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Microsoft AI-200 Exam Syllabus Topics:

SectionWeightObjectives
Secure, monitor, and optimize AI solutions20%- Manage security and configuration
  • 1. Managed identities and access control
  • 2. App Configuration for dynamic settings
  • 3. Azure Key Vault for secrets, keys, and certificates
- Implement observability and reliability
  • 1. Logging, metrics, and distributed tracing
  • 2. OpenTelemetry and Azure Monitor integration
  • 3. Optimize performance, cost, and scalability
Integrate backend services and build event-driven architectures25%- Implement messaging and event systems
  • 1. Azure Event Grid for event-driven processing
  • 2. Connect services and expose APIs securely
  • 3. Azure Service Bus for reliable messaging
- Build serverless APIs and workflows
  • 1. Azure Functions for AI integration and processing
  • 2. Orchestrate AI pipelines and workflows
Develop containerized AI solutions on Azure25%- Implement container hosting environments
  • 1. Configure scaling, networking, and security for containers
  • 2. Deploy to Azure Container Apps and Azure Kubernetes Service (AKS)
  • 3. Azure Container Registry: store, version, manage images
- Monitor and troubleshoot containerized workloads
  • 1. Manage configurations and secrets for containers
  • 2. Log analysis, health checks, and performance monitoring
Develop AI solutions using Azure data services30%- Implement vector-enabled databases
  • 1. Azure Managed Redis for caching, streaming, and vector storage
  • 2. Azure Cosmos DB for NoSQL with vector search
  • 3. Azure Database for PostgreSQL with pgvector extension
- Design and optimize data access and retrieval
  • 1. Indexing strategies, query optimization, and consistency models
  • 2. Implement hybrid search and retrieval patterns

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Microsoft Developing AI Cloud Solutions on Azure Sample Questions (Q60-Q65):

NEW QUESTION # 60
You have an Event Grid subscription that triggers an Azure Function.
You need to prevent loss of events in case the endpoint returns an HTTP 400 response. Which action should you perform?

Answer: D


NEW QUESTION # 61
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: B

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 # 62
You are building a RAG (retrieval-augmented generation) solution using Azure AI Foundry. The knowledge base consists of 50,000 PDF documents stored in Azure Blob Storage.
You need to make the document content searchable by the language model with minimal custom code.
What should you use?

Answer: A

Explanation:
Use Azure AI Search integrated vectorization with a skillset . Integrated vectorization automates the main ingestion stages required for RAG: an Azure AI Search indexer can read documents directly from a supported data source such as Azure Blob Storage, extract and split document content into chunks, invoke an embedding model, and store both searchable text and vector representations in the search index. Microsoft specifically states that integrated vectorization reduces development and maintenance effort because developers do not need to implement the chunking and embedding pipeline manually.
A skillset can include a Text Split skill for chunking and an Azure OpenAI Embedding skill or another supported embedding skill for vector generation. At query time, a matching vectorizer can convert user questions into vectors so Azure AI Search can perform similarity or hybrid retrieval for grounding the language model.
Custom text classification solves a different NLP problem. Building Azure Functions to process all 50,000 PDFs manually would introduce unnecessary custom orchestration and maintenance. Azure Table Storage does not provide the native vector-search and RAG indexing capabilities required here.
Study Guide references: Azure AI Search # integrated vectorization; indexers; skillsets; Text Split skill; embedding skills; vector indexes; RAG retrieval.


NEW QUESTION # 63
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?

Answer: B

Explanation:
Azure Container Apps session affinity, also called sticky sessions , routes requests from the same client to the same container-app replica. Microsoft documents that this functionality is implemented using HTTP cookies
, so it requires HTTP ingress rather than raw TCP ingress.
The second requirement is single revision mode . Microsoft explicitly states that session affinity is supported only when the container app is in single revision mode and HTTP ingress is enabled . In single revision mode, all traffic is directed to the latest active revision, allowing the ingress layer to consistently associate a client with a particular replica within that revision.
Multiple revision mode is designed for scenarios such as traffic splitting, blue-green deployments, and A/B testing, where inbound traffic can be distributed among multiple active revisions. That model is incompatible with the documented session-affinity restriction. TCP ingress is also incorrect because sticky-session behavior relies on HTTP cookie affinity rather than generic TCP connection routing.
Therefore, the required configuration is HTTP ingress with single revision mode .
Study Guide references: Azure Container Apps # ingress; sticky sessions/session affinity; revision modes; HTTP routing.


NEW QUESTION # 64
A RAG application ' s retrieval step is returning documents that are topically related but not precisely relevant to the user ' s question.
You need to improve retrieval precision without retraining any model.
What should you do?

Answer: B

Explanation:
Enable semantic ranking in Azure AI Search . Semantic ranking is a query-time secondary re-ranking stage that evaluates the initial candidate results using Microsoft language-understanding models and promotes documents that are more semantically aligned with the user ' s actual query intent. It can operate over BM25 results, hybrid results, and the textual content associated with vector-search results.
This directly addresses the stated problem. Vector similarity retrieval often has strong recall but can return passages that are broadly related rather than specifically useful for answering the question. Microsoft ' s RAG guidance explains that reranking improves precision by taking the retrieved candidate set and reordering it so that the most query-relevant chunks are placed first. This reduces irrelevant context passed to the generation model and improves grounding quality without retraining either the embedding model or the LLM.
Increasing embedding dimensionality alone does not guarantee better relevance. Disabling vector search sacrifices semantic retrieval capability, while reducing the index size arbitrarily removes potentially useful content rather than improving ranking quality.
Therefore, semantic re-ranking is the correct precision-improvement mechanism .
Study Guide references: Azure AI Search # semantic ranker; RAG information retrieval; hybrid/vector search; secondary ranking; relevance optimization.


NEW QUESTION # 65
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