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MLA-C02 practice question: Choosing ML models and algorithms

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.

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A solar installer operates 2,000 residential systems across three states. For each site, it needs a daily forecast of energy output for the next 30 days, and it has three years of daily production history per site with strong seasonal patterns.

Many sites share similar weather and seasonal behavior, so the team wants one model that learns from all of them together. The forecasts must come with uncertainty ranges, and the team wants a built-in algorithm in SageMaker AI.

Which algorithm should the ML engineer choose?

Choose one answer.

  1. A. Linear Learner, trained on one row per site with the average output as its target.
  2. B. DeepAR, trained on all site time series together, which produces probabilistic forecasts for each series.
  3. C. K-means, to cluster the sites and then forecast each cluster with its average output.
  4. D. Random Cut Forest, trained on all site histories, to flag days whose output is unusual and use the flagged days as the forecast.
Show the answer and explanation

Answer: B

  • A. Wrong: Linear Learner treats rows independently and would average away the daily pattern and seasonality the forecast needs. It does not produce a forecast for each day with uncertainty ranges.
  • B. Right: Correct. DeepAR learns from many related time series at once and outputs probabilistic forecasts with prediction intervals. It handles seasonality across sites, which matches the requirement. Learn more: Use the SageMaker AI DeepAR forecasting algorithm (opens in a new tab)
  • C. Wrong: K-means finds groups but does not produce forecasts over time. Averaging clusters would also hide the daily pattern for each site.
  • D. Wrong: Random Cut Forest is an anomaly detector and does not produce future values. Flagging unusual days does not give the 30-day forecast the installer needs.

Explanation

DeepAR is a built-in algorithm for forecasting many related time series, and it outputs probabilistic forecasts that include intervals. Linear Learner loses the time structure, k-means does not forecast, and Random Cut Forest detects anomalies rather than predicting values.

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