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Free LangChain course

LangChain for LLM Applications and RAG

Module 04 Knowledge Check

5 questions. Pass mark 4 out of 5. Answer every question before checking the answer key below, then retry after reading the feedback.

1. Splitting a document at a fixed character count risks…

2. PDF extraction commonly fails on…

3. Chunk metadata should record…

4. Changing your embedding model requires…

5. Chunk overlap exists mainly to…

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Answer Key and Explanations

Check these only after attempting every question.

1. B - Cutting mid-sentence or mid-table so the chunk loses its meaning

Structure-aware splitting (headings, paragraphs) preserves meaning far better than blind character counts.

2. B - Multi-column layouts, tables, and scanned pages

Always inspect extracted text before indexing; garbled tables silently poison retrieval.

3. B - Source, section, and identifiers needed for citation, filtering, and permissions

Without provenance you cannot cite, filter, or later delete the right content.

4. B - Re-embedding the entire corpus, because vectors are not comparable across models

Mixed-model vectors produce silently meaningless similarity scores.

5. B - Avoid losing an answer that straddles a split boundary

It trades storage for a lower chance of the key sentence being cut in half.

Practical Check

Apply this module to your own work: complete the module activity for *Document Loading, Splitting, and Embeddings*, then write one sentence naming what your result shows and one naming what it does not.

Strong Answer Pattern

A strong answer names the task, the evidence used, the check performed, and the remaining limitation. It avoids "proved", "guaranteed", or "always" unless the evidence genuinely supports it.