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| Section | Objectives |
|---|---|
| Topic 1: Plan and manage Azure AI solutions | - Monitor and optimize AI solutions - Plan security and compliance requirements - Select appropriate Azure AI services |
| Topic 2: Implement and monitor AI workloads | - Monitor performance and troubleshoot issues - Deploy AI models and services |
| Topic 3: Implement Azure AI solutions | - Implement generative AI solutions using Azure OpenAI - Implement computer vision solutions - Implement natural language processing solutions - Implement knowledge mining with Azure AI Search |
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NEW QUESTION # 85
Hotspot Question
You configure an Event Grid event subscription that routes AI file-upload events to an Azure Function endpoint.
Events must be delivered only in the following conditions:
- The event path starts with /uploads/ai/.
- The payload property data.fileType is "pdf."
You must store undelivered events for later investigation and reprocessing if the subscriber endpoint cannot accept an event after multiple retries.
You need to configure the filtering and reliability settings.
Which configurations should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Advanced filter
You should use an advanced filter to check that the path starts with /uploads/ai/ and that the payload property matches pdf.
Box 2: Advanced filter
Payload Property Condition: Use an Advanced Filter. While Subject filters handle prefix/suffix matching and Event Type filters handle the event schema type, Advanced filters are required to inspect deeply nested properties inside the event's data payload.
Box 3: Dead-letter destination
You should configure a Dead-letter destination to store undelivered events for later investigation and reprocessing.
Reference:
https://oneuptime.com/blog/post/2026-02-16-how-to-filter-azure-event-grid-subscription-events-with-advanced-filters/view
https://oneuptime.com/blog/post/2026-02-16-how-to-use-azure-event-grid-with-power-automate-to-react-to-blob-storage-events/view
NEW QUESTION # 86
Your application stores conversation history so a multi-turn chatbot can reference earlier turns, but the context window is limited. You need to keep the most relevant history within token limits.
What should you implement?
Answer: D
Explanation:
Progressive summarization of older conversation turns, combined with keeping recent turns verbatim, is a standard technique to preserve conversational context within a limited token budget without abruptly losing history.
NEW QUESTION # 87
You need to configure a connection string for the partner-facing service according to the technical requirements.
What should you use?
Answer: A
Explanation:
Detailed Explanation: Azure Key Vault references in App Service settings satisfy the requirement to keep secrets out of container images, source control, and directly stored application configuration. App Service resolves the referenced secret at runtime by using the app identity, so the application can consume the value as a normal setting without embedding credentials in the image. GitHub secrets are build/deployment secrets rather than a runtime App Service secret-delivery mechanism. Dockerfile ENV instructions would place secret material in the image configuration and violate the case requirements.
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 | Use Key Vault references for App Service and Functions | Managed identities for Azure resources
Topic 2, 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.
NEW QUESTION # 88
An application deployed to AKS depends on an internal API hosted in the same cluster. All pods are healthy and report Ready status, but requests between the services time out.
You need to determine whether the issue is related to Kubernetes service configuration or application code.
What should you do first?
Answer: C
NEW QUESTION # 89
You must ensure an Azure OpenAI-powered application never exceeds a defined token-per- minute budget across all users to avoid runaway costs. What should you configure?
Answer: C
Explanation:
Each Azure OpenAI deployment can have Tokens-Per-Minute (TPM) and Requests-Per-Minute (RPM) limits configured, which cap consumption at the deployment level and directly control cost exposure.
NEW QUESTION # 90
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