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AIF-C01 Practice Question: A data scientist needs to convert text into…
A data scientist needs to convert text into numerical vectors for semantic search. Which type of foundation model should they use?
⚠ Common exam trap
AWS often tests the distinction between generative models (which produce new content) and representation models (which encode data into vectors), leading candidates to mistakenly choose a text generation model for tasks requiring vector embeddings.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Embedding model
An embedding model is specifically designed to convert text (or other data) into dense numerical vectors that capture semantic meaning. These vectors enable efficient similarity comparisons in vector space, which is the core requirement for semantic search. Text generation models produce sequences of tokens, not fixed-length vector representations, making them unsuitable for this task.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Embedding model
Why this is correct
Embedding models map text into dense numerical vectors positioned so semantically similar content sits close together in vector space. This directly satisfies the semantic search requirement, because the resulting vectors can be indexed and compared by similarity rather than generating or classifying text.
- ✗
Text generation model
Why it's wrong here
Text generation models output token sequences, not fixed-dimension embedding vectors, so they cannot produce the numerical representation semantic search requires. It is tempting because the same model family often supports both tasks, and generation would be correct for summarisation or completion.
- ✗
Multimodal model
Why it's wrong here
Multimodal models accept or emit images alongside text, adding capability irrelevant to producing text embeddings. It is tempting because multimodal models do contain text encoders, and would be the correct choice when searching across images and text jointly.
- ✗
Image generation model
Why it's wrong here
Image generation models synthesise pixels from prompts and expose no text-embedding output, so they cannot vectorise descriptions. It is tempting because diffusion models also use latent vector spaces internally, and image generation would be correct for creating product visuals from text.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AIF-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AIF-C01 exam.