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| Section | Weight | Objectives |
|---|---|---|
| Integrate backend services and build event-driven architectures | 25% | - Build serverless APIs and workflows
|
| Develop AI solutions using Azure data services | 30% | - Implement vector-enabled databases
|
| Develop containerized AI solutions on Azure | 25% | - Implement container hosting environments
|
| Secure, monitor, and optimize AI solutions | 20% | - Implement observability and reliability
|
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NEW QUESTION # 65
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 # 66
You deploy an AI application across multiple Azure regions.
The application must be able to view writes across all regions within a predictable time window.
You need to determine the appropriate consistency level.
What are two consistency levels you can use to achieve the goal? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Answer: A,C
Explanation:
The two appropriate consistency levels for a globally distributed AI application needing to view writes within a predictable time window are Bounded Staleness and Strong.
Bounded Staleness: This provides a guarantee that reads will lag behind writes by a user- specified "staleness window" (either in time or number of operations). It delivers high availability and predictable read consistency across all regions, balancing performance with strict read-order guarantees.
Strong: This provides linearizable and absolute consistency, ensuring that writes are seen synchronously across all regions. It features an exact zero-second predictable time window, though it comes at the expense of higher latency for multi-region operations due to the time required to replicate globally.
Reference:
https://learn.microsoft.com/en-us/azure/cosmos-db/consistency-levels
NEW QUESTION # 67
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 # 68
You are developing a Java application that uses Apache Cassandra to store key and value data.
You plan to use a new Azure Cosmos DB resource and the Azure Cosmos DB for Apache Cassandra API in the application. You create a Microsoft Entra ID group named Azure Cosmos DB Creators to enable provisioning of Azure Cosmos DB accounts, databases, and containers.
The Microsoft Entra ID group must not be able to access the keys that are required to access the data.
You need to restrict access to the Microsoft Entra ID group.
Which role-based access control should you use?
Answer: C
Explanation:
The Azure Cosmos DB Operator built-in role should be used.
Provisioning Capabilities: The Azure Cosmos DB Operator role provides permissions to manage and provision Azure Cosmos accounts, databases, and containers (such as keyspaces and tables for the Apache Cassandra API).
Key Restriction: Unlike the DocumentDB Account Contributor role, it explicitly cannot access or read the account keys or connection strings. This satisfies the security requirement that the Microsoft Entra ID group must not have access to the keys required to read/write the underlying data.
Reference:
https://medium.com/azure-terraformer/terraforming-cosmos-db-access-control-arm-role-definitions-and-their-not-so-evil-twin-5de03cd92965
NEW QUESTION # 69
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 # 70
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