MLS-C01 · Question #129
A data scientist wants to use Amazon Forecast to build a forecasting model for inventory demand for a retail company. The company has provided a dataset of historic inventory demand for its products…
The correct answer is A. Use ETL jobs in AWS Glue to separate the dataset into a target time series dataset and an item. Target and metadata must be in two files and loaded from S3. https://docs.aws.amazon.com/forecast/latest/dg/dataset-import-guidelines-troubleshooting.html
Question
A data scientist wants to use Amazon Forecast to build a forecasting model for inventory demand for a retail company. The company has provided a dataset of historic inventory demand for its products as a .csv file stored in an Amazon S3 bucket. The table below shows a sample of the dataset. How should the data scientist transform the data?
Exhibit
Options
- AUse ETL jobs in AWS Glue to separate the dataset into a target time series dataset and an item
- BUse a Jupyter notebook in Amazon SageMaker to separate the dataset into a related time series
- CUse AWS Batch jobs to separate the dataset into a target time series dataset, a related time
- DUse a Jupyter notebook in Amazon SageMaker to transform the data into the optimized protobuf
How the community answered
(51 responses)- A82% (42)
- B2% (1)
- C6% (3)
- D10% (5)
Explanation
Target and metadata must be in two files and loaded from S3. https://docs.aws.amazon.com/forecast/latest/dg/dataset-import-guidelines-troubleshooting.html
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