Lesson 119 Quiz: Embeddings Explained Simply

Question 1

What is an embedding?

A database containing the complete original document A numerical representation of information A password used to access an AI model A user interface for an AI application

Question 2

Why are embeddings useful for semantic search?

It guarantees exact keyword matching It removes the need for document storage It allows meaning-based similarity comparison It converts every document into an image

Question 3

What is commonly stored together with an embedding to help manage retrieval results?

Only the vector dimension Only the original file name Metadata such as document identifiers and access information A users password

Question 4

What does a high similarity score between two embeddings mean?

The information is guaranteed to be correct The source document no longer matters The user is automatically authorized to see the information The representations are similar, but correctness still needs evaluation

Question 5

Which is an important security requirement when using embeddings for company documents?

Use similarity alone to decide access Remove all metadata from the vectors Enforce application-level authorization and access controls Allow every user to retrieve every document