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| Section | Objectives |
|---|---|
| Topic 1: Plan and manage Azure AI solutions | - Plan security and compliance requirements - Select appropriate Azure AI services - Monitor and optimize AI solutions |
| Topic 2: Implement and monitor AI workloads | - Monitor performance and troubleshoot issues - Deploy AI models and services |
| Topic 3: Implement Azure AI solutions | - Implement natural language processing solutions - Implement computer vision solutions - Implement knowledge mining with Azure AI Search - Implement generative AI solutions using Azure OpenAI |
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NEW QUESTION # 53
Drag and Drop Question
You plan to create a Docker image that runs an ASP.NET Core application named ContosoApp.
You have a setup script named setupScript.ps1 and a series of application files including ContosoApp.dll.
You need to create a Dockerfile document that meets the following requirements:
- Call setupScript.ps1 when the container is built.
- Run ContosoApp.dll when the container starts.
The Dockerfile document must be created in the same folder where ContosoApp.dll and setupScript.ps1 are stored.
Which five commands should you use to develop the solution? To answer, move the appropriate commands from the list of commands to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation:
Step 1: FROM microsoft/aspnetcore:latest
Sets the base image for the container.
Base First: You must always declare the base image (FROM) before running any other commands.
Step 2: WORKDIR /apps/ContosoApp
Creates and sets the working directory inside the container.
Directory and Copy: You need to set the working directory (WORKDIR) before copying files (COPY) so Docker knows exactly where to put them.
Step 3: COPY ./ .
Copies the application files and script from your local folder into the working directory.
Step 4: RUN powershell ./setupScript.ps1
Executes the script during the image build process.
Build vs. Run: The RUN command happens while building the image, so the script completes its setup before the final image is saved. The CMD instruction does not execute anything during the build; it simply configures the starting point for when the container is actually launched.
Step 5: CMD ["dotnet", "ContosoApp.dll"]
Defines the default command to run your application when the container starts.
Reference:
https://azuretar.com/using-startup-command-to-pass-command-line-arguments-to-azure-app-service-for-linux/
NEW QUESTION # 54
You are developing a solution for a hospital to support the following use cases:
*The most recent patient status details must be retrieved even if multiple users in different locations have updated the patient record.
*Patient health monitoring data retrieved must be the current version or the prior version.
*After a patient is discharged and all charges have been assessed, the patient billing record contains the final charges.
You provision a Cosmos DB NoSQL database and set the default consistency level for the database account to Strong. You set the value for Indexing Mode to Consistent.
You need to minimize latency and any impact to the availability of the solution. You must override the default consistency level at the query level to meet the required consistency guarantees for the scenarios.
Which consistency levels should you implement? To answer, drag the appropriate consistency levels to the correct requirements. Each consistency level may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Strong consistency is required for the patient-status scenario because it provides linearizability : reads return the latest committed value. This is the appropriate guarantee when updates can originate from different locations and the application must never return an older patient status. Microsoft defines Strong as the highest Azure Cosmos DB consistency level and notes that it provides the strongest freshness guarantee, at the cost of greater latency and reduced availability compared with weaker models.
For health-monitoring data, Bounded Staleness is the intended consistency level. It explicitly limits how far reads can lag behind writes using a maximum number of versions (K) or a time interval (T). Conceptually, this matches a requirement that data may be current or only a limited number of versions behind.
For finalized billing data, Eventual consistency provides the lowest latency and highest availability. Once discharge processing is complete and no further billing writes occur , replicas eventually converge on the same final value. Microsoft specifically notes that, in the absence of further writes, weaker consistency levels ultimately yield the same converged data.
Azure Cosmos DB also permits requests to relax consistency below an account ' s Strong default , which is exactly the optimization pattern described here.
Study Guide references: Azure Cosmos DB for NoSQL # Strong consistency; Bounded Staleness; Eventual consistency; per-request consistency override; latency/availability trade-offs.
NEW QUESTION # 55
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 # 56
You need to address the known issue resulting from vector similarity queries.
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,B
Explanation:
Detailed Explanation: The objective is to reduce RU consumption from vector similarity queries. Microsoft guidance identifies vector numeric precision and vector-index selection as major cost and performance levers.
Using a lower supported vector precision can reduce storage and processing cost, while quantizedFlat and DiskANN are designed to reduce latency and RU consumption compared with flat search for appropriate data sizes. Strong consistency would increase resource cost rather than solve vector-search efficiency. A regular composite index is not a substitute for the vector index. Option B should be read as reducing vector numeric precision, not changing a generic "indexing precision" setting.
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 search in Azure Cosmos DB | Optimize Cosmos DB vector search performance
Topic 1 : Proseware Inc. Case Study 7
Topic 2 : Fabrikam Inc 8
Topic 3 : Standalone Questions Set 127
TOTAL 142
Topic 1, Proseware Inc. Case Study
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.
NEW QUESTION # 57
You maintain multiple versions of a container image in Azure Container Registry.
The production deployment must always run the exact same image build even if tags are changed later.
You need to ensure predictable and immutable image selection during deployment.
What should you do?
Answer: D
Explanation:
Use the image ' s SHA-256 manifest digest when defining the production deployment. Azure Container Registry assigns every pushed image manifest a unique digest, and Microsoft explicitly states that pulling an image by digest guarantees the image version being retrieved , even if an identically named tag is later pushed to a different image. A digest reference has the form myregistry.azurecr.io/repository@sha256: < digest > .
Tags such as production or latest are mutable references . By default, a user or pipeline with sufficient permissions can push a different image under the same tag. Microsoft therefore warns against relying on reusable stable tags for production deployments when exact image reproducibility is required.
A scheduled rebuild also creates a new image artifact and therefore cannot guarantee that production executes the original build. In contrast, the manifest digest is content-addressed and resolves to the precise image manifest selected at deployment time.
Thus, for deterministic and immutable production image selection, reference the container image by its SHA digest rather than by a mutable tag .
Study Guide references: Azure Container Registry # image manifests and digests; image addressing; tagging
/versioning recommendations; immutable deployment references.
NEW QUESTION # 58
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