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AIF-C01 Practice Question 4: Embeddings Semantic Search

AWS Certified AI Practitioner (AIF-C01) · Test 1 · question 4 of 10

Fundamentals of GenAImedium

Employees complain that the intranet search engine fails when their wording differs from the documents. For example, a search for "time off for a new baby" doesn't return the page titled "Parental leave policy". Which technique addresses this problem most directly?

Select one answer
Show answer and explanation

Answer:

  • C. Converting documents and queries into vector embeddings and returning documents whose vectors are closest to the query

An embedding is a numerical vector that represents the *meaning* of a piece of text. Texts with similar meaning end up close together in vector space, even when they share no words. If the search embeds both the documents and the query and returns the nearest vectors, "time off for a new baby" matches "Parental leave policy". This is semantic search, and it's also the retrieval step in retrieval-augmented generation (RAG) (option C).

- A is wrong because temperature controls how random a model's *generated* text is. It has nothing to do with how well a search matches meaning. - B is wrong because maintaining synonyms by hand can patch individual cases, but it doesn't scale and misses phrasings nobody anticipated. It's still keyword matching. - D is wrong because splitting text into smaller tokens still depends on shared words. "Baby" and "parental" share none.

Reference: What are Embeddings in Machine Learning?

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