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NEW QUESTION 40
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 the trend well, but not the seasonality.B. The model predicts the seasonality well, but not the trend.C. The model does not predict the trend or the seasonality well.D. The model predicts both the trend and the seasonality well

Answer: C

 

NEW QUESTION 41
A Data Scientist needs to analyze employment dat
a. The dataset contains approximately 10 million
observations on people across 10 different features. During the preliminary analysis, the Data Scientist notices that income and age distributions are not normal. While income levels shows a right skew as expected, with fewer individuals having a higher income, the age distribution also show a right skew, with fewer older individuals participating in the workforce.
Which feature transformations can the Data Scientist apply to fix the incorrectly skewed data? (Choose two.)

A. Numerical value binningB. High-degree polynomial transformationC. One hot encodingD. Cross-validationE. Logarithmic transformation

Answer: A,D

 

NEW QUESTION 42
A Machine Learning Specialist is developing a daily ETL workflow containing multiple ETL jobs.
The workflow consists of the following processes:
- Start the workflow as soon as data is uploaded to Amazon S3.
- When all the datasets are available in Amazon S3, start an ETL job to join the uploaded datasets with multiple terabyte-sized datasets already stored in Amazon S3.
- Store the results of joining datasets in Amazon S3.
- If one of the jobs fails, send a notification to the Administrator.
Which configuration will meet these requirements?

A. Develop the ETL workflow using AWS Lambda to start an Amazon SageMaker notebook instance. Use a lifecycle configuration script to join the datasets and persist the results in Amazon S3. Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure.B. Use AWS Lambda to chain other Lambda functions to read and join the datasets in Amazon S3 as soon as the data is uploaded to Amazon S3. Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure.C. Develop the ETL workflow using AWS Batch to trigger the start of ETL jobs when data is uploaded to Amazon S3. Use AWS Glue to join the datasets in Amazon S3. Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure.D. Use AWS Lambda to trigger an AWS Step Functions workflow to wait for dataset uploads to complete in Amazon S3. Use AWS Glue to join the datasets. Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure.

Answer: D

Explanation:
https://aws.amazon.com/step-functions/use-cases/

 

NEW QUESTION 43
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? (Choose two.)

A. AWS CloudTrailB. AWS HealthC. Amazon CloudWatchD. AWS ConfigE. AWS Trusted Advisor

Answer: A,C

Explanation:
https://aws.amazon.com/sagemaker/faqs/

 

NEW QUESTION 44
......

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