- A
Fixed‑size token chunking with no overlap
Why wrong: Fixed‑size chunks may cut sentences in half, losing semantic coherence and hurting retrieval relevance.
- B
Overlapping fixed‑size chunks with 50% overlap
Why wrong: Overlap can help avoid missing context, but chunks remain arbitrary and may still contain irrelevant portions.
- C
Semantic chunking that splits at paragraph or section boundaries
Semantic chunking preserves complete thoughts and contexts, making each chunk more self‑contained and relevant for retrieval.
- D
Very small chunks (50 tokens) to maximize granularity
Why wrong: Very small chunks often lack sufficient context for accurate semantic matching, reducing retrieval quality.
Question 37 of 1,000
hardMultiple ChoiceObjective-mapped
AIF-C01 Practice Question: Building a RAG application with Amazon Bedrock…
This AIF-C01 practice question tests your understanding of aif-c01 exam topics. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A company is building a RAG application with Amazon Bedrock Knowledge Bases. They want to ensure that the retriever returns the most semantically relevant chunks. They are using a large document corpus with many similar passages. Which chunking strategy is MOST likely to improve retrieval accuracy?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"most likely"Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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
✓
Semantic chunking that splits at paragraph or section boundaries
Semantic chunking splits documents at natural boundaries like paragraphs or sections, preserving the coherence of each chunk. This ensures that each chunk contains a complete, self-contained idea, which allows the retriever to match the semantic meaning of the query more accurately. In a corpus with many similar passages, this approach reduces noise and improves the relevance of retrieved chunks.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Fixed‑size token chunking with no overlap
Why it's wrong here
Fixed‑size chunks may cut sentences in half, losing semantic coherence and hurting retrieval relevance.
- ✗
Overlapping fixed‑size chunks with 50% overlap
Why it's wrong here
Overlap can help avoid missing context, but chunks remain arbitrary and may still contain irrelevant portions.
- ✓
Semantic chunking that splits at paragraph or section boundaries
Why this is correct
Semantic chunking preserves complete thoughts and contexts, making each chunk more self‑contained and relevant for retrieval.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Very small chunks (50 tokens) to maximize granularity
Why it's wrong here
Very small chunks often lack sufficient context for accurate semantic matching, reducing retrieval quality.
Common exam traps
Common exam trap: answer the scenario, not the keyword
A common misconception is that more granularity (smaller chunks) always improves retrieval accuracy, but the trap here is that overly small chunks lose context and semantic completeness, which actually degrades relevance in a RAG system.
Detailed technical explanation
How to think about this question
Semantic chunking leverages natural language processing to detect boundaries such as paragraph breaks, section headers, or sentence endings, often using embeddings to measure semantic similarity between sentences. This ensures each chunk is a self-contained unit of meaning, which is critical for retrieval-augmented generation (RAG) because the retriever's embedding model compares the query embedding to chunk embeddings; coherent chunks yield higher cosine similarity scores for relevant content. In practice, a corpus of legal contracts with similar clauses benefits from semantic chunking because it keeps entire clauses intact, avoiding the fragmentation that fixed-size methods cause.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this AIF-C01 question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Semantic chunking that splits at paragraph or section boundaries — Semantic chunking splits documents at natural boundaries like paragraphs or sections, preserving the coherence of each chunk. This ensures that each chunk contains a complete, self-contained idea, which allows the retriever to match the semantic meaning of the query more accurately. In a corpus with many similar passages, this approach reduces noise and improves the relevance of retrieved chunks.
What should I do if I get this AIF-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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.
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