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Exam AWS Certified Machine Learning - Specialty topic 1 question 181 discussion

Exam question from Amazon's AWS Certified Machine Learning - Specialty
Question #: 181
Topic #: 1
[All AWS Certified Machine Learning - Specialty Questions]

A real-estate company is launching a new product that predicts the prices of new houses. The historical data for the properties and prices is stored in .csv format in an Amazon S3 bucket. The data has a header, some categorical fields, and some missing values. The company's data scientists have used Python with a common open-source library to fill the missing values with zeros. The data scientists have dropped all of the categorical fields and have trained a model by using the open-source linear regression algorithm with the default parameters.
The accuracy of the predictions with the current model is below 50%. The company wants to improve the model performance and launch the new product as soon as possible.
Which solution will meet these requirements with the LEAST operational overhead?

  • A. Create a service-linked role for Amazon Elastic Container Service (Amazon ECS) with access to the S3 bucket. Create an ECS cluster that is based on an AWS Deep Learning Containers image. Write the code to perform the feature engineering. Train a logistic regression model for predicting the price, pointing to the bucket with the dataset. Wait for the training job to complete. Perform the inferences.
  • B. Create an Amazon SageMaker notebook with a new IAM role that is associated with the notebook. Pull the dataset from the S3 bucket. Explore different combinations of feature engineering transformations, regression algorithms, and hyperparameters. Compare all the results in the notebook, and deploy the most accurate configuration in an endpoint for predictions.
  • C. Create an IAM role with access to Amazon S3, Amazon SageMaker, and AWS Lambda. Create a training job with the SageMaker built-in XGBoost model pointing to the bucket with the dataset. Specify the price as the target feature. Wait for the job to complete. Load the model artifact to a Lambda function for inference on prices of new houses.
  • D. Create an IAM role for Amazon SageMaker with access to the S3 bucket. Create a SageMaker AutoML job with SageMaker Autopilot pointing to the bucket with the dataset. Specify the price as the target attribute. Wait for the job to complete. Deploy the best model for predictions.
Show Suggested Answer Hide Answer
Suggested Answer: D 🗳️
by spaceexplorer at April 30, 2022, 2:58 p.m.
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spaceexplorer
Highly Voted 2 years ago
Selected Answer: D
D; A involves too much effort and management overhead.
upvoted 10 times
LydiaGom
2 years ago
Agree. but A has feature engineering which is the problem of the current model... confusing
upvoted 2 times
Raja3sa
1 year, 9 months ago
we don't use Logistic Regression to predict price.
upvoted 4 times
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ZSun
1 year ago
AutoML also contains feature engineering/preprocessing tools.
upvoted 1 times
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loict
Most Recent 8 months, 1 week ago
Selected Answer: D
A. NO - Logistic regression model is for classification, not to predict numerical values B. NO - approach is the highest quality, but takes time C. NO - XGBoost is for classification D. YES - simplest option
upvoted 1 times
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Mickey321
8 months, 3 weeks ago
Selected Answer: D
D for me also
upvoted 1 times
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ADVIT
10 months, 1 week ago
It's D as rest require more operation activities.
upvoted 1 times
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Shailendraa
1 year, 8 months ago
D is Correct: trick to eliminate is A can not as Logistic is classification algo which gives binary outcome.B &C seems a lot of work .
upvoted 3 times
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Morsa
1 year, 10 months ago
Selected Answer: D
The problem is not a classification problem so A is incorrect as logistic regression is used for binary problems. D is the correct solution
upvoted 3 times
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mk5183
1 year, 11 months ago
The problem is not which model you chose but primitive level feature engineering therefore correct answer should be "B"
upvoted 2 times
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edvardo
2 years ago
Selected Answer: D
D. https://aws.amazon.com/sagemaker/autopilot/ Supports missing values, categorical features, etc. The simplest solution for this case
upvoted 3 times
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ckkobe24
2 years ago
why not B？
upvoted 2 times
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