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Amazon Web Services Updated MLS-C01 Exam Questions and Answers by murphy

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Amazon Web Services MLS-C01 Exam Overview :

Exam Name: AWS Certified Machine Learning - Specialty
Exam Code: MLS-C01 Dumps
Vendor: Amazon Web Services Certification: AWS Certified Specialty
Questions: 281 Q&A's Shared By: murphy
Question 24

A Machine Learning Specialist is configuring automatic model tuning in Amazon SageMaker

When using the hyperparameter optimization feature, which of the following guidelines should be followed to improve optimization?

Choose the maximum number of hyperparameters supported by

Options:

A.

Amazon SageMaker to search the largest number of combinations possible

B.

Specify a very large hyperparameter range to allow Amazon SageMaker to cover every possible value.

C.

Use log-scaled hyperparameters to allow the hyperparameter space to be searched as quickly as possible

D.

Execute only one hyperparameter tuning job at a time and improve tuning through successive rounds of experiments

Discussion
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Question 25

A Machine Learning Specialist receives customer data for an online shopping website. The data includes demographics, past visits, and locality information. The Specialist must develop a machine learning approach to identify the customer shopping patterns, preferences and trends to enhance the website for better service and smart recommendations.

Which solution should the Specialist recommend?

Options:

A.

Latent Dirichlet Allocation (LDA) for the given collection of discrete data to identify patterns in the customer database.

B.

A neural network with a minimum of three layers and random initial weights to identify patterns in the customer database

C.

Collaborative filtering based on user interactions and correlations to identify patterns in the customer database

D.

Random Cut Forest (RCF) over random subsamples to identify patterns in the customer database

Discussion
Question 26

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?

Options:

A.

Bundle the NVIDIA drivers with the Docker image

B.

Build the Docker container to be NVIDIA-Docker compatible

C.

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

D.

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

Discussion
Question 27

A machine learning specialist stores IoT soil sensor data in Amazon DynamoDB table and stores weather event data as JSON files in Amazon S3. The dataset in DynamoDB is 10 GB in size and the dataset in Amazon S3 is 5 GB in size. The specialist wants to train a model on this data to help predict soil moisture levels as a function of weather events using Amazon SageMaker.

Which solution will accomplish the necessary transformation to train the Amazon SageMaker model with the LEAST amount of administrative overhead?

Options:

A.

Launch an Amazon EMR cluster. Create an Apache Hive external table for the DynamoDB table and S3 data. Join the Hive tables and write the results out to Amazon S3.

B.

Crawl the data using AWS Glue crawlers. Write an AWS Glue ETL job that merges the two tables and writes the output to an Amazon Redshift cluster.

C.

Enable Amazon DynamoDB Streams on the sensor table. Write an AWS Lambda function that consumes the stream and appends the results to the existing weather files in Amazon S3.

D.

Crawl the data using AWS Glue crawlers. Write an AWS Glue ETL job that merges the two tables and writes the output in CSV format to Amazon S3.

Discussion
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