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Microsoft Developing AI Cloud Solutions on Azure AI-200 Dumps in PDF

Free Microsoft AI-200 Real Questions (page: 1)

AI-200 PDF — 140 Questions

This is a case study. Case studies are not timed separately from other exam sections. You can use as much exam time as you would like to complete each case study. However, there might be additional case studies or other exam sections. Manage your time to ensure that you can complete all the exam sections in the time provided. Pay attention to the Exam Progress at the top of the screen so you have sufficient time to complete any exam sections that follow this case study.
To answer the case study questions, you will need to reference information that is provided in the case. Case studies and associated questions might contain exhibits or other resources that provide more information about the scenario described in the case. Information provided in an individual question does not apply to the other questions in the case study.
A Review Screen will appear at the end of this case study. From the Review Screen, you can review and change your answers before you move to the next exam section. After you leave this case study, you will NOT be able to return to it.
To start the case study
To display the first question in this case study, select the "Next" button. To the left of the question, a menu provides links to information such as business requirements, the existing environment, and problem statements. Please read through all this information before answering any questions.
When you are ready to answer a question, select the "Question" button to return to the question.
Background
Fabrikam Inc. is a global retail analytics company that provides AI-driven demand forecasting and product recommendation services to online retailers. The company is modernizing its solution to run entirely on Microsoft Azure.
The platform ingests transaction data, generates embeddings for semantic retrieval, performs vector similarity search, and returns product recommendations through containerized microservices. Developers use Python and Azure SDKs. Operations teams manage container orchestration, scaling, monitoring, and security.
The solution must meet strict performance, scalability, and security requirements.
Current environment
Application architecture
-The Recommendation engine is a customer-facing HTTP API running as a containerized Python application. The engine is deployed to Azure Container Apps (ACA).
-Embeddings are stored in Azure Database for PostgreSQL by using pgvector.
-Semantic retrieval uses metadata filtering combined with vector similarity search.
-Azure Managed Redis is used as a caching layer.
-Front-end and API workloads are deployed to Azure Container Apps (ACA).
-Batch model retraining workloads run in Azure Kubernetes Service (AKS).
Container and CI/CD
-Container images are stored in Azure Container Registry (ACR).
-CI/CD uses ACR Tasks to build images on commit.
-ACA environments support revision management.
-AKS workloads are deployed by using Kubernetes manifest files stored in Git.
Monitoring
-Logs are collected in Azure Monitor.
-Teams inspect container logs and Kubernetes events when troubleshooting.
-Developers write KQL queries to analyze latency spikes.
Business requirements
Customer experience: Maintain a seamless, low-latency recommendation experience for end-users, even during unpredictable seasonal traffic spikes. Operational cost efficiency: Minimize compute expenditures by deallocating resources during periods of inactivity and by preventing runaway scaling costs. Data integrity and freshness: Ensure that product recommendations always reflect the most current catalog metadata and pricing to prevent customer dissatisfaction. Security and compliance: Adhere to a Zero Trust security model by eliminating long-lived credentials and centralizing the management of all sensitive secrets. Global scalability: Support the rapid ingestion of millions of new product embeddings daily without degrading query performance for existing retailers.
Technical requirements
Performance: Semantic search latency must remain under 200 milliseconds at peak load. Database optimization: Use pgvector for embeddings and implement metadata filtering to reduce compute overhead. Configure compute and memory appropriately for vector workloads to ensure high-dimensional index residency in RAM and efficient mathematical throughput. Vector similarity calculations must be performed only against products that satisfy mandatory metadata constraints. Database performance: Database connections must support high concurrency with minimal latency through the implementation of connection optimization. Data load strategy: To ensure maximum ingestion throughput, secondary indexes must be applied only after bulk loading of embeddings is complete. Caching: Redis cache entries must expire automatically after 10 minutes. Implement a reactive mechanism to invalidate cache entries upon metadata updates. Identity: Use managed identities for all service-to-service and service-to-database authentication. Plain-text credentials in configuration files are strictly prohibited. Secret management: All secrets must be stored centrally. Secrets must be rotated automatically by using a centralized lifecycle policy. Scaling: Use Kubernetes event-driven autoscaling (KEDA) for event-driven scaling. The Recommendation API must scale based on HTTP traffic, while batch jobs must scale based on queue length and support scale-to-zero. CI/CD: All images must be stored in Azure Container Registry. Use ACR Tasks to automate image builds triggered by source code commits. Monitoring: Use KQL to analyze performance telemetry and troubleshoot microservice connectivity failures. Inspect logs and events when troubleshooting AKS and ACA.

HOTSPOT (Drag and Drop is not supported)
You need to configure the database resources for the Azure Database for PostgreSQL instance.
How should you complete the configuration to meet the business and technical requirements? To answer, select the appropriate options in the answer area.
Note: Each correct selection is worth one point.
Hot Area:

  1. See Explanation section for answer.

Answer(s): A

Explanation:




Box 1: Increase compute vCores Technical requirements, Performance: Semantic search latency must remain under 200 milliseconds at peak load.
To reduce semantic search latency under 200 milliseconds at peak load, the best action is to Increase compute vCores.
Vector similarity search and semantic retrieval are highly CPU-intensive operations. The math behind vector distance calculations (such as Cosine similarity, Dot Product, or Euclidean distance) relies heavily on mathematical operations executed per query.
When a system experiences peak load, compute vCores easily become the primary bottleneck. Adding more vCores directly increases parallel processing capacity, dramatically shortening the execution time of similarity calculations and keeping retrieval latency within the required 200 ms SLA.
Box 2: Increase memory allocation The best action is to increase memory allocation.
High-dimensional vector indexes (such as HNSW or IVFFlat managed by the pgvector extension) are highly resource-intensive and rely heavily on RAM. To maintain fast vector similarity searches and avoid high-latency disk operations, the entire vector index must fit into memory (RAM residency). Scaling up the database instance's memory directly expands the PostgreSQL shared buffers and cache, ensuring the high-dimensional index remains resident in RAM for rapid semantic retrieval.
Box 3: Enable storage autoscale Enable storage autoscale is the best action to support the continuous ingestion of transaction-based embeddings.
Continuous Ingestion Demands Dynamic Space: Continuous transaction processing causes vector databases (such as Azure Database for PostgreSQL with pgvector or Azure SQL Database) to expand constantly over time.
Preventing Ingestion Failures: If storage reaches capacity limits, the database switches into a read-only state. This immediately fails and halts all incoming real-time embedding write operations. Enabling storage autoscale allows the environment to dynamically provision storage on the fly without downtime.


Reference:

https://dl.acm.org/doi/10.1145/3695053.3731013 https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-database-postgresql/ https://learn.microsoft.com/en-us/azure/architecture/guide/technology-choices/vector-search




This is a case study. Case studies are not timed separately from other exam sections. You can use as much exam time as you would like to complete each case study. However, there might be additional case studies or other exam sections. Manage your time to ensure that you can complete all the exam sections in the time provided. Pay attention to the Exam Progress at the top of the screen so you have sufficient time to complete any exam sections that follow this case study.
To answer the case study questions, you will need to reference information that is provided in the case. Case studies and associated questions might contain exhibits or other resources that provide more information about the scenario described in the case. Information provided in an individual question does not apply to the other questions in the case study.
A Review Screen will appear at the end of this case study. From the Review Screen, you can review and change your answers before you move to the next exam section. After you leave this case study, you will NOT be able to return to it.
To start the case study
To display the first question in this case study, select the "Next" button. To the left of the question, a menu provides links to information such as business requirements, the existing environment, and problem statements. Please read through all this information before answering any questions.
When you are ready to answer a question, select the "Question" button to return to the question.
Background
Fabrikam Inc. is a global retail analytics company that provides AI-driven demand forecasting and product recommendation services to online retailers. The company is modernizing its solution to run entirely on Microsoft Azure.
The platform ingests transaction data, generates embeddings for semantic retrieval, performs vector similarity search, and returns product recommendations through containerized microservices. Developers use Python and Azure SDKs. Operations teams manage container orchestration, scaling, monitoring, and security.
The solution must meet strict performance, scalability, and security requirements.
Current environment
Application architecture
-The Recommendation engine is a customer-facing HTTP API running as a containerized Python application. The engine is deployed to Azure Container Apps (ACA).
-Embeddings are stored in Azure Database for PostgreSQL by using pgvector.
-Semantic retrieval uses metadata filtering combined with vector similarity search.
-Azure Managed Redis is used as a caching layer.
-Front-end and API workloads are deployed to Azure Container Apps (ACA).
-Batch model retraining workloads run in Azure Kubernetes Service (AKS).
Container and CI/CD
-Container images are stored in Azure Container Registry (ACR).
-CI/CD uses ACR Tasks to build images on commit.
-ACA environments support revision management.
-AKS workloads are deployed by using Kubernetes manifest files stored in Git.
Monitoring
-Logs are collected in Azure Monitor.
-Teams inspect container logs and Kubernetes events when troubleshooting.
-Developers write KQL queries to analyze latency spikes.
Business requirements
Customer experience: Maintain a seamless, low-latency recommendation experience for end-users, even during unpredictable seasonal traffic spikes. Operational cost efficiency: Minimize compute expenditures by deallocating resources during periods of inactivity and by preventing runaway scaling costs. Data integrity and freshness: Ensure that product recommendations always reflect the most current catalog metadata and pricing to prevent customer dissatisfaction. Security and compliance: Adhere to a Zero Trust security model by eliminating long-lived credentials and centralizing the management of all sensitive secrets. Global scalability: Support the rapid ingestion of millions of new product embeddings daily without degrading query performance for existing retailers.
Technical requirements
Performance: Semantic search latency must remain under 200 milliseconds at peak load. Database optimization: Use pgvector for embeddings and implement metadata filtering to reduce compute overhead. Configure compute and memory appropriately for vector workloads to ensure high-dimensional index residency in RAM and efficient mathematical throughput. Vector similarity calculations must be performed only against products that satisfy mandatory metadata constraints. Database performance: Database connections must support high concurrency with minimal latency through the implementation of connection optimization. Data load strategy: To ensure maximum ingestion throughput, secondary indexes must be applied only after bulk loading of embeddings is complete. Caching: Redis cache entries must expire automatically after 10 minutes. Implement a reactive mechanism to invalidate cache entries upon metadata updates. Identity: Use managed identities for all service-to-service and service-to-database authentication. Plain-text credentials in configuration files are strictly prohibited. Secret management: All secrets must be stored centrally. Secrets must be rotated automatically by using a centralized lifecycle policy. Scaling: Use Kubernetes event-driven autoscaling (KEDA) for event-driven scaling. The Recommendation API must scale based on HTTP traffic, while batch jobs must scale based on queue length and support scale-to-zero. CI/CD: All images must be stored in Azure Container Registry. Use ACR Tasks to automate image builds triggered by source code commits. Monitoring: Use KQL to analyze performance telemetry and troubleshoot microservice connectivity failures. Inspect logs and events when troubleshooting AKS and ACA.

HOTSPOT (Drag and Drop is not supported)
You need to deploy a batch retraining workload.
How should you complete the scaling configuration? To answer, select the appropriate options in the answer area.
Note: Each correct selection is worth one point.
Hot Area:

  1. See Explanation section for answer.

Answer(s): A

Explanation:




Box 1: Azure Service Bus scaler An Azure Service Bus scaler needs to be set up to meet this requirement.
Queue-Depth Metrics: To trigger scaling based strictly on queue depth, the orchestration layer (such as Azure Container Apps or AKS using KEDA) requires a scaler that can natively communicate with the message broker and monitor metrics like active message count.
Scale-to-Zero Support: Unlike standard resource scalers, an Azure Service Bus scaler enables event-driven batch workloads to spin up consumers when messages arrive and scale all the way down to zero instances when the queue is completely empty.
Box 2: Minimum replicas = 0. You need to set Minimum replicas = 0.
Setting the minimum replicas to 0 enables the system to scale down to zero instances when there are no messages left in the queue, completely deallocating resources and eliminating idle compute costs.
Box 3: Maximum replicas = 10 To prevent uncontrolled burst scaling during your batch retraining workloads, you need to set maximum replicas = 10.
Enforcing Upper Bounds: Allowing the system to determine the maximum replica gives the underlying autoscaler (such as KEDA or the Horizontal Pod Autoscaler in Azure Kubernetes Service) the freedom to scale out infinitely or up to large default platform limits. This directly risks uncontrolled burst scaling when a massive batch queue is processed.
Resource and Cost Protection: Hard-coding a ceiling (like maximum replicas = 10) ensures that the batch workload cannot consume more cluster resources than allocated, preventing resource starvation for customer-facing recommendation APIs and keeping operational costs predictable.


Reference:

https://azure.github.io/aca-dotnet-workshop/aca/09-aca-autoscale-keda/ https://kserve.github.io/website/docs/reference/crd-api



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?

  1. Tag the image as production and deploy it by using the production tag.
  2. Schedule nightly rebuilds of the image.
  3. Configure deployment to use the latest tag.
  4. Identify the image by using its SHA digest.

Answer(s): D

Explanation:

Deploying a container image by its unique SHA-256 digest digest instead of its tag ensures a predictable and immutable deployment. Tags can be overwritten or moved to different image builds, but a digest is a cryptographic hash that never changes.


Reference:

https://container-registry.com/posts/container-image-versioning/



A container in an AKS cluster repeatedly restarts.
Pod events show probe failures, although node-level CPU and memory metrics are normal.
You need to diagnose the cause of the repeating restarts.
What should you do first?

  1. Scale the deployment to more replicas.
  2. Decrease the initialDelaySeconds for the container liveness probe.
  3. Drain and reboot the node hosting the pod.
  4. Inspect the pod events and container logs.

Answer(s): D

Explanation:

Inspecting the pod events and container logs is the correct first action to take.
Because node-level metrics are normal, the issue is isolated to the specific workload or its individual container boundaries. Pod events will clarify exactly which health probe (liveness or readiness) is triggering the restart, and the logs will reveal why the application inside the container is failing to respond.


Reference:

https://aws.plainenglish.io/why-your-kubernetes-pod-is-crashing-and-how-to-fix-it-5c71f8d2779a? gi=a46b335270ff



You develop a message-processing service deployed to Azure Container Apps. The service reads messages from an Azure Service Bus queue.
The solution must minimize costs by ensuring NO compute resources are consumed when the queue is empty.
You need to configure scaling for the service.
Which two actions should you perform? Each correct answer presents part of the solution.
Note: Each correct selection is worth one point.

  1. Increase the scaling rule to allow for the maximum running replica count.
  2. Configure the scaling rule to allow for the termination of all active replicas.
  3. Configure a Kubernetes Event-driven Autoscaler rule that monitors queue length.
  4. Enable HTTP ingress concurrency scaling.

Answer(s): B,C

Explanation:

[C] You can configure an Azure Service Bus scale rule in Azure Container Apps, which utilizes built-in KEDA (Kubernetes Event-driven Autoscaling) to monitor queue length and scale the app from zero instances to meet your processing demands.
[B] Configure the scaling rule to allow for the termination of all active replicas is the correct action to take.
To ensure that no compute resources are consumed when the queue is empty, you must set the minimum replica count (minReplicas) to 0 in your Azure Container Apps scaling configuration.
When the Azure Service Bus queue has zero messages, KEDA will scale the container replicas down to zero, stopping all compute billing.


Reference:

https://learn.microsoft.com/en-us/azure/container-apps/scale-app



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.

  1. Timer trigger
  2. Source code commit trigger
  3. Registry event trigger
  4. Base image update trigger
  5. Webhook notification trigger

Answer(s): A,B,D

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



DRAG DROP (Drag and Drop is not supported)
You are developing several microservices to run on Azure Container Apps.
The microservices must allow HTTPS access by using a custom domain.
You need to configure the custom domain in Azure Container Apps.
In which order should you perform the actions? To answer, move all actions from the list of actions to the answer area and arrange them in the correct order.
Select and Place:

  1. See Explanation section for answer.

Answer(s): A

Explanation:





Step 1: Enable ingress You must first expose your container app to external traffic to generate the default fully qualified domain name (FQDN) needed for DNS mapping.
Step 2: Add DNS records to the domain provider Log into your domain registrar to create the required TXT (for verification) and CNAME/A records pointing to your container app.
Step 3: Validate the custom domain name Azure checks your DNS records to confirm that you actually own the domain before allowing it to be linked.
Step 4: Add the custom domain name Once validation passes, you officially add and register the custom domain name within the Azure Container App configuration.
Step 5: Bind the certificate Finally, bind an SSL/TLS certificate to the custom domain to secure the connection and enable HTTPS access.


Reference:

https://learn.microsoft.com/en-us/azure/container-apps/ingress-overview



You plan to deploy an Azure Container app.
You need to configure the container app to support session affinity.
Which ingress type and revision mode should you assign to the container app?

  1. TCP ingress type and single revision mode
  2. TCP ingress type and multiple revision mode
  3. HTTP ingress type and multiple revision mode
  4. HTTP ingress type and single revision mode

Answer(s): D

Explanation:

HTTP ingress type and single revision mode must be assigned. Session affinity (sticky sessions) requires an HTTP-based proxy layer to track and route client requests to the same replica, which is incompatible with raw TCP ingress. Single revision mode ensures standard traffic flow without multi-revision splitting complexity.


Reference:

https://learn.microsoft.com/en-us/azure/container-apps/ingress-environment-configuration



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