Online Access Free Amazon.AWS-Certified-Machine-Learning-Specialty.v2022-11-29.q177 Practice Test (Page 6)

AWS-Certified-Machine-Learning-Specialty Exam Question 21

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?

A.Load a smaller subset of the data into the SageMaker notebook and train locally. Confirm that the training code is executing and the model parameters seem reasonable. Initiate a SageMaker training job using the full dataset from the S3 bucket using Pipe input mode.

B.Launch an Amazon EC2 instance with an AWS Deep Learning AMI and attach the S3 bucket to the instance. Train on a small amount of the data to verify the training code and hyperparameters. Go back to Amazon SageMaker and train using the full dataset

C.Use AWS Glue to train a model using a small subset of the data to confirm that the data will be compatible with Amazon SageMaker. Initiate a SageMaker training job using the full dataset from the S3 bucket using Pipe input mode.

D.Load a smaller subset of the data into the SageMaker notebook and train locally. Confirm that the training code is executing and the model parameters seem reasonable. Launch an Amazon EC2 instance with an AWS Deep Learning AMI and attach the S3 bucket to train the full dataset.

AWS-Certified-Machine-Learning-Specialty Exam Question 22

A Machine Learning Specialist is assigned a TensorFlow project using Amazon SageMaker for training, and needs to continue working for an extended period with no Wi-Fi access.
Which approach should the Specialist use to continue working?

A.Install Python 3 and boto3 on their laptop and continue the code development using that environment.

B.Download the SageMaker notebook to their local environment then install Jupyter Notebooks on their laptop and continue the development in a local notebook.

C.Download the TensorFlow Docker container used in Amazon SageMaker from GitHub to their local environment, and use the Amazon SageMaker Python SDK to test the code.

D.Download TensorFlow from tensorflow.org to emulate the TensorFlow kernel in the SageMaker environment.

AWS-Certified-Machine-Learning-Specialty Exam Question 23

During mini-batch training of a neural network for a classification problem, a Data Scientist notices that training accuracy oscillates.
What is the MOST likely cause of this issue?

A.The class distribution in the dataset is imbalanced.

B.Dataset shuffling is disabled.

C.The batch size is too big.

D.The learning rate is very high.

AWS-Certified-Machine-Learning-Specialty Exam Question 24

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 do1?

A.Build the Docker container to be NVIDIA-Docker compatible

B.Bundle the NVIDIA drivers with the Docker image

C.Set the GPU flag in the Amazon SageMaker Create TrainingJob request body

D.Organize the Docker container's file structure to execute on GPU instances.

AWS-Certified-Machine-Learning-Specialty Exam Question 25

A Machine Learning Specialist is building a model to predict future employment rates based on a wide range of economic factors While exploring the data, the Specialist notices that the magnitude of the input features vary greatly The Specialist does not want variables with a larger magnitude to dominate the model What should the Specialist do to prepare the data for model training'?

A.Apply normalization to ensure each field will have a mean of 0 and a variance of 1 to remove any significant magnitude

B.Apply the orthogonal sparse Diagram (OSB) transformation to apply a fixed-size sliding window to generate new features of a similar magnitude.

C.Apply the Cartesian product transformation to create new combinations of fields that are independent of the magnitude

D.Apply quantile binning to group the data into categorical bins to keep any relationships in the data by replacing the magnitude with distribution

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AWS-Certified-Machine-Learning-Specialty Exam Question 21

AWS-Certified-Machine-Learning-Specialty Exam Question 22

AWS-Certified-Machine-Learning-Specialty Exam Question 23

AWS-Certified-Machine-Learning-Specialty Exam Question 24

AWS-Certified-Machine-Learning-Specialty Exam Question 25

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