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MLS-C01 · Question #276

A data scientist is working on a forecast problem by using a dataset that consists of .csv files that are stored in Amazon S3. The files contain a timestamp variable in the following format: March…

The correct answer is C. Create a new flow in Amazon SageMaker Data Wrangler. Import the S3 file, use the Featurize. https://aws.amazon.com/blogs/machine-learning/prepare-time-series-data-with-amazon- sagemaker-data-wrangler/ "Featurize datetime time series transformation to add the month, day of the month, day of the year, week of the year, and quarter features to our dataset."

Machine Learning Implementation and Operations

Question

A data scientist is working on a forecast problem by using a dataset that consists of .csv files that are stored in Amazon S3. The files contain a timestamp variable in the following format:

March 1st, 2020, 08:14pm There is a hypothesis about seasonal differences in the dependent variable. This number could be higher or lower for weekdays because some days and hours present varying values, so the day of the week, month, or hour could be an important factor. As a result, the data scientist needs to transform the timestamp into weekdays, month, and day as three separate variables to conduct an analysis. Which solution requires the LEAST operational overhead to create a new dataset with the added features?

Options

  • ACreate an Amazon EMR cluster. Develop PySpark code that can read the timestamp variable as
  • BCreate a processing job in Amazon SageMaker. Develop Python code that can read the
  • CCreate a new flow in Amazon SageMaker Data Wrangler. Import the S3 file, use the Featurize
  • DCreate an AWS Glue job. Develop code that can read the timestamp variable as a string,

How the community answered

(34 responses)
  • A
    3% (1)
  • B
    9% (3)
  • C
    85% (29)
  • D
    3% (1)

Explanation

https://aws.amazon.com/blogs/machine-learning/prepare-time-series-data-with-amazon- sagemaker-data-wrangler/ "Featurize datetime time series transformation to add the month, day of the month, day of the year, week of the year, and quarter features to our dataset."

Topics

#Feature Engineering#Data Preparation#SageMaker Data Wrangler#Operational Overhead

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