Amazon MLA-C01 Exam Practice Test Instant Access

Question 1

A company has a team of data scientists who use Amazon SageMaker notebook instances to test ML models. When the data scientists need new permissions, the company attaches the permissions to each individual role that was created during the creation of the SageMaker notebook instance.

The company needs to centralize management of the team's permissions.

Which solution will meet this requirement?

ACreate a single IAM role that has the necessary permissions. Attach the role to each notebook instance that the team uses.

BCreate a single IAM group. Add the data scientists to the group. Associate the group with each notebook instance that the team uses.

CCreate a single IAM user. Attach the AdministratorAccess AWS managed IAM policy to the user. Configure each notebook instance to use the IAM user.

DCreate a single IAM group. Add the data scientists to the group. Create an IAM role. Attach the AdministratorAccess AWS managed IAM policy to the role. Associate the role with the group. Associate the group with each notebook instance that the team uses.

Answer : A

Question 2

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?

ACollect more images from all the cameras. Use Data Wrangler to prepare a new training dataset.

BRecreate the training dataset by using the Data Wrangler corrupt image transform. Specify the impulse noise option.

CRecreate the training dataset by using the Data Wrangler enhance image contrast transform. Specify the Gamma contrast option.

DRecreate the training dataset by using the Data Wrangler resize image transform. Crop all images to the same size.

Answer : B

Question 3

An ML engineer needs to process thousands of existing CSV objects and new CSV objects that are uploaded. The CSV objects are stored in a central Amazon S3 bucket and have the same number of columns. One of the columns is a transaction date. The ML engineer must query the data based on the transaction date.

Which solution will meet these requirements with the LEAST operational overhead?

AUse an Amazon Athena CREATE TABLE AS SELECT (CTAS) statement to create a table based on the transaction date from data in the central S3 bucket. Query the objects from the table.

BCreate a new S3 bucket for processed data. Set up S3 replication from the central S3 bucket to the new S3 bucket. Use S3 Object Lambda to query the objects based on transaction date.

CCreate a new S3 bucket for processed data. Use AWS Glue for Apache Spark to create a job to query the CSV objects based on transaction date. Configure the job to store the results in the new S3 bucket. Query the objects from the new S3 bucket.

DCreate a new S3 bucket for processed data. Use Amazon Data Firehose to transfer the data from the central S3 bucket to the new S3 bucket. Configure Firehose to run an AWS Lambda function to query the data based on transaction date.

Answer : A

Question 4

A company is using ML to predict the presence of a specific weed in a farmer's field. The company is using the Amazon SageMaker linear learner built-in algorithm with a value of multiclass_dassifier for the predictorjype hyperparameter.

What should the company do to MINIMIZE false positives?

ASet the value of the weight decay hyperparameter to zero.

BIncrease the number of training epochs.

CIncrease the value of the target_precision hyperparameter.

DChange the value of the predictorjype hyperparameter to regressor.

Answer : C

Question 5

A company is running ML models on premises by using custom Python scripts and proprietary datasets. The company is using PyTorch. The model building requires unique domain knowledge. The company needs to move the models to AWS.

Which solution will meet these requirements with the LEAST effort?

AUse SageMaker built-in algorithms to train the proprietary datasets.

BUse SageMaker script mode and premade images for ML frameworks.

CBuild a container on AWS that includes custom packages and a choice of ML frameworks.

DPurchase similar production models through AWS Marketplace.

Answer : B

Question 6

A company has a large collection of chat recordings from customer interactions after a product release. An ML engineer needs to create an ML model to analyze the chat dat

a. The ML engineer needs to determine the success of the product by reviewing customer sentiments about the product.

Which action should the ML engineer take to complete the evaluation in the LEAST amount of time?

AUse Amazon Rekognition to analyze sentiments of the chat conversations.

BTrain a Naive Bayes classifier to analyze sentiments of the chat conversations.

CUse Amazon Comprehend to analyze sentiments of the chat conversations.

DUse random forests to classify sentiments of the chat conversations.

Answer : C

Question 7

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 dat

a. 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?

ADeploy the model on Amazon SageMaker. Create a set of AWS Lambda functions to identify and remove the sensitive data.

BDeploy 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.

CUse Amazon Macie to identify the sensitive data. Create a set of AWS Lambda functions to remove the sensitive data.

DUse Amazon Comprehend to identify the sensitive data. Launch Amazon EC2 instances to remove the sensitive data.

Answer : C

Amazon AWS Certified Machine Learning Engineer - Associate MLA-C01 Exam Practice Test