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| Section | Objectives |
|---|---|
| Topic 1: Implement and monitor AI workloads | - Monitor performance and troubleshoot issues - Deploy AI models and services |
| Topic 2: Implement Azure AI solutions | - Implement natural language processing solutions - Implement generative AI solutions using Azure OpenAI - Implement knowledge mining with Azure AI Search - Implement computer vision solutions |
| Topic 3: Plan and manage Azure AI solutions | - Plan security and compliance requirements - Monitor and optimize AI solutions - Select appropriate Azure AI services |
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NEW QUESTION # 118
An AI platform uses App Configuration for feature flags and endpoint routing.
The platform stores secrets alongside configuration data and does NOT support dynamic configuration refresh.
You need to redesign the configuration approach so that:
configuration supports dynamic refresh;
secrets are stored outside App Configuration;
the application can securely access both App Configuration and Key Vault.
You need to enable secure dynamic configuration management for the platform.
Which three actions should you perform? Each correct answer presents part of the solution. Choose three.
NOTE: Each correct selection is worth one point.
Answer: C,D,E
Explanation:
Detailed Explanation: The original answer omitted the secret-storage requirement. API keys and other secrets should be held in Key Vault, while App Configuration should retain non-secret configuration and Key Vault references. Dynamic refresh is implemented through the App Configuration refresh/polling mechanism rather than requiring an application restart. Managed identity provides credential-free access to both services in Azure. Environment variables do not provide centralized dynamic refresh, and a stored service-principal secret creates exactly the credential-management problem the design is intended to eliminate.
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 | Dynamic configuration in ASP.NET Core | Use Key Vault references in App Configuration | Managed identities for Azure resources
NEW QUESTION # 119
You are designing an Azure Database for PostgreSQL table for semantic search. Queries frequently filter on the created_at column.
The schema must support vector similarity search and reliable date filtering.
You need to ensure that the table meets the 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,B
Explanation:
To meet the requirements, store embeddings in a pgvector column and use a typed timestamp column for created_at. These two steps enable high-dimensional vector similarity operations and allow efficient, reliable date-based filtering.
Store embeddings in a pgvector column: Enables the database to store and query high- dimensional vector embeddings using native similarity operators.
Use a typed timestamp column for created_at: Ensures accurate date-and-time comparisons, indexing, and dependable performance when filtering queries by creation date.
Reference:
https://learn.microsoft.com/en-us/training/modules/implement-vector-search-azure-database-postgresql/
NEW QUESTION # 120
You are developing an Azure Functions app.
All functions in the app meet the following requirements:
Run until either a successful run or until 10 run attempts occur.
Ensure that there are at least 20 seconds between attempts for up to 15 minutes.
You need to configure the host.json file.
How should you complete the code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 121
You store embeddings in a property named embedding in an Azure Cosmos DB for NoSQL container.
You must retrieve the five most similar documents to an input embedding.
You need to construct the similarity query.
What should you do?
Answer: A
Explanation:
To retrieve the five most similar documents from Azure Cosmos DB for NoSQL using vector search, you must limit the number of results.
When performing vector similarity searches using the VectorDistance() function, you must restrict the output size directly in the query.
Reference:
https://stemhash.com/semantic-search-with-llms/
NEW QUESTION # 122
A customer support bot built with Azure AI Foundry Agent Service must ground its answers strictly in an internal knowledge base and avoid hallucinating unsupported facts.
What should you implement?
Answer: C
Explanation:
Connect the agent to an Azure AI Search knowledge source and explicitly constrain its instructions to answer from retrieved evidence. Microsoft Foundry Agent Service can use Azure AI Search to retrieve proprietary organizational content and ground model responses in that content. Retrieved documents provide the factual context from which the model generates its answer, and Azure AI Search can return source references for attribution.
Microsoft ' s RAG guidance also specifically recommends using clear system instructions that require the model to stay within retrieved content when hallucination risk must be reduced. If the available evidence does not support an answer, the instructions should direct the agent to state that the information is unavailable rather than inventing facts. Retrieval grounding substantially reduces unsupported generation, although production systems should still evaluate outputs because grounding alone does not mathematically guarantee zero hallucinations.
Increasing temperature would generally increase output variability and is contrary to the objective. A larger context window without retrieval does not give the model access to the organization ' s internal knowledge base. Disabling content filtering weakens safety controls and has no role in factual grounding.
Study Guide references: Microsoft Foundry Agent Service # Azure AI Search tool; RAG grounding; knowledge sources; source attribution; grounding instructions.
NEW QUESTION # 123
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