MLS-C01 Question #128: Real Exam Question with Answer & Explanation

Question

A logistics company needs a forecast model to predict next month's inventory requirements for a single item in 10 warehouses. A machine learning specialist uses Amazon Forecast to develop a forecast model from 3 years of monthly data. There is no missing data. The specialist selects the DeepAR+ algorithm to train a predictor. The predictor means absolute percentage error (MAPE) is much larger than the MAPE produced by the current human forecasters. Which changes to the CreatePredictor API call could improve the MAPE? (Choose two.)

Options

• ASet PerformAutoML to true.
• BSet ForecastHorizon to 4.
• CSet ForecastFrequency to W for weekly.
• DSet PerformHPO to true.
• ESet FeaturizationMethodName to filling.

Explanation

By default, Amazon Forecast uses the 0.1 (P10), 0.5 (P50), and 0.9 (P90) quantiles for hyperparameter tuning during hyperparameter optimization (HPO) and for model selection during AutoML. If you specify custom forecast types when creating a predictor, Forecast uses those forecast types during HPO and AutoML. If custom forecast types are specified, Forecast evaluates metrics at those specified forecast types, and takes the averages of those metrics to determine the optimal outcomes during HPO For both AutoML and HPO, Forecast chooses the option that minimizes the average losses over the forecast types. During HPO, Forecast uses the first backtest window to find the optimal hyperparameter values. During AutoML, Forecast uses the averages across all backtest windows and the optimal hyperparameters values from HPO to find the optimal algorithm.

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