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Exam AWS Certified Machine Learning - Specialty All Questions

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Exam AWS Certified Machine Learning - Specialty topic 1 question 103 discussion

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?

  • A. Use Amazon Rekognition, Amazon Comprehend, and Amazon Transcribe to tag data into distinct categories/classes.
  • B. Create a set of Amazon Mechanical Turk Human Intelligence Tasks to label all footage.
  • C. 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.
  • D. 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.
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Suggested Answer: A 🗳️

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SophieSu
Highly Voted 3 years, 7 months ago
A. Fastest route must Amazon Services.
upvoted 27 times
AShahine21
3 years, 6 months ago
Amazon Mechanical Turk is an Amazon service
upvoted 2 times
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loict
Most Recent 1 year, 7 months ago
Selected Answer: A
A. YES - Rekognition with built-in labels for images & video, Transcribe to convert sound to text and Comprehend for Topic Modeling B. NO - complicated C. NO - complicated D. NO - complicated
upvoted 1 times
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Mickey321
1 year, 8 months ago
Selected Answer: A
AWS services for fastest route
upvoted 1 times
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angus
1 year, 11 months ago
why not C? C. 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.
upvoted 1 times
winstonmcgee69
10 months, 1 week ago
this takes time and u need to have atleast some technical ML expertise
upvoted 1 times
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Flysun
2 years, 1 month ago
I will choose B,https://aws.amazon.com/cn/getting-started/hands-on/machine-learning-tutorial-label-training-data/
upvoted 1 times
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Valcilio
2 years, 1 month ago
Selected Answer: A
Mechanical Turk is the most accurate, but the three services in letter A is the fastest!
upvoted 1 times
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AjoseO
2 years, 2 months ago
Selected Answer: A
A. Use Amazon Rekognition, Amazon Comprehend, and Amazon Transcribe to tag data into distinct categories/classes is the fastest route to index the assets. These AWS services provide pre-built machine learning models that can be used to tag the content in the archive without the need for building custom models from scratch. This option would be faster than using custom models with the AWS Deep Learning AMI and Amazon EC2 GPU instances, or using Amazon Mechanical Turk for human labeling. Additionally, the use of pre-built models reduces the need for machine learning expertise, aligning with the company's goal of accelerating efforts by its in-house researchers.
upvoted 2 times
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VinceCar
2 years, 5 months ago
A. Option B is for those without ML experience. But "researchers who have limited machine learning expertise“, so A is better.
upvoted 2 times
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hess
3 years, 3 months ago
A. The most straight forward use of services.
upvoted 2 times
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YJ4219
3 years, 6 months ago
I would have said B, but in B it says "label footage" which means it ignored the rest of the data, so i'd go with A
upvoted 1 times
Madwyn
3 years, 6 months ago
The question said "a very large archive" meaning a lot of money to pay for labour. B won't be as fast as machine, plus you only label the footage, ignored other stuff.
upvoted 2 times
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AjithkumarSL
3 years, 6 months ago
Would go for A
upvoted 1 times
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AShahine21
3 years, 6 months ago
I will go with B
upvoted 1 times
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Juka3lj
3 years, 7 months ago
Correct answer is A
upvoted 3 times
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ksrivastavaSumit
3 years, 7 months ago
B. as no one in-house is an expert and It probably is the fastest way to get there
upvoted 2 times
AhmedAbuMusa
3 years, 6 months ago
Take into consideration that it is "a very large archive"
upvoted 1 times
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A (35%)
C (25%)
B (20%)
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