hardMultiple ChoiceObjective-mapped
AIF-C01 Practice Question: A data scientist is building a RAG application…
A data scientist is building a RAG application using Amazon Bedrock Knowledge Bases. They want to ensure that only the most semantically relevant documents are retrieved for each query. Which embedding model characteristic is MOST important for this requirement?
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
✓
High embedding dimension
Semantic relevance depends on the quality of embeddings. High-dimensional embeddings (e.g., 1024-dim) can capture finer semantic nuances compared to lower dimensions, leading to better retrieval accuracy. Training data diversity, inference latency, and context window length are secondary factors.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Large training dataset size
Why it's wrong here
A large dataset is beneficial but does not guarantee better semantic understanding than high dimensionality.
- ✓
High embedding dimension
Why this is correct
Correct. Higher dimensions can represent more nuanced semantics, improving relevance.
- ✗
Long context window
Why it's wrong here
Context window limits the input length for the LLM, not the embedding quality.
- ✗
Low inference latency
Why it's wrong here
Latency affects speed, not semantic relevance.
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