400 Python LlamaIndex Interview Questions with Answers 2026
Course Description
Master LlamaIndex for AI Engineering & RAG Interviews
Python LlamaIndex Interview Practice Questions are meticulously designed to bridge the gap between basic LLM tutorials and production-grade RAG engineering. This comprehensive question bank prepares you for technical interviews and real-world implementation by diving deep into data ingestion via LlamaHub, sophisticated indexing strategies like Auto-Merging Retrievers, and the nuances of Agentic RAG architectures. Whether you are navigating complex response synthesis modes or optimizing evaluation frameworks with Arize Phoenix, these practice exams provide the rigorous, scenario-based testing needed to validate your expertise in building autonomous, data-driven AI systems.
Exam Domains & Sample Topics
Data Ingestion & Transformation: LlamaHub connectors, custom metadata extraction, and transformation pipelines.
Advanced Retrieval: Small-to-Big retrieval, Sentence Windowing, and Index structures (Tree, Keyword, Summary).
Post-Processing & Synthesis: Reranking strategies (Cohere/BGE) and Response Synthesis (Refine vs. Compact).
Agentic RAG: Tool abstractions, ReAct agents, and Sub-Question Query Engines.
Production & Evaluation: Faithfulness/Relevancy metrics, observability, and PII masking.
Sample Practice Questions
1. When implementing a "Sentence Window Retrieval" strategy to improve context quality, which component is primarily responsible for expanding the retrieved node to its surrounding sentences?
A. Metadata Replacement Post-processor B. VectorStoreIndex C. SummaryIndex D. TreeSummarize Response Mode E. KeywordTableIndex F. ReAct Agent
Correct Answer: A
Overall Explanation: Sentence Window Retrieval stores small chunks (sentences) for precise embedding search but replaces them with a wider "window" of context during retrieval to provide the LLM with better surrounding information.
Option A (Correct): The MetadataReplacementPostprocessor is used specifically to swap the small retrieved text with the larger window stored in the metadata.
Option B (Incorrect): VectorStoreIndex stores the embeddings but does not handle the logic of window expansion.
Option C (Incorrect): SummaryIndex is used for retrieving all nodes or summarizing them, not for window-based granular retrieval.
Option D (Incorrect): TreeSummarize is a synthesis mode for final answers, not a retrieval post-processor.
Option E (Incorrect): KeywordTableIndex retrieves nodes based on keyword matches, not windowed context.
Option F (Incorrect): A ReAct Agent handles reasoning loops and tool use, not the low-level retrieval mechanics.
2. You are building a RAG system that must handle complex queries by breaking them down into several sub-queries across different data sources. Which LlamaIndex tool is best suited for this?
A. ListIndex B. SimpleDirectoryReader C. SubQuestionQueryEngine D. PropertyGraphIndex E. StorageContext F. ServiceContext (Deprecated)
Correct Answer: C
Overall Explanation: Complex queries often require data from multiple indexes or parts of a document; query decomposition allows the system to answer pieces of the prompt individually before synthesizing a final response.
Option A (Incorrect): ListIndex is a simple way to iterate through nodes; it doesn't decompose complex questions.
Option B (Incorrect): SimpleDirectoryReader is for data ingestion, not query processing.
Option C (Correct): SubQuestionQueryEngine is designed specifically to break a complex query into sub-questions against multiple sub-engines.
Option D (Incorrect): PropertyGraphIndex focuses on knowledge graph relationships, not necessarily query decomposition.
Option E (Incorrect): StorageContext manages where the data is stored (disk, DB), not how the query is executed.
Option F (Incorrect): ServiceContext was an older configuration object, now largely replaced by Settings, and never handled query decomposition.
3. In LlamaIndex, which Response Synthesis mode is most efficient for saving LLM tokens when you have many retrieved nodes but need a single, concise summary?
A. Refine B. Tree Summarize C. Compact D. Generation E. No_Text F. Accumulate
Correct Answer: C
Overall Explanation: Response synthesis modes determine how the retrieved text is packed into the LLM prompt. Efficiency is key to managing both cost and latency.
Option A (Incorrect): Refine goes through nodes sequentially, which can be token-heavy and slow for many nodes.
Option B (Incorrect): Tree Summarize builds a tree of summaries; while powerful, it may involve more LLM calls than Compact.
Option C (Correct): Compact stuffs as many chunks as possible into a single prompt before moving to the next, reducing the total number of LLM calls compared to Refine.
Option D (Incorrect): Generation isn't a standard synthesis mode; the system usually uses "Compact And Refine".
Option E (Incorrect): No_Text only retrieves the nodes and does not generate a response at all.
Option F (Incorrect): Accumulate applies the prompt to each node separately and returns a list of results, which is the opposite of a "single concise summary."
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