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

An ecommerce company is automating the categorization of its products based on images. A data scientist has trained a computer vision model using the Amazon SageMaker image classification algorithm. The images for each product are classified according to specific product lines. The accuracy of the model is too low when categorizing new products. All of the product images have the same dimensions and are stored within an Amazon S3 bucket. The company wants to improve the model so it can be used for new products as soon as possible.
Which steps would improve the accuracy of the solution? (Choose three.)

1. A. Use the SageMaker semantic segmentation algorithm to train a new model to achieve improved accuracy.
2. B. Use the Amazon Rekognition DetectLabels API to classify the products in the dataset.
3. C. Augment the images in the datase
4. D. Use open source libraries to crop, resize, flip, rotate, and adjust the brightness and contrast of the images.
5. E. Use a SageMaker notebook to implement the normalization of pixels and scaling of the image
6. F. Store the new dataset in Amazon S3.
7. G. Use Amazon Rekognition Custom Labels to train a new model.
8. H. Check whether there are class imbalances in the product categories, and apply oversampling or undersampling as require
9. I. Store the new dataset in Amazon S3.

Correct Answer: BCE

A Machine Learning Specialist is developing a custom video recommendation model for an application The dataset used to train this model is very large with millions of data points and is hosted in an Amazon S3 bucket The Specialist wants to avoid loading all of this data onto an Amazon SageMaker notebook instance because it would take hours to move and will exceed the attached 5 GB Amazon EBS volume on the notebook instance.
Which approach allows the Specialist to use all the data to train the model?

1. A. Load a smaller subset of the data into the SageMaker notebook and train locall
2. B. Confirm that thetraining code is executing and the model parameters seem reasonabl
3. C. Initiate a SageMaker training job using the full dataset from the S3 bucket using Pipe input mode.
4. D. Launch an Amazon EC2 instance with an AWS Deep Learning AMI and attach the S3 bucket to theinstanc
5. E. Train on a small amount of the data to verify the training code and hyperparameter
6. F. Go back toAmazon SageMaker and train using the full dataset
7. G. Use AWS Glue to train a model using a small subset of the data to confirm that the data will be compatible with Amazon SageMake
8. H. Initiate a SageMaker training job using the full dataset from the S3 bucket using Pipe input mode.
9. I. Load a smaller subset of the data into the SageMaker notebook and train locall
10. J. Confirm that the training code is executing and the model parameters seem reasonabl
11. K. Launch an Amazon EC2 instance with an AWS Deep Learning AMI and attach the S3 bucket to train the full dataset.

Correct Answer: A

A Machine Learning Specialist working for an online fashion company wants to build a data ingestion solution for the company's Amazon S3-based data lake.
The Specialist wants to create a set of ingestion mechanisms that will enable future capabilities comprised of:
• Real-time analytics
• Interactive analytics of historical data
• Clickstream analytics
• Product recommendations
Which services should the Specialist use?

1. A. AWS Glue as the data dialog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for real-time data insights; Amazon Kinesis Data Firehose for delivery to Amazon ES for clickstream analytics; Amazon EMR to generate personalized product recommendations
2. B. Amazon Athena as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for near-realtime data insights; Amazon Kinesis Data Firehose for clickstream analytics; AWS Glue to generate personalized product recommendations
3. C. AWS Glue as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for historical data insights; Amazon Kinesis Data Firehose for delivery to Amazon ES for clickstream analytics; Amazon EMR to generate personalized product recommendations
4. D. Amazon Athena as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for historical data insights; Amazon DynamoDB streams for clickstream analytics; AWS Glue to generate personalized product recommendations

Correct Answer: A

A machine learning (ML) specialist is using Amazon SageMaker hyperparameter optimization (HPO) to improve a model’s accuracy. The learning rate parameter is specified in the following HPO configuration:

During the results analysis, the ML specialist determines that most of the training jobs had a learning rate between 0.01 and 0.1. The best result had a learning rate of less than 0.01. Training jobs need to run regularly over a changing dataset. The ML specialist needs to find a tuning mechanism that uses different learning rates more evenly from the provided range between MinValue and MaxValue.
Which solution provides the MOST accurate result?

1. A. Modify the HPO configuration as follows: C:\Users\Admin\Desktop\Data\Odt data\Untitled.jpgSelect the most accurate hyperparameter configuration form this HPO job.
2. B. Run three different HPO jobs that use different learning rates form the following intervals for MinValue and MaxValue while using the same number of training jobs for each HPO job:[0.01, 0.1][0.001, 0.01][0.0001, 0.001]Select the most accurate hyperparameter configuration form these three HPO jobs.
3. C. Modify the HPO configuration as follows: C:\Users\Admin\Desktop\Data\Odt data\Untitled.jpgSelect the most accurate hyperparameter configuration form this training job.
4. D. Run three different HPO jobs that use different learning rates form the following intervals for MinValue and MaxValu
5. E. Divide the number of training jobs for each HPO job by three:[0.01, 0.1][0.001, 0.01][0.0001, 0.001]Select the most accurate hyperparameter configuration form these three HPO jobs.

Correct Answer: C