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NEW QUESTION 34
A Machine Learning Specialist is configuring automatic model tuning in Amazon SageMaker When using the hyperparameter optimization feature, which of the following guidelines should be followed to improve optimization?
Choose the maximum number of hyperparameters supported by
- A. Specify a very large hyperparameter range to allow Amazon SageMaker to cover every possible value.
- B. Use log-scaled hyperparameters to allow the hyperparameter space to be searched as quickly as possible
- C. Execute only one hyperparameter tuning job at a time and improve tuning through successive rounds of experiments
- D. Amazon SageMaker to search the largest number of combinations possible
Answer: B
NEW QUESTION 35
A large mobile network operating company is building a machine learning model to predict customers who are likely to unsubscribe from the service. The company plans to offer an incentive for these customers as the cost of churn is far greater than the cost of the incentive.
The model produces the following confusion matrix after evaluating on a test dataset of 100 customers:
Based on the model evaluation results, why is this a viable model for production?
- A. The precision of the model is 86%, which is greater than the accuracy of the model.
- B. The model is 86% accurate and the cost incurred by the company as a result of false positives is less than the false negatives.
- C. The model is 86% accurate and the cost incurred by the company as a result of false negatives is less than the false positives.
- D. The precision of the model is 86%, which is less than the accuracy of the model.
Answer: D
NEW QUESTION 36
A Machine Learning Specialist is configuring Amazon SageMaker so multiple Data Scientists can access notebooks, train models, and deploy endpoints. To ensure the best operational performance, the Specialist needs to be able to track how often the Scientists are deploying models, GPU and CPU utilization on the deployed SageMaker endpoints, and all errors that are generated when an endpoint is invoked.
Which services are integrated with Amazon SageMaker to track this information? (Select TWO.)
- A. AWS Config
- B. AWS CloudTrail
- C. AWS Health
- D. AWS Trusted Advisor
- E. Amazon CloudWatch
Answer: B,E
NEW QUESTION 37
The displayed graph is from a forecasting model for testing a time series.
Considering the graph only, which conclusion should a Machine Learning Specialist make about the behavior of the model?
- A. The model predicts both the trend and the seasonality well
- B. The model does not predict the trend or the seasonality well.
- C. The model predicts the seasonality well, but not the trend.
- D. The model predicts the trend well, but not the seasonality.
Answer: A
Explanation:
https://machinelearningmastery.com/decompose-time-series-data-trend-seasonality/
NEW QUESTION 38
A Machine Learning Specialist uploads a dataset to an Amazon S3 bucket protected with server-side encryption using AWS KMS.
How should the ML Specialist define the Amazon SageMaker notebook instance so it can read the same dataset from Amazon S3?
- A. Define security group(s) to allow all HTTP inbound/outbound traffic and assign those security group(s) to the Amazon SageMaker notebook instance.
- B. Assign an IAM role to the Amazon SageMaker notebook with S3 read access to the dataset. Grant permission in the KMS key policy to that role.
- C. Сonfigure the Amazon SageMaker notebook instance to have access to the VPC. Grant permission in the KMS key policy to the notebook's KMS role.
- D. Assign the same KMS key used to encrypt data in Amazon S3 to the Amazon SageMaker notebook instance.
Answer: D
Explanation:
Explanation/Reference: https://docs.aws.amazon.com/sagemaker/latest/dg/encryption-at-rest.html
NEW QUESTION 39
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