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AWS-Certified-Machine-Learning-Specialty-MLS-C01 – AWS Certified Machine Learning - Specialty (MLS-C01)

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Showing 13–15 of 15 questions

Question 13

The chief editor for a product catalog wants the research and development team to build a machine learning system that can be used to detect whether or not individuals in a collection of images are wearing the company's retail brand. The team has a set of training data.

Which machine learning algorithm should the researchers use that BEST meets their requirements?

Select an option, then click Submit answer.

  • ○

    Latent Dirichlet Allocation (LDA)
  • ○

    Recurrent neural network (RNN)
  • ○

    K-means
  • ○

    Convolutional neural network (CNN)
Question 14

An agricultural company is interested in using machine learning to detect specific types of weeds in a 100-acre

grassland field. Currently, the company uses tractor-mounted cameras to capture multiple images of the field

as 10 × 10 grids. The company also has a large training dataset that consists of annotated images of popular

weed classes like broadleaf and non-broadleaf docks.

The company wants to build a weed detection model that will detect specific types of weeds and the location of each type within the field. Once the model is ready, it will be hosted on Amazon SageMaker endpoints. The

model will perform real-time inferencing using the images captured by the cameras.

Which approach should a Machine Learning Specialist take to obtain accurate predictions?

Select an option, then click Submit answer.

  • ○

    Prepare the images in RecordIO format and upload them to Amazon S3. Use Amazon SageMaker to train,
    test, and validate the model using an image classification algorithm to categorize images into various weed
    classes.
  • ○

    Prepare the images in Apache Parquet format and upload them to Amazon S3. Use Amazon SageMaker to
    train, test, and validate the model using an object-detection single-shot multibox detector (SSD) algorithm.
  • ○

    Prepare the images in RecordIO format and upload them to Amazon S3. Use Amazon SageMaker to train,
    test, and validate the model using an object-detection single-shot multibox detector (SSD) algorithm.
  • ○

    Prepare the images in Apache Parquet format and upload them to Amazon S3. Use Amazon SageMaker to
    train, test, and validate the model using an image classification algorithm to categorize images into various
    weed classes.
Question 15

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?

Select an option, then click Submit answer.

  • ○

    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.
  • ○

    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.
  • ○

    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 VPCreate Amazon SageMaker Processing jobs from the notebook.
  • ○

    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.