AIF-C01 Question 93

Community Discussion

Written by other learners. Comments can disagree with the marked answer — check the official documentation when in doubt.

• Chose C1

  C is the correct answer

• Chose C3

  Explanation: The DeepAR forecasting algorithm in Amazon SageMaker is specifically designed for time series forecasting tasks. Time series data consists of observations collected over time, often at regular intervals (e.g., daily, weekly, or monthly). This data is typically used to forecast future values based on historical trends and patterns. In the context of predicting the demand for a product, time series data would include past sales figures, inventory levels, and other relevant metrics over a period of time. The DeepAR algorithm can analyze this historical data and generate forecasts for future demand.

• Chose C2

  DeepAR and Time Series Data: The Amazon SageMaker DeepAR forecasting algorithm is specifically designed to handle time series data for forecasting tasks. Time series data consists of observations collected at regular intervals over time (e.g., daily sales of a product). DeepAR uses historical patterns in this data to predict future values. Why Time Series Data is Required: To predict product demand, the model needs past sales data (e.g., daily, weekly, or monthly), which is inherently time series data.