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| Section | Objectives |
|---|---|
| Implement and monitor AI workloads | - Deploy AI models and services - Monitor performance and troubleshoot issues |
| Implement Azure AI solutions | - Implement generative AI solutions using Azure OpenAI - Implement computer vision solutions - Implement knowledge mining with Azure AI Search - Implement natural language processing solutions |
| Plan and manage Azure AI solutions | - Plan security and compliance requirements - Monitor and optimize AI solutions - Select appropriate Azure AI services |
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NEW QUESTION # 118
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 vector embedding updates according to the business and technical requirements.
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:
Business Requirements: Embeddings for new or updated Azure Cosmos DB for NoSQL-hosted documents must be automatically generated.
Technical Requirements: Vector similarity search must use embeddings stored in Azure Cosmos DB for NoSQL.
Box 1: Change feed processor
The Change feed processor is the correct component to configure for identifying document changes that should trigger vectorization.
This feature in Azure Cosmos DB provides a reliable, distributed, and scalable way to listen to a constant stream of changes (inserts and updates) within a container. It allows you to process these modifications in real-time, making it the perfect trigger for automated workflows like sending new text to an embedding model.
Box 2: Lease Container
The lease container is the component you configure to coordinate and horizontally scale out the change feed-driven vectorization processing across multiple workers or compute instances.
The Lease Container acts as state storage, tracks checkpoints, and distributes partition keys across parallel compute workers (such as Azure Functions or custom change feed processors) to scale out throughput processing safely without duplicate work.
Incorrect:
RU Throughput: Dictates the maximum performance limit, but provisioning RUs alone does not orchestrate or scale out multiple processing workers.
Consistency Level: Affects data replication guarantees and read/write ordering, not parallel processing distribution.
Reference:
https://blog.nashtechglobal.com/managing-record-changes-with-change-feed-in-azure-cosmos-db/
NEW QUESTION # 119
You are developing an Azure Functions app.
All functions in the app meet the following requirements:
* Run until either a successful run or until 10 run attempts occur.
* Ensure that there are at least 20 seconds between attempts for up to 15 minutes.
You need to configure the host.json file.
How should you complete the code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 120
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. Choose two NOTE: Each correct selection is worth one point.
Answer: A,C
Explanation:
Detailed Explanation: The cache-aside design needs both bounded lifetime and explicit invalidation. A ten- minute TTL ensures cached search results do not persist indefinitely, while deleting related keys when source data changes prevents known-stale entries from being returned during that ten-minute window. Sliding expiration would extend lifetime based on access and can preserve stale data. Keyspace notifications report Redis key events; they do not replace application logic that invalidates entries when the authoritative source changes.
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 | Azure Managed Redis documentation
NEW QUESTION # 121
A large retail company operates online and physical stores. The company tracks inventory levels in real time to manage stock efficiently across all locations. You develop an Azure Event Grid solution to handle events generated by the inventory management system deployed to Azure.
You need to implement a subscription filter that dynamically adjusts to seasonal changes in product demand.
Which event filter should you use?
Answer: B
Explanation:
To handle dynamic, seasonal changes in product demand within Azure Event Grid, you should use Advanced Filters with a string or numeric comparison that evaluates event data attributes, typically paired with an automated external process (like an Azure Function or Logic App) to dynamically update the subscription filter rules via the Event Grid management API as seasons change Reference:
https://techcommunity.microsoft.com/blog/analyticsonazure/a-technical-implementation-guide-for-multi-store-retail-environments/4488418
NEW QUESTION # 122
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?
Answer: A
NEW QUESTION # 123
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