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Exam AWS Certified Machine Learning Engineer - Associate MLA-C01 All Questions

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Exam AWS Certified Machine Learning Engineer - Associate MLA-C01 topic 1 question 58 discussion

Exam question from Amazon's AWS Certified Machine Learning Engineer - Associate MLA-C01
Question #: 58
Topic #: 1
[All AWS Certified Machine Learning Engineer - Associate MLA-C01 Questions]

An ML engineer trained an ML model on Amazon SageMaker to detect automobile accidents from dosed-circuit TV footage. The ML engineer used SageMaker Data Wrangler to create a training dataset of images of accidents and non-accidents.
The model performed well during training and validation. However, the model is underperforming in production because of variations in the quality of the images from various cameras.
Which solution will improve the model's accuracy in the LEAST amount of time?

  • A. Collect more images from all the cameras. Use Data Wrangler to prepare a new training dataset.
  • B. Recreate the training dataset by using the Data Wrangler corrupt image transform. Specify the impulse noise option.
  • C. Recreate the training dataset by using the Data Wrangler enhance image contrast transform. Specify the Gamma contrast option.
  • D. Recreate the training dataset by using the Data Wrangler resize image transform. Crop all images to the same size.
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Suggested Answer: B 🗳️
by a4002bd at Nov. 27, 2024, 3:49 a.m.
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6913a18
2 weeks, 6 days ago
Selected Answer: B
Corrupting an image or creating any kind of noise helps make a model more robust. The model can predict with more accuracy even if it receives a corrupted image because it was trained with corrupt and non-corrupt images. https://aws.amazon.com/blogs/machine-learning/prepare-image-data-with-amazon-sagemaker-data-wrangler/
upvoted 1 times
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Lance665
1 month ago
Selected Answer: B
It's B. Did you guys clearly understand the question? "The model performed well during training and validation. However, the model is underperforming in production because of variations in the quality of the images from various cameras." https://aws.amazon.com/blogs/machine-learning/prepare-image-data-with-amazon-sagemaker-data-wrangler/ Corrupting an image or creating any kind of noise helps make a model more robust. The model can predict with more accuracy even if it receives a corrupted image because it was trained with corrupt and non-corrupt images.
upvoted 4 times
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Saransundar
1 month, 2 weeks ago
Selected Answer: C
Option B focus on impulse noise targets noisy images, not quality variations like contrast or lighting. Option C focus on contrast enhancement fixing lighting and clarity issues, directly addressing camera quality variations.
upvoted 1 times
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GiorgioGss
1 month, 3 weeks ago
Selected Answer: C
https://aws.amazon.com/blogs/machine-learning/prepare-image-data-with-amazon-sagemaker-data-wrangler/
upvoted 1 times
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a4002bd
1 month, 3 weeks ago
Selected Answer: C
Enhancing image contrast can help standardize the quality of images from various cameras, making the model more robust to variations in image quality
upvoted 1 times
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