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

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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: 322 Q&A's Shared By: azalea
Question 84

An ecommerce company wants to train a large image classification model with 10.000 classes. The company runs multiple model training iterations and needs to minimize operational overhead and cost. The company also needs to avoid loss of work and model retraining.

Which solution will meet these requirements?

Options:

A.

Create the training jobs as AWS Batch jobs that use Amazon EC2 Spot Instances in a managed compute environment.

B.

Use Amazon EC2 Spot Instances to run the training jobs. Use a Spot Instance interruption notice to save a snapshot of the model to Amazon S3 before an instance is terminated.

C.

Use AWS Lambda to run the training jobs. Save model weights to Amazon S3.

D.

Use managed spot training in Amazon SageMaker. Launch the training jobs with checkpointing enabled.

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Question 85

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 AWS Key Management Service (AWS KMS) CMKs. An ML specialist must run data preprocessing by using an Amazon SageMaker Processing job that is triggered from code in 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 container image in Amazon Elastic Container Registry (Amazon ECR). The ML specialist needs to grant permissions to ensure a smooth data preprocessing workflow.

Which set of actions should the ML specialist take to meet these requirements?

Options:

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 ECR permissions. Attach the role to the SageMaker notebook instance. Create an Amazon SageMaker 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 SageMaker Processing job with an IAM role that has read and write permissions to the relevant S3 bucket, and appropriate KMS and ECR permissions.

C.

Create an IAM role that has permissions to create Amazon SageMaker Processing jobs and to access Amazon ECR. Attach the role to the SageMaker notebook instance. Set up both an S3 endpoint and a KMS endpoint in the default VPC. Create Amazon SageMaker Processing 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 default VPC. Create Amazon SageMaker Processing jobs with the access key and secret key of the IAM user with appropriate KMS and ECR permissions.

Discussion
Question 86

A company operates large cranes at a busy port. The company plans to use machine learning (ML) for predictive maintenance of the cranes to avoid unexpected breakdowns and to improve productivity.

The company already uses sensor data from each crane to monitor the health of the cranes in real time. The sensor data includes rotation speed, tension, energy consumption, vibration, pressure, and …perature for each crane. The company contracts AWS ML experts to implement an ML solution.

Which potential findings would indicate that an ML-based solution is suitable for this scenario? (Select TWO.)

Options:

A.

The historical sensor data does not include a significant number of data points and attributes for certain time periods.

B.

The historical sensor data shows that simple rule-based thresholds can predict crane failures.

C.

The historical sensor data contains failure data for only one type of crane model that is in operation and lacks failure data of most other types of crane that are in operation.

D.

The historical sensor data from the cranes are available with high granularity for the last 3 years.

E.

The historical sensor data contains most common types of crane failures that the company wants to predict.

Discussion
Question 87

A company has set up and deployed its machine learning (ML) model into production with an endpoint using Amazon SageMaker hosting services. The ML team has configured automatic scaling for its SageMaker instances to support workload changes. During testing, the team notices that additional instances are being launched before the new instances are ready. This behavior needs to change as soon as possible.

How can the ML team solve this issue?

Options:

A.

Decrease the cooldown period for the scale-in activity. Increase the configured maximum capacity of instances.

B.

Replace the current endpoint with a multi-model endpoint using SageMaker.

C.

Set up Amazon API Gateway and AWS Lambda to trigger the SageMaker inference endpoint.

D.

Increase the cooldown period for the scale-out activity.

Discussion
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