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Amazon Web Services MLS-C01 Dumps Questions Answers

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Last Update: Jun 10, 2023
208 Questions
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AWS Certified Machine Learning - Specialty Questions and Answers

Questions 1

A media company with a very large archive of unlabeled images, text, audio, and video footage wishes to

index its assets to allow rapid identification of relevant content by the Research team. The company wants to

use machine learning to accelerate the efforts of its in-house researchers who have limited machine learning


Which is the FASTEST route to index the assets?



Use Amazon Rekognition, Amazon Comprehend, and Amazon Transcribe to tag data into distinct



Create a set of Amazon Mechanical Turk Human Intelligence Tasks to label all footage.


Use Amazon Transcribe to convert speech to text. Use the Amazon SageMaker Neural Topic Model (NTM)

and Object Detection algorithms to tag data into distinct categories/classes.


Use the AWS Deep Learning AMI and Amazon EC2 GPU instances to create custom models for audio

transcription and topic modeling, and use object detection to tag data into distinct categories/classes.

Questions 2

A monitoring service generates 1 TB of scale metrics record data every minute A Research team performs queries on this data using Amazon Athena The queries run slowly due to the large volume of data, and the team requires better performance

How should the records be stored in Amazon S3 to improve query performance?



CSV files


Parquet files


Compressed JSON



Questions 3

A company is running a machine learning prediction service that generates 100 TB of predictions every day A Machine Learning Specialist must generate a visualization of the daily precision-recall curve from the predictions, and forward a read-only version to the Business team.

Which solution requires the LEAST coding effort?



Run a daily Amazon EMR workflow to generate precision-recall data, and save the results in Amazon S3 Give the Business team read-only access to S3


Generate daily precision-recall data in Amazon QuickSight, and publish the results in a dashboard shared with the Business team


Run a daily Amazon EMR workflow to generate precision-recall data, and save the results in Amazon S3 Visualize the arrays in Amazon QuickSight, and publish them in a dashboard shared with the Business team


Generate daily precision-recall data in Amazon ES, and publish the results in a dashboard shared with the Business team.

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