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Exam AWS Certified Machine Learning - Specialty topic 1 question 330 discussion

Exam question from Amazon's AWS Certified Machine Learning - Specialty
Question #: 330
Topic #: 1
[All AWS Certified Machine Learning - Specialty Questions]

A company that operates oil platforms uses drones to photograph locations on oil platforms that are difficult for humans to access to search for corrosion.

Experienced engineers review the photos to determine the severity of corrosion. There can be several corroded areas in a single photo. The engineers determine whether the identified corrosion needs to be fixed immediately, scheduled for future maintenance, or requires no action. The corrosion appears in an average of 0.1% of all photos.

A data science team needs to create a solution that automates the process of reviewing the photos and classifying the need for maintenance.

Which combination of steps will meet these requirements? (Choose three.)

  • A. Use an object detection algorithm to train a model to identify corrosion areas of a photo.
  • B. Use Amazon Rekognition with label detection on the photos.
  • C. Use a k-means clustering algorithm to train a model to classify the severity of corrosion in a photo.
  • D. Use an XGBoost algorithm to train a model to classify the severity of corrosion in a photo.
  • E. Perform image augmentation on photos that contain corrosion.
  • F. Perform image augmentation on photos that do not contain corrosion.
Show Suggested Answer Hide Answer
Suggested Answer: ADE 🗳️
by KAST_424 at March 14, 2024, 7:32 p.m.
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LeoD
6 months, 3 weeks ago
Selected Answer: ADE
XGBoost is primarily designed for tabular data. For a task involving image data, XGBoost would not be a suitable choice because it does not have the capability to directly process images or learn from the spatial relationships inherent in image data. In the context of the problem described, a more suitable approach would involve using a deep learning model, e.g., CNNs. Refer to Question 258 where we choose ResNet-50 instead of. XGBoost
upvoted 1 times
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ciscochamps
7 months, 2 weeks ago
Selected Answer: ADE
ADE â—ŠRecognition is for general identification, this requires a specific training with only 1% data available.
upvoted 2 times
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spinatram
8 months, 2 weeks ago
A,B,F You can not use XGBoost for object recognition directly. You need to integrate it with CNN. You can use Rekognition custom for this scenario
upvoted 1 times
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MultiCloudIronMan
9 months ago
Selected Answer: ADE
Recognition is for general identification, this requires a specific training with only 1% data available.
upvoted 1 times
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cloudera3
1 year ago
Selected Answer: ADE
Detecting corrosion is too specialized a task for Rekognition - trying transfer learning would literally mean re-training. AWS blog explaining suitability of Object Detection/Semantic Segmentation (though they prefer the color classification approach using XGBoost): https://aws.amazon.com/blogs/machine-learning/rust-detection-using-machine-learning-on-aws/ The enhancement step may be needed to contrast enhance/sharpen images.
upvoted 3 times
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3eb0542
1 year, 2 months ago
Selected Answer: ABE
The combination of steps that would meet the requirements is indeed A, B, and E
upvoted 1 times
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vkbajoria
1 year, 4 months ago
Selected Answer: ABE
This is object detection problem, which Rekognitation can do since they have 0.1% corrosion photos, augmentation is necessary
upvoted 2 times
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butaman
1 year, 4 months ago
Selected Answer: ADE
A. Use an object detection algorithm: This can help identify corrosion areas in a photo. E. Perform image augmentation on photos with corrosion: This can improve the model’s ability to generalize by increasing the diversity of the training data. D. Use an XGBoost algorithm: This can classify the severity of the corrosion after the areas of corrosion have been identified. It’s effective for multi-class classification problems.
upvoted 4 times
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AIWave
1 year, 4 months ago
Selected Answer: ABE
A: Yes - train a model that identifies corrosion areas within the photos B: Yes - identify and label objects, including corrosion, in the images. C: No - Not for classification D: No - XGBoost doesn't work on images E: Yes - rotation, scaling, and flipping, can enhance the model’s ability to generalize and improve its performance F: not required
upvoted 2 times
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KAST_424
1 year, 4 months ago
Selected Answer: ADE
Object detection algorithm can be trained to identify corrosion rather than too customize Rekognition
upvoted 1 times
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