Google Professional Machine Learning Engineer Question 80
Select 3Google Cloud PlatformYou are a machine learning engineer at a retail company that wants to predict future product demand for inventory management. You decide to use AutoML for forecasting. During the model creation process, which of the following steps are required to ensure the AutoML model generates accurate forecasts?
- A
Provide a dataset with a clear time column and target variable.
- B
Ensure the dataset includes at least one categorical feature for encoding.
- C
Split the dataset into training, validation, and test sets automatically using AutoML's built-in functionality.
- D
Enable the Explainability feature in AutoML to understand feature importance.
- E
Include historical data for the target variable and relevant features over a sufficient time range.
Show answer and explanation
Correct answers: A, C, E
Explanation
To create a forecasting model using AutoML, you must provide a properly formatted dataset that includes a time column, target variable, and relevant historical data for both the target and features. AutoML handles data splitting automatically to ensure proper evaluation. While categorical features and explainability can enhance the model's utility, they are not mandatory for forecasting accuracy.
- A. Correct.
Providing a dataset with a clear time column and target variable is critical for time-series forecasting, as AutoML requires this structure to understand the temporal patterns.
- B. Incorrect.
While categorical features can be helpful, they are not mandatory for creating a forecasting model in AutoML. The absence of categorical features does not prevent the model from functioning.
- C. Correct.
AutoML automatically splits data into training, validation, and test sets during the model training process. This step is essential to ensure proper evaluation and generalization.
- D. Incorrect.
Enabling the Explainability feature is optional and does not directly impact the model's ability to generate accurate forecasts. It is primarily used to interpret the model's predictions.
- E. Correct.
Including historical data for the target variable and relevant features over a sufficient time range is crucial for capturing temporal trends and seasonality, which are essential for accurate forecasting.