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Quiz

Question 1: What type of query reveals a weakness in pure Vector Search?

  • A. Queries asking for summaries.
  • B. Queries requiring absolute accuracy in wording, like proper names or product codes.
  • C. Queries asking about complex sentiments.
  • D. Queries written in multiple languages.

Answer: B

Question 2: What does the BM25 algorithm specialize in finding?

  • A. Contextual synonyms.
  • B. Hypothetical vectors.
  • C. Precise keywords based on frequency statistics.
  • D. Nodes in a graph database.

Answer: C

Question 3: BM25 is considered a refined upgrade of which classical information retrieval algorithm?

  • A. HNSW
  • B. Cosine Similarity
  • C. TF-IDF
  • D. MMR.

Answer: C

Question 4: What are the two parallel search streams combined in Hybrid Search?

  • A. Cross-Encoder and Bi-Encoder
  • B. Semantic Chunking and Recursive Chunking
  • C. Sparse Retriever (BM25) and Dense Retriever (Vector Search)
  • D. Neo4j and ChromaDB.

Answer: C

Question 5: Instead of caring about numerical scores, what does the RRF algorithm rely on?

  • A. Rank
  • B. Keyword Density
  • C. Vector Length
  • D. Document Chunk Size.

Answer: A

Question 6: Why might Vector Search miss a query specifically looking for 'Error 503'?

  • A. It ignores all numbers.
  • B. In vector space, these numbers may not carry much specific semantic meaning, causing it to prioritize general 'error' synonyms.
  • C. It automatically rounds numbers to the nearest tenth.
  • D. It assumes 503 is a typographical error.

Answer: B

Question 7: How does the 'TF Saturation' mechanism in BM25 prevent keyword spamming?

  • A. It completely ignores keywords that appear more than once.
  • B. Unlike TF-IDF, after appearing a certain number of times, appearing again hardly adds more score, asymptoting to a limit.
  • C. It deletes the document from the index if spam is detected.
  • D. It divides the score by the total word count.

Answer: B

Question 8: What is the function of the IDF (Inverse Document Frequency) principle in BM25?

  • A. It counts the total documents in the database.
  • B. It ensures short paragraphs rank lower.
  • C. It heavily penalizes common words and greatly rewards rare words.
  • D. It increases the score infinitely for repeated terms.

Answer: C

Question 9: How does 'Length Normalization' make BM25 smarter?

  • A. By padding short documents with empty vectors.
  • B. By rating a keyword occurrence in a short paragraph higher than in a long novel where information is diluted.
  • C. By only accepting documents of a fixed length.
  • D. By translating long novels into summaries.

Answer: B

Question 10: What is the primary problem encountered when trying to fuse results from Vector Search and BM25 directly?

  • A. They return results in different languages.
  • B. The scoring scales are completely different (cosine similarity vs statistical formula) and cannot be directly added.
  • C. Vector Search is too slow to wait for BM25.
  • D. BM25 cannot run in parallel.

Answer: B

Question 11: What does the 'Fusion' step accomplish in the Hybrid Search Implementation Process?

  • A. It translates the documents.
  • B. It merges the two lists (from BM25 and Vector Search) into a single list.
  • C. It concatenates text using a Cross-Encoder.
  • D. It expands the query using an LLM.

Answer: B

Question 12: What is listed as a major 'Con' of implementing Hybrid Search?

  • A. It fails if the user uses synonyms.
  • B. It is more complex to deploy and consumes more resources due to running 2 parallel streams.
  • C. It loses the ability to match exact keywords.
  • D. It cannot handle multi-lingual queries.

Answer: B

Question 13: In the RRF formula 1 / (k + rank_r(d)), what is the typical value chosen for the smoothing constant k?

  • A. 1
  • B. 10
  • C. 60
  • D. 100

Answer: C

Question 14: What specific role does the smoothing constant k play in the Reciprocal Rank Fusion formula?

  • A. It converts cosine similarity to a percentage.
  • B. It acts as a hard limit on the number of retrieved documents.
  • C. It helps reduce score disparity between very high ranks (e.g., Top 1 vs Top 2), ensuring fairness.
  • D. It prevents BM25 from calculating saturation.

Answer: C

Question 15: In the RRF illustrative example, why does Doc B (Rank 2 Vector, Rank 3 BM25) beat Doc A (Rank 1 Vector, Rank 10 BM25)?

  • A. Because BM25 scores are weighted double by default.
  • B. Because RRF prioritizes documents that have high consensus from both algorithms, yielding a higher combined sum (0.0320 > 0.0307).
  • C. Because Doc A triggered the TF saturation penalty.
  • D. Because Doc B was shorter, gaining a Length Normalization bonus.

Answer: B

Question 16: What is the fundamental assumption that makes Reciprocal Rank Fusion (RRF) effective?

  • A. That cosine similarity is always more accurate than statistical frequency.
  • B. That if a document appears at a high rank in both lists, it is certainly an important document.
  • C. That keywords in the title are more important than in the body.
  • D. That sparse retrievers will eventually replace dense retrievers.

Answer: B

Question 17: If a user searches for 'symptoms of meningitis', how does BM25 handle the word 'of' compared to 'meningitis'?

  • A. It treats them equally.
  • B. It drops 'meningitis' due to complexity.
  • C. Through IDF, it heavily penalizes 'of' as a common word and greatly rewards 'meningitis' as a rare word.
  • D. It triggers length normalization based on the word 'of'.

Answer: C

Question 18: In the 'Galaxy' keyword spam example, why does TF-IDF fail compared to BM25?

  • A. TF-IDF cannot process the word 'Galaxy'.
  • B. TF-IDF scores increase linearly infinitely with repetition, giving Doc A an overwhelming win, while BM25 saturation stops the score from increasing further.
  • C. TF-IDF strictly relies on document length normalization.
  • D. BM25 uses vector similarity to realize it is spam.

Answer: B

Question 19: Which Search method is described as 'Poor at matching exact keywords, hard to explain results'?

  • A. BM25
  • B. Graph Traversal
  • C. Vector Search
  • D. Hybrid Search.

Answer: C

Question 20: Which Search method 'Does not understand context, fails if user uses different synonyms than the text'?

  • A. Vector Search
  • B. Hybrid Search
  • C. MMR
  • D. BM25.

Answer: D