Session 2: Advanced Document Analytics - Test Solutions¶
📝 Multiple Choice Test¶
Question 1: Document Complexity Scoring¶
What is the primary benefit of document complexity scoring in RAG systems?
A) It reduces processing time for all documents
B) Enables optimal processing strategy selection based on document characteristics ✅
C) It eliminates the need for human document review
D) It automatically fixes document formatting issues
Explanation: Document complexity scoring analyzes structural, semantic, and processing difficulty aspects to recommend the most appropriate chunking and processing strategies. This ensures that simple documents use efficient processing while complex documents receive the specialized handling they require.
Question 2: Chunk Quality Assessment¶
What represents the most comprehensive approach to chunk quality assessment?
A) Balance of coherence, information density, and completeness ✅
B) Semantic similarity to source document
C) Reading level and vocabulary complexity
D) Word count and character length only
Explanation: Effective chunk quality assessment requires evaluating multiple dimensions simultaneously. Coherence ensures adjacent chunks maintain topical relationships, information density measures content value, and completeness ensures chunks contain sufficient context for understanding.
Question 3: Domain-Specific Processing¶
Why is domain-specific document processing important for RAG systems?
A) It preserves domain-specific structure and terminology for better retrieval ✅
B) It reduces computational requirements
C) It standardizes all documents to a common format
D) It eliminates the need for manual document preparation
Explanation: Domain-specific processing recognizes that legal documents, medical records, and technical documentation have unique structures, terminology, and relationships. Preserving these domain-specific elements significantly improves retrieval accuracy and maintains critical contextual information.
Question 4: Information Density Measurement¶
How is information density typically measured in document chunks?
A) Total word count divided by paragraph count
B) Number of sentences per chunk
C) Ratio of unique words to total words ✅
D) Average word length in the chunk
Explanation: Information density is calculated as the ratio of unique words to total words in a chunk. This metric helps identify chunks that are either too sparse (low unique word ratio, indicating repetitive content) or appropriately dense with distinct information.
Question 5: Coherence Measurement¶
What is the best method for measuring coherence between document chunks?
A) By document structure and formatting
B) By word overlap and shared vocabulary
C) By reading level and complexity scores
D) By semantic similarity and topic consistency ✅
Explanation: Coherence between chunks should be measured using semantic similarity algorithms that evaluate topic consistency and conceptual relationships. This ensures that adjacent chunks maintain logical flow and contextual continuity for both retrieval systems and end users.