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Quiz

Advanced Indexing​

Question 1: What characterizes 'Fixed-size Chunking'?

  • A. Cuts based on changes in content meaning.
  • B. Text is cut mechanically every 500 or 1000 characters, regardless of sentence completion.
  • C. Uses an LLM to rewrite the text.
  • D. Relies on BM25 for text division.

Answer: B

Question 2: What happens when search utilizes 'Flat Indexing'?

  • A. The system calculates the similarity of consecutive sentences.
  • B. The system has to compare the question with every single vector in a long list.
  • C. Data points are structured hierarchically.
  • D. The semantic flow of text is perfectly preserved.

Answer: B

Question 3: What does the abbreviation ANN stand for in vector searching?

  • A. Artificial Neural Network
  • B. Advanced Node Navigation
  • C. Approximate Nearest Neighbor
  • D. Algorithm for Numerical Nodes.

Answer: C

Question 4: What determines the split points in the Semantic Chunking method?

  • A. A fixed character limit.
  • B. Understanding the content to decide split points.
  • C. The number of punctuation marks.
  • D. Random allocation by the system.

Answer: B

Question 5: What is the first step in the Semantic Chunking implementation process?

  • A. Thresholding
  • B. Similarity Calculation
  • C. Generating hypothetical documents.
  • D. Sentence Splitting: Split the text into complete sentences based on punctuation.

Answer: D

Question 6: Why does 'Loss of Semantics' occur with mechanical chunking?

  • A. Because embedding models ignore large chunks.
  • B. Because mechanical chunking accidentally breaks the flow of text, splitting complete ideas into meaningless chunks.
  • C. Because it uses the HNSW algorithm.
  • D. Because it relies heavily on BM25.

Answer: B

Question 7: How does Semantic Chunking detect a change in topic between sentences?

  • A. By counting the characters.
  • B. When vectors abruptly change direction, creating a large distance.
  • C. By checking for new paragraph formatting.
  • D. By querying a Graph Database.

Answer: B

Question 8: What happens in Semantic Chunking if the similarity between a sentence and the next one is high?

  • A. The system generates a Cypher query.
  • B. The topic has changed, so a new chunk starts.
  • C. Two sentences are on the same topic and are merged into one chunk.
  • D. The algorithm performs a brute-force search.

Answer: C

Question 9: What is one of the main cons of Semantic Chunking compared to Recursive Chunking?

  • A. It easily cuts through important ideas.
  • B. It creates noisy context.
  • C. It consumes computational resources due to running a model to compare each sentence.
  • D. It is only suitable for strictly structured documents like laws.

Answer: C

Question 10: What is the structure of data in the HNSW algorithm?

  • A. A single long list.
  • B. A flat SQL table.
  • C. A multi-layered graph structure.
  • D. A recursive binary tree.

Answer: C

Question 11: In the HNSW graph, what does Layer 0 contain?

  • A. Only sparse shortcut links.
  • B. The most recently added data only.
  • C. All data points, and the most detailed links between them.
  • D. Only vectors that match exactly.

Answer: C

Question 12: What role do the 'higher layers' serve in an HNSW graph?

  • A. They contain the full detailed index.
  • B. They hold backup data for failure recovery.
  • C. They act as shortcuts helping the algorithm move quickly through the large data space.
  • D. They are used exclusively for BM25 processing.

Answer: C

Question 13: What does the parameter 'M' control in HNSW, and what is the trade-off of increasing it?

  • A. Controls search depth, increases speed but lowers accuracy.
  • B. Specifies the maximum number of links per node, increases accuracy but RAM consumption increases significantly.
  • C. Controls document chunk size, improves context but slows embedding.
  • D. Specifies the threshold for semantic chunking, diversity.

Answer: B

Question 14: How does the parameter 'ef_construction' affect index construction?

  • A. It sets the maximum token length for documents.
  • B. A larger value makes the algorithm scan more candidates to find optimal links, ensuring high structure quality but slower loading.
  • C. It limits the number of nodes at Layer 0 to save RAM.
  • D. It determines how heavily rare words are penalized.

Answer: B

Question 15: If you are configuring HNSW for a Real-time Chatbot Application, what is the recommended practical strategy?

  • A. Set M to 64 and ef_search to 500.
  • B. Disable HNSW and use brute-force.
  • C. Keep 'ef_search' at a low level (e.g., 50 - 100) to optimize latency, accepting a small margin of error.
  • D. Set ef_construction to 0 to bypass indexing.

Answer: C

Question 16: What describes the search process transition between layers in HNSW?

  • A. It scans all nodes on every layer sequentially.
  • B. It moves to the nearest neighbor until a local extremum is reached, which becomes the entry point for the next layer down.
  • C. It calculates the cosine similarity for all nodes on Layer 0 first.
  • D. It performs an exact BM25 keyword match at the highest layer.

Answer: B

Question 17: In the context of evaluating Chunking Methods, why is Recursive Chunking considered 'suitable for documents with clear structure'?

  • A. Because it relies heavily on LLM semantic analysis.
  • B. Because it cuts based on punctuation and a fixed number of characters, which aligns well with laws and contracts.
  • C. Because it is the only method that supports HNSW.
  • D. Because it automatically merges sentences on the same topic.

Answer: B

Question 18: What is the specific risk of decreasing the 'ef_search' parameter?

  • A. The RAM consumption will skyrocket.
  • B. The system will crash during index building.
  • C. The system responds extremely fast, but there is a risk of returning results not closest to the question.
  • D. It will trigger an endless loop in the top layer.

Answer: C

Question 19: Why did Semantic Chunking successfully keep all 'NVIDIA H100 GPU' information intact in the visual example, unlike Recursive Chunking?

  • A. Because the word count was exactly 500 characters.
  • B. Because it detected the semantic break point between H100 and Llama-3 and cut at the right time.
  • C. Because H100 was designated as a keyword.
  • D. Because it bypassed the punctuation rules entirely.

Answer: B

Question 20: What is an explicit consequence of a 'brute-force' approach when a system scales up many times over?

  • A. The vectors lose their semantic meaning completely.
  • B. Sequentially scanning through millions of vectors is too slow to meet real-time requirements.
  • C. The LLM hallucinates technical terms.
  • D. BM25 saturation limits are bypassed.

Answer: B