AI-200 Exam Dumps : Developing AI Cloud Solutions on Azure

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.

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Service fails to communicate with another service: Inspect Kubernetes service endpoints.

Pod restarts repeatedly: Inspect container logs.

Readiness probe failures are observed: Inspect Pod descriptions.

For a service-to-service communication failure , first inspect the Kubernetes service endpoints . Microsoft’s AKS troubleshooting guidance recommends verifying that the destination pod IP address and application port appear as endpoints for the Kubernetes Service. An empty or incorrect endpoint list commonly indicates a selector/label mismatch, incorrect port configuration, or unavailable backend pods.

For a pod that restarts repeatedly , inspect the container logs , including the previous container instance when necessary. Microsoft specifically recommends kubectl logs < pod > --previous for workloads that repeatedly restart because it exposes application errors and termination messages from the container immediately before the restart.

For readiness probe failures , inspect the Pod description by using kubectl describe pod. The description exposes the configured health probes, pod conditions, container state, and associated probe-failure events, allowing you to determine whether the probe path, port, timing, or application readiness behavior is incorrect. Microsoft recommends reviewing kubectl describe output when pods are not Ready or health probes are failing.

Therefore, the correct mapping is service endpoints → container logs → Pod descriptions .

Study Guide references: AKS troubleshooting; Kubernetes Services and endpoints; container logging; Pod health; readiness/liveness probes; kubectl describe and kubectl logs.