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NEW QUESTION 24
A Machine Learning Specialist has created a deep learning neural network model that performs well on the training data but performs poorly on the test data.
Which of the following methods should the Specialist consider using to correct this? (Select THREE.)

A. Increase regularization.B. Increase feature combinations.C. Decrease feature combinations.D. Increase dropout.E. Decrease dropout.F. Decrease regularization.

Answer: A,B,E

 

NEW QUESTION 25
A Machine Learning Specialist is packaging a custom ResNet model into a Docker container so the company can leverage Amazon SageMaker for training. The Specialist is using Amazon EC2 P3 instances to train the model and needs to properly configure the Docker container to leverage the NVIDIA GPUs.
What does the Specialist need to do?

A. Organize the Docker container's file structure to execute on GPU instances.B. Set the GPU flag in the Amazon SageMaker CreateTrainingJob request bodyC. Bundle the NVIDIA drivers with the Docker image.D. Build the Docker container to be NVIDIA-Docker compatible.

Answer: C

 

NEW QUESTION 26
A Machine Learning Specialist has completed a proof of concept for a company using a small data sample and now the Specialist is ready to implement an end-to-end solution in AWS using Amazon SageMaker The historical training data is stored in Amazon RDS Which approach should the Specialist use for training a model using that data?

A. Write a direct connection to the SQL database within the notebook and pull data inB. Move the data to Amazon ElastiCache using AWS DMS and set up a connection within the notebook to pull data in for fast access.C. Push the data from Microsoft SQL Server to Amazon S3 using an AWS Data Pipeline and provide the S3 location within the notebook.D. Move the data to Amazon DynamoDB and set up a connection to DynamoDB within the notebook to pull data in

Answer: C

 

NEW QUESTION 27
A credit card company wants to build a credit scoring model to help predict whether a new credit card applicant will default on a credit card payment. The company has collected data from a large number of sources with thousands of raw attributes. Early experiments to train a classification model revealed that many attributes are highly correlated, the large number of features slows down the training speed significantly, and that there are some overfitting issues.
The Data Scientist on this project would like to speed up the model training time without losing a lot of information from the original dataset.
Which feature engineering technique should the Data Scientist use to meet the objectives?

A. Normalize all numerical values to be between 0 and 1B. Cluster raw data using k-means and use sample data from each cluster to build a new datasetC. Use an autoencoder or principal component analysis (PCA) to replace original features with new featuresD. Run self-correlation on all features and remove highly correlated features

Answer: A

 

NEW QUESTION 28
A monitoring service generates 1 TB of scale metrics record data every minute. A Research team performs queries on this data using Amazon Athena. The queries run slowly due to the large volume of data, and the team requires better performance.
How should the records be stored in Amazon S3 to improve query performance?

A. Compressed JSONB. Parquet filesC. CSV filesD. RecordIO

Answer: B

 

NEW QUESTION 29
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