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| Section | Objectives |
|---|---|
| Implement Azure AI solutions | - Implement generative AI solutions using Azure OpenAI - Implement natural language processing solutions - Implement knowledge mining with Azure AI Search - Implement computer vision solutions |
| Implement and monitor AI workloads | - Monitor performance and troubleshoot issues - Deploy AI models and services |
| Plan and manage Azure AI solutions | - Select appropriate Azure AI services - Plan security and compliance requirements - Monitor and optimize AI solutions |
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NEW QUESTION # 20
You have a newly provisioned Azure subscription. You are designing a custom Event Grid workflow for AI inference events.
You need to implement the Event Grid components to support routing of high-confidence events to a downstream processor.
Which three 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:
Verified Answer: 1) Register the Event Grid resource provider; 2) create a custom topic; 3) create an event subscription.
Detailed Explanation: A newly provisioned subscription must have the Event Grid resource provider available before Event Grid resources can be created. The publisher needs a custom topic as the event-ingress resource, and routing to a downstream processor is then defined by an event subscription on that topic. The event subscription can include filters such as event type or data fields so that only high-confidence events reach the processor. Creating a partner topic or domain is unnecessary for the stated custom workflow.
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 | Create an Event Grid custom topic or domain
NEW QUESTION # 21
Case Study 1 - Fabrikam Inc.
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.
You need to configure image builds for a new service to meet the technical requirements.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: A,D
Explanation:
Technical requirements, CI/CD: All images must be stored in Azure Container Registry. Use ACR Tasks to automate image builds triggered by source code commits.
The two best actions to meet the requirements are to create and configure an ACR Task with a source repository context and then push updated code to the source repository.
The core constraint explicitly dictates using ACR Tasks to automate image builds triggered by source code commits.
[D] Create and configure an ACR Task with a source repository context: This step directly establishes the automation hook. By defining the repository context (such as GitHub or Azure Repos) during task creation, Azure Container Registry is instructed exactly where to watch for code modifications.
[F] Push updated code to the source repository: Once the ACR Task is configured with the source repository context, pushing new commits acts as the native trigger. ACR automatically intercepts the commit webhook, spins up a transient cloud agent, builds the Python container image via the Dockerfile, and pushes it directly into your registry.
Reference:
https://learn.microsoft.com/en-us/azure/container-registry/container-registry-tasks-overview
NEW QUESTION # 22
You have an Azure subscription named Sub1 that contains a resource group named RG1 and a Service Bus queue named SB1.
You plan to implement an Azure Event Grid push even: subscription that will deliver an event lo SB1 whenever a resource is created, modified, or deleted in RG1. You must minimize the development and configuration efforts.
You need to create an Event Grid topic for your planned implementation
Which type of event topic should you create?
Answer: A
NEW QUESTION # 23
You are designing Azure Functions for three different backend workloads.
Each workload requires one of the following dispatching models:
- Return an immediate response to a client.
- Process background work from a queue.
- Run code on a fixed schedule.
You need to select the trigger for each requirement.
Which triggers should you select? To answer, move the appropriate triggers to the correct requirements. You may use each trigger 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:
* Return an immediate response to a client: HTTP trigger
* Process background work from a queue: Queue Storage trigger
* Run code on a fixed schedule: Timer trigger
Use an HTTP trigger when a function must be invoked by an HTTP request and return a response directly to the calling client. Microsoft documents HTTP triggers as the standard mechanism for building serverless APIs and handling web requests or webhooks. This makes the HTTP trigger the correct fit for synchronous request- response workloads.
For asynchronous background processing, use an Azure Queue Storage trigger . A queue-triggered function runs when messages are added to an Azure Storage queue. This decouples the producer from the consumer and is appropriate for background processing where work can be buffered and processed independently of the original request. Microsoft also documents target-based scaling for Queue Storage triggers on supported Azure Functions hosting plans.
For scheduled execution, use a Timer trigger . Microsoft states that a timer trigger runs a function according to a configured schedule, typically expressed with an NCRONTAB/CRON expression. This makes it the correct choice for periodic maintenance, scheduled synchronization, reporting, or other fixed-time workloads.
Event Grid and Blob Storage triggers are event-driven mechanisms for event notifications and blob changes respectively, but neither matches these three stated dispatching requirements.
Study Guide references: Azure Functions # HTTP triggers; Queue Storage triggers; Timer triggers; event- driven and scheduled execution.
NEW QUESTION # 24
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals.
You are using Azure Monitor Application Insights to investigate a production API. You open the Logs blade and set the time range to Last 24 hours.
An engineer recommends the following query to count requests by result code and sort the results from most frequent to least frequent:
requests
| summarize request_count = count() by resultCode
| order by request_count desc
You need to determine whether the query returns the number of requests grouped by result code and sorted from most to least frequent.
Solution: The result codes are sorted alphabetically.
Does the solution meet the goal?
Answer: B
Explanation:
The solution does not meet the goal because the query does not sort by resultCode. The summarize operator groups request records by each distinct resultCode value and calculates the number of requests in each group by using count().
The resulting table contains a resultCode column and a calculated request_count column. The next statement:
| order by request_count desc
sorts the output by request_count in descending order , which places the most frequently occurring result code first and the least frequent last.
Therefore, the output is sorted by frequency , not alphabetically or numerically by the resultCode field.
If alphabetical sorting by result code were required, the query would instead use a statement such as:
| order by resultCode asc
That is not what the provided query does.
Because the Logs blade is already scoped to the Last 24 hours , the query operates over that selected time range unless additional time filtering is explicitly added.
Study Guide references: Azure Monitor Logs; Application Insights requests table; KQL summarize; count() aggregation; order by; descending sort semantics.
NEW QUESTION # 25
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