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

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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: annabella
Question 52

A media company is building a computer vision model to analyze images that are on social media. The model consists of CNNs that the company trained by using images that the company stores in Amazon S3. The company used an Amazon SageMaker training job in File mode with a single Amazon EC2 On-Demand Instance.

Every day, the company updates the model by using about 10,000 images that the company has collected in the last 24 hours. The company configures training with only one epoch. The company wants to speed up training and lower costs without the need to make any code changes.

Which solution will meet these requirements?

Options:

A.

Instead of File mode, configure the SageMaker training job to use Pipe mode. Ingest the data from a pipe.

B.

Instead Of File mode, configure the SageMaker training job to use FastFile mode with no Other changes.

C.

Instead Of On-Demand Instances, configure the SageMaker training job to use Spot Instances. Make no Other changes.

D.

Instead Of On-Demand Instances, configure the SageMaker training job to use Spot Instances. Implement model checkpoints.

Discussion
Question 53

A real estate company wants to create a machine learning model for predicting housing prices based on a

historical dataset. The dataset contains 32 features.

Which model will meet the business requirement?

Options:

A.

Logistic regression

B.

Linear regression

C.

K-means

D.

Principal component analysis (PCA)

Discussion
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Question 54

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?

Options:

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.

Discussion
Question 55

A Machine Learning Specialist observes several performance problems with the training portion of a machine learning solution on Amazon SageMaker The solution uses a large training dataset 2 TB in size and is using the SageMaker k-means algorithm The observed issues include the unacceptable length of time it takes before the training job launches and poor I/O throughput while training the model

What should the Specialist do to address the performance issues with the current solution?

Options:

A.

Use the SageMaker batch transform feature

B.

Compress the training data into Apache Parquet format.

C.

Ensure that the input mode for the training job is set to Pipe.

D.

Copy the training dataset to an Amazon EFS volume mounted on the SageMaker instance.

Discussion
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