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Exam AWS Certified Machine Learning - Specialty topic 1 question 91 discussion

Exam question from Amazon's AWS Certified Machine Learning - Specialty
Question #: 91
Topic #: 1
[All AWS Certified Machine Learning - Specialty Questions]

A manufacturer of car engines collects data from cars as they are being driven. The data collected includes timestamp, engine temperature, rotations per minute
(RPM), and other sensor readings. The company wants to predict when an engine is going to have a problem, so it can notify drivers in advance to get engine maintenance. The engine data is loaded into a data lake for training.
Which is the MOST suitable predictive model that can be deployed into production?

  • A. Add labels over time to indicate which engine faults occur at what time in the future to turn this into a supervised learning problem. Use a recurrent neural network (RNN) to train the model to recognize when an engine might need maintenance for a certain fault.
  • B. This data requires an unsupervised learning algorithm. Use Amazon SageMaker k-means to cluster the data.
  • C. Add labels over time to indicate which engine faults occur at what time in the future to turn this into a supervised learning problem. Use a convolutional neural network (CNN) to train the model to recognize when an engine might need maintenance for a certain fault.
  • D. This data is already formulated as a time series. Use Amazon SageMaker seq2seq to model the time series.
Show Suggested Answer Hide Answer
Suggested Answer: A 🗳️
by ac71 at Feb. 3, 2021, 4:04 a.m.
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ac71
Highly Voted 2 years, 7 months ago
This is a supervised problem and needs labels. Can't use clustering to find when faults can happen. CNN is for images not for timeseries data here. Hence, A seems appropriate.
upvoted 53 times
youjun
2 years, 4 months ago
AGREE WITH YOU
upvoted 2 times
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astonm13
2 years, 7 months ago
Agree, the answer is A
upvoted 7 times
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loict
Most Recent 8 months ago
Selected Answer: A
A. YES - RNN good for time series as we want to use previous input B. NO - we know the class (fault) ahead of time, it is supervised C. NO - CNN is for images D. NO - seq2seq is for word generation
upvoted 1 times
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Mickey321
8 months, 2 weeks ago
Selected Answer: A
Answer is A
upvoted 1 times
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AjoseO
1 year, 3 months ago
Selected Answer: A
A recurrent neural network (RNN) is a more suitable choice than a convolutional neural network (CNN) because the data collected from the engines is a sequence of values over time, and the goal is to predict a future event (an engine fault). RNNs are designed to handle sequential data and can learn patterns and dependencies over time, making them well-suited for time-series data like this. On the other hand, CNNs are designed for image processing and are not ideal for sequential data.
upvoted 3 times
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spidy20
1 year, 8 months ago
Selected Answer: A
Answer should be A
upvoted 1 times
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Morsa
1 year, 10 months ago
Selected Answer: A
It can only be A. Agree with the comments before
upvoted 1 times
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irimala
1 year, 11 months ago
Selected Answer: A
Obviously A
upvoted 2 times
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apprehensive_scar
2 years, 3 months ago
Selected Answer: A
A - obviously.
upvoted 1 times
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bitsplease
2 years, 3 months ago
Seq2Seq also uses RNN under the hood, BUT option D. did not mention anything about "adding labels"--which is required here--hence --> A
upvoted 3 times
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geekgirl007
2 years, 4 months ago
Selected Answer: A
A is correct. CNN is for images and RNN is for timeseries.
upvoted 1 times
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loyor94478
2 years, 6 months ago
AAAAAAAAAAAA https://towardsdatascience.com/how-to-implement-machine-learning-for-predictive-maintenance-4633cdbe4860
upvoted 2 times
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omar8024
2 years, 6 months ago
I think A is correct
upvoted 1 times
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Vita_Rasta84444
2 years, 6 months ago
It is A
upvoted 1 times
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