Exam AWS Certified Machine Learning - Specialty topic 1 question 312 discussion

Exam question from Amazon's AWS Certified Machine Learning - Specialty

Question #: 312
Topic #: 1

[All AWS Certified Machine Learning - Specialty Questions]

A data scientist receives a new dataset in .csv format and stores the dataset in Amazon S3. The data scientist will use the dataset to train a machine learning (ML) model.

The data scientist first needs to identify any potential data quality issues in the dataset. The data scientist must identify values that are missing or values that are not valid. The data scientist must also identify the number of outliers in the dataset.

Which solution will meet these requirements with the LEAST operational effort?

A. Create an AWS Glue job to transform the data from .csv format to Apache Parquet format. Use an AWS Glue crawler and Amazon Athena with appropriate SQL queries to retrieve the required information.
B. Leave the dataset in .csv format. Use an AWS Glue crawler and Amazon Athena with appropriate SQL queries to retrieve the required information.
C. Create an AWS Glue job to transform the data from .csv format to Apache Parquet format. Import the data into Amazon SageMaker Data Wrangler. Use the Data Quality and Insights Report to retrieve the required information.
D. Leave the dataset in .csv format. Import the data into Amazon SageMaker Data Wrangler. Use the Data Quality and Insights Report to retrieve the required information.

Show Suggested Answer

Suggested Answer: D 🗳️

by AIWave at March 9, 2024, 10:26 p.m.

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Comments

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MultiCloudIronMan

8 months ago

Selected Answer: D

Data wrangler design for ML etl with inbuilt functions

upvoted 1 times

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vkbajoria

1 year, 2 months ago

Selected Answer: D

Data Wrangler best tool for the job

upvoted 1 times

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rav009

1 year, 2 months ago

Selected Answer: D

Data wrangler for all you need when prepare data for ML.

upvoted 1 times

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AIWave

1 year, 2 months ago

Selected Answer: D

A: No - more manual/operational overhead B: No - more manual/operational overhead C: No - Data transformation to parquet requires more unnecessary operational overhead D: Yes - least operational effort - wrangler has built-in identification of data quality issues and outliers

upvoted 2 times

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