MLS-C01 Question #113: Real Exam Question with Answer & Explanation

Question

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 expertise. Which is the FASTEST route to index the assets?

Options

• AUse Amazon Rekognition, Amazon Comprehend, and Amazon Transcribe to tag data into distinct
• BCreate a set of Amazon Mechanical Turk Human Intelligence Tasks to label all footage.
• CUse Amazon Transcribe to convert speech to text. Use the Amazon SageMaker Neural Topic
• DUse the AWS Deep Learning AMI and Amazon EC2 GPU instances to create custom models for

Explanation

A media company needs to rapidly index a very large archive of various unlabeled media assets using ML, with researchers having limited ML expertise.

Common mistakes.

• B. Amazon Mechanical Turk involves human labeling, which is a time-consuming and manual process that would not be the fastest route for indexing a "very large archive" compared to automated ML services.
• C. While Amazon Transcribe is useful, pairing it with Amazon SageMaker Neural Topic Model requires more ML expertise and custom model development than using fully managed, out-of-the-box AI services for all media types, making it less fast.
• D. Using AWS Deep Learning AMI and Amazon EC2 GPU instances for custom model creation requires significant machine learning expertise, development time, and effort, which contradicts the requirement for the fastest route and limited ML expertise.

Concept tested. Leveraging managed AI services for rapid content indexing

Reference. https://aws.amazon.com/rekognition/

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