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Microsoft AI-200 Exam Syllabus Topics:

SectionObjectives
Develop containerized solutions on Azure- Implement containerized applications
  • 1. Implement scalable hosting patterns
  • 2. Deploy AI workloads in containers
  • 3. Manage containerized compute environments
Connect to and consume Azure services- Integrate Azure services
  • 1. Third-party SDKs
  • 2. Azure SDKs
  • 3. Azure messaging and eventing
  • 4. Event-driven architectures
  • 5. Serverless integration patterns
Develop AI solutions by using Azure data management services- Work with Azure data platforms for AI workloads
  • 1. Azure data management services
  • 2. Data integration for AI applications
  • 3. Vector databases
Secure, monitor, troubleshoot Azure solutions- Operate AI cloud solutions
  • 1. Troubleshooting Azure solutions
  • 2. Security and secret management
  • 3. Monitoring and observability
  • 4. Performance optimization

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Microsoft Developing AI Cloud Solutions on Azure Sample Questions (Q118-Q123):

NEW QUESTION # 118
You plan to deploy a container to an Azure App Service API app named api1. You host the source code for api1 in a GitHub repository. The container uses the API key at runtime to connect to a backend service.
The container must be able to retrieve the API key at runtime without exposing it in the source repository or Git commit history. The API key must not be stored in application configuration directly. The API key must be accessed securely from Azure Key Vault.
You need to ensure that the API key is stored securely in Azure Key Vault and is available to the container at runtime without being exposed in source control or Git commit history.
Solution: Store the API key as a GitHub repository secret.
Does the solution meet the goal?

Answer: B


NEW QUESTION # 119
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.

Answer:

Explanation:

Explanation:

Verified Answer: Preserve parent sampling decisions: ParentBasedSampler. Capture all spans locally:
AlwaysOnSampler. Sample 10% in production: TraceIdRatioBasedSampler(0.1).
Detailed Explanation: Parent-based sampling propagates the sampling decision from the upstream parent, preventing broken sampling decisions across a distributed trace. AlwaysOnSampler records every span, which is suitable for local test environments where complete trace visibility is more important than telemetry volume. TraceIdRatioBasedSampler with 0.1 selects approximately ten percent of traces based on trace identifiers, providing deterministic fixed-ratio sampling for production. A batch span processor and an exporter control processing/export, not the sampling decision itself.
Study Guide Alignment: Security and operations: Key Vault, App Configuration, managed identity, OpenTelemetry, Azure Monitor, and KQL-based troubleshooting.
Official Microsoft Learn References: AI-200 Study Guide | OpenTelemetry sampling in Azure Monitor | Enable Azure Monitor OpenTelemetry


NEW QUESTION # 120
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 a batch embedding workload according to the planned application architecture.
What should you use?

Answer: A

Explanation:
Scenario, application architecture
Azure Functions generate vector embeddings of Azure Cosmos DB for NoSQL-hosted documents and send messages to Service Bus to trigger search index updates.
YAML-formatted files should be used for this deployment.
In a production-grade Kubernetes architecture, declarative management using configuration files provides the predictability, version control, and automation required for complex microservices.
Declarative Configuration: Kubernetes relies natively on YAML to define the desired state of resources like Deployments, KEDA ScaledObjects (for event-driven Azure Function scaling), and Secrets.
Complex Deployments: Your workload requires setting up multiple components, environmental variables, and scaling rules that cannot be efficiently managed via single-line commands.
GitOps Readiness: Storing configurations in YAML allows you to track changes in source control and deploy updates automatically through CI/CD pipelines.
Reference:
https://developer.okta.com/blog/2022/05/05/kubernetes-microservices-azure


NEW QUESTION # 121
Hotspot Question
You have a web service that is used to pay for food deliveries. The web service uses Azure Cosmos DB as the data store.
You plan to add a new feature that allows users to set a tip amount. The new feature requires that a property named tip on the document in Azure Cosmos DB must be present and contain a numeric value.
There are many existing websites and mobile apps that use the web service that will not be updated to set the tip property for some time.
How should you complete the trigger?
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
Box 1: getContext().getRequest();
Y: if (!i["tip"]) { (or alternatively: depending on your specific exam question version)Z:
Box 2: if (isNaN(i["tip"]) || i["tip"] === null) {
Box 3: r.setBody(i);


NEW QUESTION # 122
You are developing several microservices to run on Azure Container Apps. External HTTP ingress traffic has been enabled for the Microservices.
A deployed microservice must be updated to allow users to test new features. You have the following requirements:
* Enable and maintain a single URL for the updated microservice to provide to test users.
* Update the microservice that corresponds to the current microservice version.
You need to configure Azure Container Apps.
Which features should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

Verified answer: Single URL for test users: Revision label. Current microservice activation behavior:
Revision mode.
Detailed Explanation: Azure Container Apps revision labels provide a stable label-specific URL that can be reassigned to a different revision, which is appropriate for a consistent test endpoint. Revision mode controls whether one or multiple revisions can be active at the same time and therefore governs how the current and updated application versions are activated. The container image and registry identify what is deployed, but they do not provide the stable test URL or the application-level revision activation behavior requested.
Study Guide Alignment: Containerized Azure workloads: registry builds, App Service containers, Container Apps revision/scaling behavior, and AKS deployment choices.
Official Microsoft Learn References: AI-200 Study Guide | Azure Container Apps revisions | Azure Container Apps revision labels


NEW QUESTION # 123
......

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