MLS-C01 · Question #332
A company wants to forecast the daily price of newly launched products based on 3 years of data for older product prices, sales, and rebates. The time-series data has irregular timestamps and is missi
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Question
A company wants to forecast the daily price of newly launched products based on 3 years of data for older product prices, sales, and rebates. The time-series data has irregular timestamps and is missing some values. Data scientist must build a dataset to replace the missing values. The data scientist needs a solution that resamples the data daily and exports the data for further modeling. Which solution will meet these requirements with the LEAST implementation effort?
Options
- AUse Amazon EMR Serverless with PySpark.
- BUse AWS Glue DataBrew.
- CUse Amazon SageMaker Studio Data Wrangler.
- DUse Amazon SageMaker Studio Notebook with Pandas.
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