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Microsoft AI-200 Exam Syllabus Topics:

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

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Microsoft Developing AI Cloud Solutions on Azure Sample Questions (Q70-Q75):

NEW QUESTION # 70
You are developing a solution that uses several Azure Service Bus queues. You create an Azure Event Grid subscription for the Azure Service Bus namespace. You use Azure Functions as subscribers to process the messages.
You need to ensure that events can be sent to Azure Event Grid from the queues. The solution must use the principle of least privilege and minimize costs.
Which Azure Service Bus role and tier level should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

Verified Answer: Tier: Premium. Role: Azure Service Bus Data Receiver.
Detailed Explanation: Microsoft documents Event Grid integration for Service Bus queues and topics as requiring a Service Bus Premium namespace. The Azure Function subscriber must then receive messages from the queue; the least-privilege built-in data-plane role for that purpose is Azure Service Bus Data Receiver. Data Sender does not permit receiving, and Data Owner is broader than necessary. Premium therefore satisfies the feature prerequisite, while Data Receiver satisfies the stated least-privilege requirement.
Study Guide Alignment: Azure service integration: Service Bus, Event Grid, Azure Functions triggers
/bindings, and event-driven processing.
Official Microsoft Learn References: AI-200 Study Guide | Service Bus to Event Grid integration | Authenticate Service Bus with Microsoft Entra ID


NEW QUESTION # 71
Drag and Drop Question
A recommendation system stores 3 million embeddings in Azure Database for PostgreSQL.
During peak hours, P95 similarity-query latency increases, and the cache hit ratio drops significantly.
You suspect that the vector index working set no longer fits in memory, resulting in more disk reads.
You need to scale server resources and validate the outcome.
Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

Answer:

Explanation:

Explanation:
Step 1: Before peak hours, run a fixed workload to capture baseline P95 latency and capture EXPLAIN ANALYZE for the vector query Establishing a baseline is crucial for performance tuning. Running EXPLAIN ANALYZE provides deep insight into whether the query is actually using the vector index (like HNSW or IVFFlat) or falling back to a slow sequential scan.
Step 2: Scale to a tier with more memory per vCore
Since the problem states that the vector index working set no longer fits in memory, scaling to a tier with higher memory (such as Memory-Optimized tiers) directly solves the root cause by allowing the RAM to fully cache the vector index, eliminating slow disk reads.
Step 3: Re-run the same benchmark and compare P95 and P99 latency
To validate the outcome of your scaling operations, you must run the identical benchmark workload and compare high-percentile latencies (P95, P99) against your baseline to prove the bottleneck was resolved.
Step 4: Review memory pressure and cache hit ratio during peak hours and compare them to the baseline This step confirms the success of the resource scaling. A successful remediation will show a significantly increased cache hit ratio and reduced memory pressure/disk I/O compared to the baseline metrics.
Reference:
https://www.pgedge.com/blog/ai-features-in-pgadmin-ai-insights-for-explain-plans


NEW QUESTION # 72
You need to deploy a batch embedding workload according to the planned application architecture.
What should you use?

Answer: C

Explanation:
The batch embedding workload is planned to run in Azure Kubernetes Service (AKS) , and Kubernetes workloads should be defined declaratively by using YAML manifest files . Microsoft states that a Kubernetes manifest defines the desired state of cluster resources, including Deployments, Jobs, Services, ConfigMaps, and other Kubernetes objects. These manifests can then be deployed by using kubectl apply -f < manifest > .
yaml.
For this case, YAML is particularly appropriate because the technical requirements emphasize repeatable, version-controlled, automated deployments . A batch vector embedding regeneration process would typically be represented as a Kubernetes workload such as a Job or another batch-oriented resource, with its configuration committed to Git alongside the rest of the deployment definitions.
kubectl run and kubectl create are imperative commands and are less appropriate for a controlled Git-based deployment model because they do not inherently preserve the complete desired-state definition as source- controlled configuration. XML is not the standard Kubernetes manifest format. az aks commands are primarily used to provision and manage the AKS cluster itself rather than declaratively define application workloads running inside the cluster.
Therefore, the correct choice is YAML-formatted Kubernetes manifest files .
Study Guide references: Azure Kubernetes Service # Kubernetes manifests; declarative deployments; kubectl apply; Git-based workload configuration.


NEW QUESTION # 73
Drag and Drop Question
You plan to deploy a web application to AKS.
The solution must:
- Scale out the application by adding more pods during peak CPU usage.
- Expose the application internally within the cluster only.
You need to configure a Kubernetes resource for each requirement.
Which resources should you configure? To answer, move the appropriate resources to the correct requirements. You may use each resource 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:
Box 1: HorizontalPodAutoscaler
Horizontal Pod Autoscaler (HPA) is the AKS feature to use. It automatically scales the number of pods in a deployment based on observed CPU utilization or other metrics.
Box 2: ClusterIP service
Use a ClusterIP service or internal LoadBalancer with annotations to expose your web application internally within an Azure Kubernetes Service (AKS) cluster only.
Reference:
https://learn.microsoft.com/en-us/azure/aks/optimize-aks-costs
https://learn.microsoft.com/en-us/azure/aks/concepts-network-services


NEW QUESTION # 74
You are designing a message based order processing system by using Azure Service Bus.
Each order message must be processed in the same sequence it was received. In addition, a separate audit service must receive copies of all order messages far compliance tracking You need to configure a Service Bus namespace and its entities to meet the ordering and audit requirements Which service bus options should you use? To answer, select the appropriate options m the answer area NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:


NEW QUESTION # 75
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