Amazon MLA-C01 Online Practice Questions and Exam Preparation

Amazon MLA-C01 Online Questions & Answers

• Question 1:

  Which algorithm is most suitable for a use case that requires clustering unstructured text data?

  A. K-Means
  B. XGBoost
  C. Linear Regression
  D. Random Forest
  A. K-Means

• Question 2:

  A company intends to deploy a machine learning model for production inference on an Amazon SageMaker endpoint. The inference payload size will range from 100 MB to 300 MB, and each inference request must be processed within 60 minutes or less.

  Which Amazon SageMaker inference option would best fulfill these requirements?

  A. Serverless inference
  B. Asynchronous inference
  C. Real-time inference
  D. Batch transform
  B. Asynchronous inference

• Question 3:

  A company needs to run a batch data-processing job on Amazon EC2 instances. The job will run during the weekend and will take 90 minutes to nish running. The processing can handle interruptions. The company will run the job every weekend for the next 6 months. Which EC2 instance purchasing option will meet these requirements MOST cost-effectively?

  A. Spot Instances
  B. Reserved Instances
  C. On-Demand Instances
  D. Dedicated Instances
  A. Spot Instances

• Question 4:

  A company that has hundreds of data scientists is using Amazon SageMaker to create ML models. The models are in model groups in the SageMaker Model Registry. The data scientists are grouped into three categories: computer vision,

  natural language processing (NLP), and speech recognition. An ML engineer needs to implement a solution to organize the existing models into these groups to improve model discoverability at scale. The solution must not affect the integrity

  of the model artifacts and their existing groupings.

  Which solution will meet these requirements?

  A. Create a custom tag for each of the three categories. Add the tags to the model packages in the SageMaker Model Registry.
  B. Create a model group for each category. Move the existing models into these category model groups.
  C. Use SageMaker ML Lineage Tracking to automatically identify and tag which model groups should contain the models.
  D. Create a Model Registry collection for each of the three categories. Move the existing model groups into the collections.
  A. Create a custom tag for each of the three categories. Add the tags to the model packages in the SageMaker Model Registry.

• Question 5:

  A company uses a hybrid cloud environment. A model that is deployed on premises uses data in Amazon 53 to provide customers with a live conversational engine. The model is using sensitive data. An ML engineer needs to implement a solution to identify and remove the sensitive data. Which solution will meet these requirements with the LEAST operational overhead?

  A. Deploy the model on Amazon SageMaker. Create a set of AWS Lambda functions to identify and remove the sensitive data.
  B. Deploy the model on an Amazon Elastic Container Service (Amazon ECS) cluster that uses AWS Fargate. Create an AWS Batch job to identify and remove the sensitive data.
  C. Use Amazon Macie to identify the sensitive data. Create a set of AWS Lambda functions to remove the sensitive data.
  D. Use Amazon Comprehend to identify the sensitive data. Launch Amazon EC2 instances to remove the sensitive data.
  C. Use Amazon Macie to identify the sensitive data. Create a set of AWS Lambda functions to remove the sensitive data.

• Question 6:

  An ML engineer normalized training data by using min-max normalization in AWS Glue DataBrew. The ML engineer must normalize the production inference data in the same way as the training data before passing the production inference

  data to the model for predictions.

  Which solution will meet this requirement?

  A. Apply statistics from a well-known dataset to normalize the production samples.
  B. Keep the min-max normalization statistics from the training set. Use these values to normalize the production samples.
  C. Calculate a new set of min-max normalization statistics from a batch of production samples. Use these values to normalize all the production samples.
  D. Calculate a new set of min-max normalization statistics from each production sample. Use these values to normalize all the production samples.
  B. Keep the min-max normalization statistics from the training set. Use these values to normalize the production samples.

• Question 7:

  A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central model registry, model deployment, and model

  monitoring. The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3. The company needs to run an on-demand work ow to monitor bias drift for models that are

  deployed to real-time endpoints from the application.

  Which action will meet this requirement?

  A. Configure the application to invoke an AWS Lambda function that runs a SageMaker Clarify job.
  B. Invoke an AWS Lambda function to pull the sagemaker-model-monitor-analyzer built-in SageMaker image.
  C. Use AWS Glue Data Quality to monitor bias.
  D. Use SageMaker notebooks to compare the bias.
  A. Configure the application to invoke an AWS Lambda function that runs a SageMaker Clarify job.

• Question 8:

  A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central model registry, model deployment, and model

  monitoring. The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3. The company needs to use the central model registry to manage different versions of models

  in the application.

  Which action will meet this requirement with the LEAST operational overhead?

  A. Create a separate Amazon Elastic Container Registry (Amazon ECR) repository for each model.
  B. Use Amazon Elastic Container Registry (Amazon ECR) and unique tags for each model version.
  C. Use the SageMaker Model Registry and model groups to catalog the models.
  D. Use the SageMaker Model Registry and unique tags for each model version.
  C. Use the SageMaker Model Registry and model groups to catalog the models.

• Question 9:

  An ML engineer trained an ML model on Amazon SageMaker to detect automobile accidents from dosed-circuit TV footage. The ML engineer used SageMaker Data Wrangler to create a training dataset of images of accidents and non-

  accidents. The model performed well during training and validation. However, the model is underperforming in production because of variations in the quality of the images from various cameras.

  Which solution will improve the model's accuracy in the LEAST amount of time?

  A. Collect more images from all the cameras. Use Data Wrangler to prepare a new training dataset.
  B. Recreate the training dataset by using the Data Wrangler corrupt image transform. Specify the impulse noise option.
  C. Recreate the training dataset by using the Data Wrangler enhance image contrast transform. Specify the Gamma contrast option.
  D. Recreate the training dataset by using the Data Wrangler resize image transform. Crop all images to the same size.
  B. Recreate the training dataset by using the Data Wrangler corrupt image transform. Specify the impulse noise option.

• Question 10:

  A company is using an Amazon Redshift database as its single data source. Some of the data is sensitive. A data scientist needs to use some of the sensitive data from the database. An ML engineer must give the data scientist access to the data without transforming the source data and without storing anonymized data in the database. Which solution will meet these requirements with the LEAST implementation effort?

  A. Configure dynamic data masking policies to control how sensitive data is shared with the data scientist at query time.
  B. Create a materialized view with masking logic on top of the database. Grant the necessary read permissions to the data scientist.
  C. Unload the Amazon Redshift data to Amazon S3. Use Amazon Athena to create schema-on-read with masking logic. Share the view with the data scientist.
  D. Unload the Amazon Redshift data to Amazon S3. Create an AWS Glue job to anonymize the data. Share the dataset with the data scientist.
  A. Configure dynamic data masking policies to control how sensitive data is shared with the data scientist at query time.