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MLS-C01 Practice Questions

AWS Certified Machine Learning - Specialty

PDF

$15

Number Of Questions

208

Last Update

28/03/2022

Certification

AWS Certified Specialty

Exam Code

MLS-C01

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The Amazon Specialty exam is designed for people who work as solutions architects. The test assesses an applicant’s capacity to develop secure and durable solutions using AWS tools. The Exam also authorizes the following activities:

Create a solution based on appropriate AWS services and architectural moralities that are informed by the needs.

Offer implementation advice based on industry best practices, throughout the workload lifecycle.

The applicant should have had at least one year of hands-on experience developing secure, high-performance, cost-effective, highly obtainable, and scalable systems using AWS services.

Recommended AWS knowledge for MLS-C01 Exam

The applicant should have the following AWS expertise:

Hands-on experience working with computing, networking, storage, management, and database Amazon services.

The ability to define AWS-specific technical needs for a solution that requires it.
The capacity to determine which AWS services are appropriate for a particular specialized condition.
Considerate best practices for creating well-architected solutions on AWS.
A version of the AWS global infrastructure
Understanding AWS security services and features of traditional services.
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Demo Questions

Question # 1
A company is building a demand forecasting model based on machine learning (ML). In thedevelopment stage, an ML specialist uses an Amazon SageMaker notebook to performfeature engineering during work hours that consumes low amounts of CPU and memoryresources. A data engineer uses the same notebook to perform data preprocessing once aday on average that requires very high memory and completes in only 2 hours. The datapreprocessing is not configured to use GPU. All the processes are running well on anml.m5.4xlarge notebook instance.The company receives an AWS Budgets alert that the billing for this month exceeds theallocated budget.Which solution will result in the MOST cost savings?

A. Change the notebook instance type to a memory optimized instance with the samevCPU number as the ml.m5.4xlarge instance has. Stop the notebook when it is not in use.Run both data preprocessing and feature engineering development on that instance.
B. Keep the notebook instance type and size the same. Stop the notebook when it is not inuse. Run data preprocessing on a P3 instance type with the same memory as theml.m5.4xlarge instance by using Amazon SageMaker Processing.
C. Change the notebook instance type to a smaller general purpose instance. Stop thenotebook when it is not in use. Run data preprocessing on an ml.r5 instance with the samememory size as the ml.m5.4xlarge instance by using Amazon SageMaker Processing.
D. Change the notebook instance type to a smaller general purpose instance. Stop thenotebook when it is not in use. Run data preprocessing on an R5 instance with the samememory size as the ml.m5.4xlarge instance by using the Reserved Instance option.
ANSWER: B

Question # 2
A manufacturing company wants to use machine learning (ML) to automate quality controlin its facilities. The facilities are in remote locations and have limited internet connectivity.The company has 20 of training data that consists of labeled images of defective productparts. The training data is in the corporate on-premises data center.The company will use this data to train a model for real-time defect detection in new partsas the parts move on a conveyor belt in the facilities. The company needs a solution thatminimizes costs for compute infrastructure and that maximizes the scalability of resourcesfor training. The solution also must facilitate the company’s use of an ML model in the lowconnectivity environments.Which solution will meet these requirements?

A. Move the training data to an Amazon S3 bucket. Train and evaluate the model by usingAmazon SageMaker. Optimize the model by using SageMaker Neo. Deploy the model on aSageMaker hosting services endpoint.
B. Train and evaluate the model on premises. Upload the model to an Amazon S3 bucket.Deploy the model on an Amazon SageMaker hosting services endpoint.
C. Move the training data to an Amazon S3 bucket. Train and evaluate the model by usingAmazon SageMaker. Optimize the model by using SageMaker Neo. Set up an edge devicein the manufacturing facilities with AWS IoT Greengrass. Deploy the model on the edgedevice.
D. Train the model on premises. Upload the model to an Amazon S3 bucket. Set up anedge device in the manufacturing facilities with AWS IoT Greengrass. Deploy the model onthe edge device.
ANSWER: A

Question # 3
A company is building a predictive maintenance model based on machine learning (ML).The data is stored in a fully private Amazon S3 bucket that is encrypted at rest with AWSKey Management Service (AWS KMS) CMKs. An ML specialist must run datapreprocessing by using an Amazon SageMaker Processing job that is triggered from codein an Amazon SageMaker notebook. The job should read data from Amazon S3, process it,and upload it back to the same S3 bucket. The preprocessing code is stored in a containerimage in Amazon Elastic Container Registry (Amazon ECR). The ML specialist needs togrant permissions to ensure a smooth data preprocessing workflowWhich set of actions should the ML specialist take to meet these requirements?

A. Create an IAM role that has permissions to create Amazon SageMaker Processing jobs,S3 read and write access to the relevant S3 bucket, and appropriate KMS and ECRpermissions. Attach the role to the SageMaker notebook instance. Create an AmazonSageMaker Processing job from the notebook.
B. Create an IAM role that has permissions to create Amazon SageMaker Processing jobs.Attach the role to the SageMaker notebook instance. Create an Amazon SageMakerProcessing job with an IAM role that has read and write permissions to the relevant S3bucket, and appropriate KMS and ECR permissions.
C. Create an IAM role that has permissions to create Amazon SageMaker Processing jobsand to access Amazon ECR. Attach the role to the SageMaker notebook instance. Set upboth an S3 endpoint and a KMS endpoint in the default VPC. Create Amazon SageMakerProcessing jobs from the notebook.
D. Create an IAM role that has permissions to create Amazon SageMaker Processing jobs.Attach the role to the SageMaker notebook instance. Set up an S3 endpoint in the defaultVPC. Create Amazon SageMaker Processing jobs with the access key and secret key ofthe IAM user with appropriate KMS and ECR permissions.
ANSWER : D

Question # 4
A machine learning specialist is developing a proof of concept for government users whoseprimary concern is security. The specialist is using Amazon SageMaker to train aconvolutional neural network (CNN) model for a photo classifier application. The specialistwants to protect the data so that it cannot be accessed and transferred to a remote host bymalicious code accidentally installed on the training container.Which action will provide the MOST secure protection?

A. Remove Amazon S3 access permissions from the SageMaker execution role.
B. Encrypt the weights of the CNN model.
C. Encrypt the training and validation dataset.
D. Enable network isolation for training jobs.
ANSWER : D

Question # 5
A company wants to create a data repository in the AWS Cloud for machine learning (ML)projects. The company wants to use AWS to perform complete ML lifecycles and wants touse Amazon S3 for the data storage. All of the company’s data currently resides onpremises and is 40 in size.The company wants a solution that can transfer and automatically update data between theon-premises object storage and Amazon S3. The solution must support encryption,scheduling, monitoring, and data integrity validation.Which solution meets these requirements?

A. Use the S3 sync command to compare the source S3 bucket and the destination S3bucket. Determine which source files do not exist in the destination S3 bucket and whichsource files were modified.
B. Use AWS Transfer for FTPS to transfer the files from the on-premises storage toAmazon S3.
C. Use AWS DataSync to make an initial copy of the entire dataset. Schedule subsequentincremental transfers of changing data until the final cutover from on premises to AWS.
D. Use S3 Batch Operations to pull data periodically from the on-premises storage. EnableS3 Versioning on the S3 bucket to protect against accidental overwrites.
ANSWER : C

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