Session 4: Advanced Query Understanding - Test Solutions¶
📝 Multiple Choice Test¶
Question 1: Intent Classification Benefits¶
What is the primary benefit of intent classification in query processing?
A) Reduces processing time
B) Enables specialized processing strategies tailored to query types ✅
C) Reduces memory usage
D) Simplifies implementation
Explanation: Intent classification allows RAG systems to route different query types to appropriate processing pipelines. Factual queries may need different retrieval strategies than procedural or analytical queries. This specialization improves both relevance and efficiency by matching processing approach to user intent.
Question 2: Context-Aware Query Enhancement¶
Why is context-aware query enhancement important?
A) It reduces computational costs
B) It resolves ambiguities and adds implicit context from conversation history ✅
C) It speeds up retrieval
D) It reduces storage requirements
Explanation: Context-aware enhancement addresses the ambiguity inherent in conversational interactions. By resolving pronouns, maintaining entity references, and incorporating conversation history, it transforms incomplete or ambiguous queries into clear, actionable search requests that retrieve more relevant information.
Question 3: Multi-Modal Query Processing¶
How does multi-modal query processing improve RAG systems?
A) It reduces complexity
B) It enables processing of queries with images, documents, and other media types ✅
C) It reduces memory usage
D) It simplifies deployment
Explanation: Multi-modal processing extends RAG capabilities beyond text-only queries. By analyzing images with vision models and processing document attachments, the system can understand queries that reference visual content, enabling richer interactions and more comprehensive knowledge retrieval.
Question 4: Reference Resolution Value¶
What is the value of reference resolution in conversational RAG?
A) It improves speed
B) It resolves pronouns and references using conversation context for clarity ✅
C) It reduces costs
D) It simplifies architecture
Explanation: Reference resolution is essential for conversational RAG systems where users frequently use pronouns and implicit references. Converting "How does it work?" to "How does the HNSW algorithm work?" based on conversation context ensures accurate retrieval and maintains conversational flow.
Question 5: Query Complexity Assessment¶
Why should query complexity assessment guide processing strategy?
A) It reduces infrastructure costs
B) It allows allocation of appropriate computational resources and techniques ✅
C) It speeds up all queries
D) It reduces memory usage
Explanation: Query complexity assessment enables dynamic resource allocation and technique selection. Simple queries can use lightweight processing, while complex analytical queries may require more sophisticated reasoning, multiple retrieval rounds, or specialized processing pipelines to generate accurate responses.