Crucial Exams

Microsoft Azure AI Engineer Associate AI-102 Practice Question

You plan to index several thousand JSON documents stored in Azure Blob Storage by using Azure AI Search. Each document contains a content string that you want to query with both semantic ranking and vector similarity search. You create an index with these fields:

  • id (Edm.String, key)
  • content (Edm.String, searchable) You also create a skillset that calls the Azure OpenAI embedding REST API. When you configure the indexer, which additional step is required to ensure that the generated embeddings are stored in the index so that you can run vector‐based k-nearest neighbor (KNN) queries?

  • Add an output field mapping in the skillset that routes the embedding skill output to a new index field of type Collection(Edm.Single) configured for vector search.

  • Schedule the indexer to run in high‐frequency incremental mode so that new embeddings are generated on every change.

  • Append the query string parameter ?vectorize=true to the Blob Storage connection string used by the data source.

  • Enable semantic configuration on the content field and set its weight to the maximum value.

Microsoft Azure AI Engineer Associate AI-102
Implement knowledge mining and information extraction solutions
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