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Exam AWS Certified Machine Learning - Specialty topic 1 question 307 discussion

Exam question from Amazon's AWS Certified Machine Learning - Specialty
Question #: 307
Topic #: 1
[All AWS Certified Machine Learning - Specialty Questions]

A company uses sensors on devices such as motor engines and factory machines to measure parameters, temperature and pressure. The company wants to use the sensor data to predict equipment malfunctions and reduce services outages.

Machine learning (ML) specialist needs to gather the sensors data to train a model to predict device malfunctions. The ML specialist must ensure that the data does not contain outliers before training the model.

How can the ML specialist meet these requirements with the LEAST operational overhead?

  • A. Load the data into an Amazon SageMaker Studio notebook. Calculate the first and third quartile. Use a SageMaker Data Wrangler data flow to remove only values that are outside of those quartiles.
  • B. Use an Amazon SageMaker Data Wrangler bias report to find outliers in the dataset. Use a Data Wrangler data flow to remove outliers based on the bias report.
  • C. Use an Amazon SageMaker Data Wrangler anomaly detection visualization to find outliers in the dataset. Add a transformation to a Data Wrangler data flow to remove outliers.
  • D. Use Amazon Lookout for Equipment to find and remove outliers from the dataset.
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Suggested Answer: C 🗳️
by kyuhuck at Feb. 7, 2024, 11:36 a.m.
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MultiCloudIronMan
8 months, 1 week ago
Selected Answer: D
from copilot - Amazon Lookout for Equipment is specifically designed for predictive maintenance and can automatically detect anomalies in sensor data, making it a suitable choice for this scenario. It minimizes the need for manual intervention and leverages advanced machine learning models to identify and handle outliers efficiently.
upvoted 1 times
ce0df07
5 days, 20 hours ago
Amazon Lookout for Equipment would introduce an additional service to manage and integrate.
upvoted 1 times
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Peter_Hsieh
1 year ago
Selected Answer: C
https://docs.aws.amazon.com/sagemaker/latest/dg/data-wrangler-analyses.html#data-wrangler-time-series-anomaly-detection
upvoted 3 times
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vkbajoria
1 year, 2 months ago
Selected Answer: C
Data Wrangler can do it all
upvoted 1 times
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AIWave
1 year, 2 months ago
Anomaly detection visualization feature in SageMaker Data Wrangler is designed to identify outliers in the dataset based on sensor data parameters such as temperature and pressure. By visually inspecting the anomalies, the ML specialist can easily identify and remove outliers using transformations within Data Wrangler data flows, minimizing operational overhead.
upvoted 2 times
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AIWave
1 year, 2 months ago
Anomaly detection visualization feature in SageMaker Data Wrangler is designed to identify outliers in the dataset based on sensor data parameters such as temperature and pressure. By visually inspecting the anomalies, the ML specialist can easily identify and remove outliers using transformations within Data Wrangler data flows, minimizing operational overhead.
upvoted 1 times
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Adzz
1 year, 3 months ago
Selected Answer: C
Going with C
upvoted 1 times
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delfoxete
1 year, 3 months ago
Selected Answer: C
agree with C
upvoted 1 times
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kyuhuck
1 year, 3 months ago
Selected Answer: C
Amazon SageMaker Data Wrangler is a tool that helps data scientists and ML developers to prepare data for ML. One of the features of Data Wrangler is the anomaly detection visualization, which uses an unsupervised ML algorithm to identify outliers in the dataset based on statistical properties. The ML specialist can use this feature to quickly explore the sensor data and find any anomalous values that may affect the model performance. The ML specialist can then add a transformation to a Data Wrangler data flow to remove the outliers from the dataset. The data flow can be exported as a script or a pipeline to automate the data preparation process. This option requires the least operational overhead compared to the other options. References: Amazon SageMaker Data Wrangler - Amazon Web Services (AWS) Anomaly Detection Visualization - Amazon SageMaker Transform Data - Amazon SageMaker
upvoted 1 times
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