MLA-C02 practice question: Hyperparameter tuning (1 of 2)
ML Model and Foundation Model (FM) Development A free question from our original MLA-C02 practice exam. Try it, then open the answer to see why each option is right or wrong.
A regional bank tuned an XGBoost credit model last quarter with an automatic model tuning job. It has since received three months of new loan applications, and the team wants to re-tune the same algorithm, starting from the best settings it already found.
The team wants the new tuning job to reuse what the earlier job learned, even though the training data is now different. Its parent job used the same XGBoost algorithm and image version.
Which warm start configuration should the team use?
Choose one answer.
AA. Use TRANSFER_LEARNING, because it allows the input data to change while the new job still uses the parent job's results as a starting point.
BB. Use IDENTICAL_DATA_AND_ALGORITHM, because the new job reuses the parent job's results.
CC. Start a new tuning job without warm start, because warm start is allowed only when the input data stays exactly the same as in the parent tuning job.
DD. Skip tuning, and run one training job on the new data with the parent job's best hyperparameters set as fixed values.
Show the answer and explanation
Answer: A
A. Right: Correct. TRANSFER_LEARNING lets the new tuning job use different input data, and even a different version of the training image, while starting from the parent job's results. The total number of static and tunable hyperparameters must still match the parent. Learn more: Run a warm start hyperparameter tuning job (opens in a new tab)
B. Wrong: This type requires the same input data and training image as the parent. The bank now has new data, so the warm start would not be allowed.
C. Wrong: This discards what the earlier job learned, which the team wants to reuse. Warm start with transfer learning allows the data to change, so the option misreads the rule.
D. Wrong: This reuses the earlier best values but performs no search, so it cannot adjust them to the new data. The team wants to re-tune the model, starting from what the parent job learned.
Explanation
Warm start with TRANSFER_LEARNING lets a new tuning job use new input data while starting from the parent job's results. IDENTICAL_DATA_AND_ALGORITHM requires the same input data and training image, which the bank no longer has. Running a single job with fixed values performs no tuning at all.
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