure source triggers, base image triggers, and timer triggers in Azure Container Registry (ACR) Tasks.
Timer Triggers: Automates regular scheduled rebuilds. This uses a cron schedule expression to run image builds at specific intervals (e.g., weekly or monthly) regardless of code changes.
Source Triggers: Automates rebuilds when application updates occur. This monitors changes in your source code repository (like GitHub or Azure Repos) and fires a build when code is committed.
Base Image Triggers: Automates rebuilds when the underlying OS or framework image updates.
ACR tracks dependencies and automatically kicks off a new build when your defined FROM image changes in the public registry or your private registry.
Reference:
https://oneuptime.com/blog/post/2026-02-16-how-to-set-up-acr-tasks-for-automated-container-image-builds-on-git-commit/view
NEW QUESTION # 33
Hotspot Question
You deploy a Linux container image to App Service.
The container requires the following environment variables at runtime:
- A non-sensitive configuration value named MODEL_VERSION
- A database password that must remain secure
You need to configure App Service to provide these environment variables at runtime.
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: App setting with plain text value
To provide a non-sensitive environment variable named MODEL_VERSION to a Linux custom container deployed to Azure App Service, you should configure it as an Application Setting (App Setting). In Azure App Service, any custom configuration defined within the Application Settings is automatically injected into Linux containers as an environment variable at runtime.
Box 2: Key Vault reference syntax
You should configure the database password as an Azure Key Vault reference inside App Service Application Settings. This prevents the secret from being stored in plaintext in the app configuration and injects it securely into the container as an environment variable at runtime.
Reference:
https://learn.microsoft.com/en-us/azure/app-service/tutorial-custom-container?tabs=azure-cli&pivots=container-linux
NEW QUESTION # 34
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.
You need to deploy Azure Function resources and apps to meet the business and technical requirements. What should you use?
Answer: C
Explanation:
GitHub Actions is the correct tool to use.
Automated Pipeline: It natively automates infrastructure deployment (Bicep) and code compilation through version-controlled workflows.
Eliminates Local Deployments: Workloads are triggered by repository events (like a code merge), removing the need for manual command-line interventions.
Auditability & Repeatability: It keeps a centralized log of every deployment, ensuring a clear audit trail for governance.
Scenario:
Technical Requirements
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.
Business Requirements
Development efforts must be minimized.
Reference:
https://github.com/marketplace/actions/azure-functions-action
NEW QUESTION # 35
Hotspot Question
You are developing a Retrieval-Augmented Generation (RAG) solution for a company.
AI responses and embedding vectors are cached in Redis.
The solution must meet the following requirements:
- AI responses must expire exactly 24 hours after they are cached.
- Cached embeddings must always reflect the current source data.
You need to configure Redis to meet the requirements.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Set a Time for Live (TTL) on each key.
To configure Redis to expire your AI responses exactly 24 hours after caching, you must apply a Time-To-Live (TTL) of 86,400 seconds to the specific keys holding those responses.
Box 2: Delete related keys when the document changes
You must invalidate or update those cached keys when source data changes to prevent your RAG system from retrieving stale information.
Incorrect:
Configuring allkeys-lru (Least Recently Used) is not recommended to ensure your cached embeddings or AI responses reflect current source data.allkeys-lru is an eviction policy. It only deletes data when Redis reaches its maximum memory limit, and it targets keys that haven't been accessed recently. It does not verify if the underlying source data has changed, meaning users will get dangerously outdated (hallucinated) AI responses if the source document is updated but the cached response remains in memory.
Reference:
https://zenvanriel.com/ai-engineer-blog/ai-caching-strategies/
NEW QUESTION # 36
Hotspot Question
A Python web API uses OpenTelemetry for tracing. The call_downstream_service function makes an outbound HTTP request by using the requests library.
The following code is the only OpenTelemetry configuration in the application:
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:
Box 1: Yes
This code will successfully create a local span for the process_request function, but it will not automatically create or propagate spans for the outbound HTTP request made by the requests library.
Box 2: No
Manual tracing only: Your current configuration only creates a span for the code wrapped inside the with tracer.start_as_current_span(...) context manager.
Box 3: Yes
Missing instrumentation: The standard requests library does not natively know about OpenTelemetry. It requires an external instrumentation package to hook into its network calls.
Reference:
https://learn.microsoft.com/en-us/python/api/overview/azure/core-tracing-opentelemetry-readme
NEW QUESTION # 37
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