Guide-linked quiz · Medium Level
Retrieval Quality Quiz
Check retrieval relevance, recall and precision at a cutoff, ranking, current evidence, permissions, unanswerable queries, and missing support.
Question 1 of 8
What does recall@k measure for a retrieval system?
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Answers and explanations appear as you take the quiz.
- What does recall@k measure for a retrieval system?
- The rank position of the first relevant document returned
- The share of the top k results that are actually relevant
- The share of all relevant documents found in the top k
- The time taken for the system to return k results
- A retrieval evaluation cares whether the best evidence appears near the top. What kind of metric addresses that concern?
- A rank-aware metric
- A server uptime metric
- A count of stored files
- A tokenizer vocabulary size
- An exact order code is missed by semantic search, while a paraphrased question is missed by keyword search. What should the team inspect?
- Only the number of documents stored
- Whether every user asked the same question
- The appearance of the search button
- Each retrieval method’s blind spots on fixed examples
- A team adds hybrid retrieval and reranking. How should it judge whether the change helped?
- Compare only the highest similarity score after the change
- Evaluate both versions on the same fixed labeled examples
- Assume more retrieved passages always improve answers
- Count the number of model parameters rather than results
- A retrieved passage loses the sentence that qualifies its claim. What failure pattern should the reviewer flag?
- The source document was necessarily false
- The user must have asked an unanswerable question
- A passage was cut away from its qualification
- The search system returned too few languages
- In the guide’s constructed example, five passages are returned and three are relevant. What is precision at five?
- Three out of five, or 60%
- Three out of four, or 75%
- Five out of four, or 125%
- One out of five, or 20%
- Why should retrieval tests include access restrictions and unanswerable queries?
- To increase the average similarity score
- To check that the system avoids unauthorized evidence and unsupported answers
- To ensure every query returns a document
- To replace relevance labels
- A passage is about the right product but describes an older version. What does this show?
- The passage must rank first because it shares keywords
- The query has no retrieval requirements
- A high similarity score proves the answer
- Topical relevance alone may not provide evidence for the specific version or date in the question
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