AIP-C01 practice question: Choosing and generating embeddings (Titan, Cohere, Nova multimodal)
Foundation Model Integration, Data Management, and Compliance A free question from our original AIP-C01 practice exam. Try it, then open the answer to see why each option is right or wrong.
A travel marketplace indexes 60 million short text passages in English, Spanish, Portuguese and French for a search feature. Storage and memory for the vector index are now the largest part of the search bill.
The team can accept a small loss in retrieval quality, but the search must still return the right passages for at least 98 percent of the queries in its own benchmark set, which it can run before any change goes live.
Reindexing the whole corpus is acceptable once, during a planned maintenance window.
Which approach meets these requirements at the LOWEST index cost?
Choose one answer.
AA. Keep the English-only Cohere embedding model at 1024 dimensions for all four languages, because its high dimension count gives the strongest recall across the corpus.
BB. Use Amazon Titan Text Embeddings V2 at 1024 dimensions, the largest available size, because larger vectors give better recall at the same cost per query.
CC. Switch to Titan Text Embeddings V2 at 256 dimensions without running any benchmark, because smaller vectors cost less to store and search, and accept whatever recall results.
DD. Use Titan Text Embeddings V2 at 512 dimensions, after benchmarking it against the 1024-dimension baseline on the team's own queries, and reindex the corpus once.
Show the answer and explanation
Answer: D
A. Wrong: The English-only model is weak on Spanish, Portuguese and French passages, so recall would fall for most of the corpus. The high dimension count also keeps the index as large as the cost requirement forbids.
B. Wrong: Larger vectors raise the storage and memory that drive the bill, so the cost of the index grows with the dimension count. The claim that the cost stays the same is wrong, so this option misses the cost requirement.
C. Wrong: The smallest size does reduce index cost, but skipping the benchmark means the 98 percent recall target cannot be shown. The team needs measured quality before the change goes live.
D. Right: Halving the dimensions roughly halves the vector storage while the benchmark confirms whether recall stays at the target. Titan V2 is multilingual, and the single planned reindex matches the stated maintenance window. Learn more: Amazon Titan Text Embeddings models (opens in a new tab)
Explanation
Titan Text Embeddings V2 is multilingual and offers smaller dimension sizes, so choosing 512 dimensions cuts index cost while a benchmark confirms the recall target. The English-only model, the largest size and an unmeasured smallest size each fail a stated requirement.
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