AWS-Certified-Machine-Learning-Specialty Free Practice Test

A machine learning (ML) specialist wants to create a data preparation job that uses a PySpark script with complex window aggregation operations to create data for training and testing. The ML specialist needs to evaluate the impact of the number of features and the sample count on model performance.
Which approach should the ML specialist use to determine the ideal data transformations for the model?

1. A. Add an Amazon SageMaker Debugger hook to the script to capture key metric
2. B. Run the script as an AWS Glue job.
3. C. Add an Amazon SageMaker Experiments tracker to the script to capture key metric
4. D. Run the script as an AWS Glue job.
5. E. Add an Amazon SageMaker Debugger hook to the script to capture key parameter
6. F. Run the script as a SageMaker processing job.
7. G. Add an Amazon SageMaker Experiments tracker to the script to capture key parameter
8. H. Run the script as a SageMaker processing job.

Correct Answer: B

This graph shows the training and validation loss against the epochs for a neural network The network being trained is as follows
• Two dense layers one output neuron
• 100 neurons in each layer
• 100 epochs
• Random initialization of weights

Which technique can be used to improve model performance in terms of accuracy in the validation set?

1. A. Early stopping
2. B. Random initialization of weights with appropriate seed
3. C. Increasing the number of epochs
4. D. Adding another layer with the 100 neurons

Correct Answer: D

A Machine Learning Specialist trained a regression model, but the first iteration needs optimizing. The Specialist needs to understand whether the model is more frequently overestimating or underestimating the target.
What option can the Specialist use to determine whether it is overestimating or underestimating the target value?

1. A. Root Mean Square Error (RMSE)
2. B. Residual plots
3. C. Area under the curve
4. D. Confusion matrix

Correct Answer: B

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?

1. A. 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
2. B. 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
3. 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
4. D. 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

Correct Answer: A

A medical imaging company wants to train a computer vision model to detect areas of concern on patients' CT scans. The company has a large collection of unlabeled CT scans that are linked to each patient and stored in an Amazon S3 bucket. The scans must be accessible to authorized users only. A machine learning engineer needs to build a labeling pipeline.
Which set of steps should the engineer take to build the labeling pipeline with the LEAST effort?

1. A. Create a workforce with AWS Identity and Access Management (IAM). Build a labeling tool on Amazon EC2 Queue images for labeling by using Amazon Simple Queue Service (Amazon SQS). Write the labeling instructions.
2. B. Create an Amazon Mechanical Turk workforce and manifest fil
3. C. Create a labeling job by using the built-in image classification task type in Amazon SageMaker Ground Trut
4. D. Write the labeling instructions.
5. E. Create a private workforce and manifest fil
6. F. Create a labeling job by using the built-in bounding box task type in Amazon SageMaker Ground Trut
7. G. Write the labeling instructions.
8. H. Create a workforce with Amazon Cognit
9. I. Build a labeling web application with AWS Amplif
10. J. Build a labeling workflow backend using AWS Lambd
11. K. Write the labeling instructions.

Correct Answer: C
https://docs.aws.amazon.com/sagemaker/latest/dg/sms-workforce-private.html