Exam AIP-C01 Topic 1 Question 69 Discussion
Actual exam question for Amazon's AIP-C01 exam
Question #: 69
Topic #: 1
Question #: 69
Topic #: 1
A legal research company has a Retrieval Augmented Generation (RAG) application that uses Amazon Bedrock and Amazon OpenSearch Service. The application stores 768-dimensional vector embeddings for 15 million legal documents, including statutes, court rulings, and case summaries.
The company's current chunking strategy segments text into fixed-length blocks of 500 tokens. The current chunking strategy often splits contextually linked information such as legal arguments, court opinions, or statute references across separate chunks. Researchers report that generated outputs frequently omit key context or cite outdated legal information.
Recent application logs show a 40% increase in response times. The p95 latency metric exceeds 2 seconds.
The company expects storage needs for the application to grow from 90 GB to 360 GB within a year.
The company needs a solution to improve retrieval relevance and system performance at scale.
Which solution will meet these requirements?
The company's current chunking strategy segments text into fixed-length blocks of 500 tokens. The current chunking strategy often splits contextually linked information such as legal arguments, court opinions, or statute references across separate chunks. Researchers report that generated outputs frequently omit key context or cite outdated legal information.
Recent application logs show a 40% increase in response times. The p95 latency metric exceeds 2 seconds.
The company expects storage needs for the application to grow from 90 GB to 360 GB within a year.
The company needs a solution to improve retrieval relevance and system performance at scale.
Which solution will meet these requirements?
Suggested Answer: C Vote an answer
Option C directly addresses both retrieval relevance and performance scalability. Fixed token chunking breaks semantic continuity in legal texts, causing incomplete context retrieval and degraded response quality. By switching to semantic chunking-based on legal arguments, clauses, or sections-the application preserves contextual integrity, improving retrieval accuracy and reducing hallucinations.
Regenerating embeddings aligned with the new chunk structure also improves vector search efficiency, reducing unnecessary comparisons and helping control latency as the dataset scales.
Option A increases cost and latency without fixing the core issue. Option B removes dynamic reasoning, which defeats the purpose of a legal RAG system. Option D discards vector semantics entirely and is unsuitable for nuanced legal research. Therefore, Option C is the correct and scalable solution.
Regenerating embeddings aligned with the new chunk structure also improves vector search efficiency, reducing unnecessary comparisons and helping control latency as the dataset scales.
Option A increases cost and latency without fixing the core issue. Option B removes dynamic reasoning, which defeats the purpose of a legal RAG system. Option D discards vector semantics entirely and is unsuitable for nuanced legal research. Therefore, Option C is the correct and scalable solution.
by Chapman at Feb 06, 2026, 08:29 AM
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