quiz.json

{
  "lesson": "50-hypothesis-generator",
  "title": "Hypothesis Generator",
  "questions": [
    {
      "stage": "pre",
      "question": "Why does the generator produce a ranked queue instead of a single hypothesis?",
      "options": [
        "Because the parser cannot read a single block",
        "Because the runner needs depth so it can pop the next hypothesis when the first one fails",
        "Because the mock model only emits lists",
        "Because the embedding requires more than one input"
      ],
      "correct": 1,
      "explanation": "The point of generating a queue is to amortise sampling cost across the loop. When the first hypothesis fails the runner pops the next without a fresh sampling pass."
    },
    {
      "stage": "pre",
      "question": "What does the temperature ramp accomplish on each pass?",
      "options": [
        "It raises the parser tolerance",
        "It widens the sampling distribution so later drafts can land further from the seed",
        "It increases the embedding dimension",
        "It triples the seed value"
      ],
      "correct": 1,
      "explanation": "Higher temperature widens the sampling distribution. The ramp encourages each pass to drift further so the novelty filter has something to do."
    },
    {
      "stage": "check",
      "question": "When does the novelty filter reject a draft?",
      "options": [
        "When its rank score is below the threshold",
        "When its minimum cosine distance to any prior survivor falls below the novelty threshold",
        "When its parser passes but its tag count is wrong",
        "When its draft pass is greater than the queue length"
      ],
      "correct": 1,
      "explanation": "Novelty is the minimum distance to prior survivors. If that distance is below the threshold the draft is a near duplicate and is dropped."
    },
    {
      "stage": "check",
      "question": "Which three components combine in the rank score?",
      "options": [
        "Latency, throughput, cost",
        "Novelty, specificity, testability",
        "Temperature, seed, pass index",
        "Variables, metric, baseline length"
      ],
      "correct": 1,
      "explanation": "The rank score is a weighted sum of novelty, specificity, and testability. Each sub score lives between zero and one."
    },
    {
      "stage": "check",
      "question": "Why is the mock language model keyed on a temperature bucket rather than the raw float?",
      "options": [
        "Because floats cannot be hashed",
        "Because buckets make the schedule discrete so a small temperature change can pick a different scripted draft",
        "Because the parser needs an integer",
        "Because the embedding requires it"
      ],
      "correct": 1,
      "explanation": "Buckets discretise the continuous schedule. Two adjacent temperatures can map to different buckets and pull different drafts from the scripted bank, which is how the mock simulates varied sampling."
    },
    {
      "stage": "check",
      "question": "What happens if every draft from the mock model fails the parser?",
      "options": [
        "The generator raises a hard error",
        "The queue is empty and each pass logs a parse rejection so the failure mode is auditable",
        "The runner retries with a fresh prompt",
        "The novelty threshold is lowered automatically"
      ],
      "correct": 1,
      "explanation": "Parser failures are recorded as logs with a parse reject reason. The queue can come back empty without crashing the loop, and the logs explain why."
    }
  ]
}