Quiz with answers — The complete pre-training pipeline
Answer all eight questions, then check the score. Open only the explanations needed for remediation.
1. Solve the worked-case variant: Append a sixth token to the five-token sequence. Trace input→target pairs before and after, then state what changes in the batch without claiming that this one example improves the model.
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B. Five tokens produce four next-token pairs; six tokens produce five. The batch gains one supervised position and one computation step, but quality still depends on filtering, diversity, and many updates. The correct answer executes the requested change and gives a checkable result; the other texts do not close this calculation or trace.
2. Which causal order correctly connects the first three stages of “The complete pre-training pipeline”?
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C. Collect without accepting everything → Tokenize → Build inputs and targets The chain follows the taught progression; reversing stages consumes a representation or state before it is produced.
3. If “Build inputs and targets” is removed, which diagnostic method is defensible?
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A. Keep the same input, predict the first output that depends on “Build inputs and targets,” then compare the before/after trace. One intervention and a prior prediction make the delta attributable to the removed mechanism.
4. Which verdict respects this session’s validity boundary?
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D. A teaching pipeline omits distributed storage, security, data policies, and many production quality controls. The correct answer bounds the conclusion; the others turn a local relation into a global guarantee.
5. Which evidence best matches the stated status of “The complete pre-training pipeline”?
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B. Established mechanisms; numerical simplifications are pedagogical. Product or mechanism evidence must remain attributed and measured; availability and completion do not prove value.
6. When should a simpler baseline be preferred to “Loss and update”?
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C. When a controlled test shows equivalent quality with lower memory, latency, or complexity. The choice depends on a measured trade-off on the real workload, not novelty or one isolated metric.
7. A learner gets the right result but cannot explain “Tokenize.” Which remediation is most useful?
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A. Rebuild the first missing transformation, label its inputs and outputs, then test a neighboring case. The remediation targets the first causal break and then requires transfer instead of rewarding a guessed result.
8. Which submission actually demonstrates the outcome “Separate pre-training, post-training, and evaluation.”?
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D. A trace with starting data, transformations, observed result, boundary, and next experiment. The correct submission makes the reasoning reproducible and the verdict revisable by future measurement.