Amazon MLA-C01 Practice Exam Questions

Amazon MLA-C01 Sample Question Answers

Question # 1

A company wants to predict the success of advertising campaigns by considering the color scheme of each advertisement. An ML engineer is preparing data for a neural network model. The dataset includes color information as categorical data. Which technique for feature engineering should the ML engineer use for the model? 

A. Apply label encoding to the color categories. Automatically assign each color a unique integer. 
B. Implement padding to ensure that all color feature vectors have the same length. 
C. Perform dimensionality reduction on the color categories. 
D. One-hot encode the color categories to transform the color scheme feature into a binary matrix. 

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Question # 2

An ML engineer is using AWS CodeDeploy to deploy new container versions for inference on Amazon ECS. The deployment must shift 10% of traffic initially, and the remaining 90% must shift within 10–15 minutes. Which deployment configuration meets these requirements? 

A. CodeDeployDefault.LambdaLinear10PercentEvery10Minutes
 B. CodeDeployDefault.ECSAllAtOnce 
C. CodeDeployDefault.ECSCanary10Percent15Minutes
 D. CodeDeployDefault.LambdaCanary10Percent15Minutes 

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Question # 3

A company runs an ML model on Amazon SageMaker AI. The company uses an automatic process that makes API calls to create training jobs for the model. The company has new compliance rules that prohibit the collection of aggregated metadata from training jobs. Which solution will prevent SageMaker AI from collecting metadata from the training jobs? 

A. Opt out of metadata tracking for any training job that is submitted. 
B. Ensure that training jobs are running in a private subnet in a custom VPC. 
C. Encrypt the training data with an AWS Key Management Service (AWS KMS) customer managed key. 
D. Reconfigure the training jobs to use only AWS Nitro instances. 

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Question # 4

A company needs to create a central catalog for all the company's ML models. The models are in AWS accounts where the company developed the models initially. The models are hosted in Amazon Elastic Container Registry (Amazon ECR) repositories. Which solution will meet these requirements? 

A. Configure ECR cross-account replication for each existing ECR repository. Ensure that each model is visible in each AWS account. 
B. Create a new AWS account with a new ECR repository as the central catalog. Configure ECR cross-account replication between the initial ECR repositories and the central catalog. 
C. Use the Amazon SageMaker Model Registry to create a model group for models hosted in Amazon ECR. Create a new AWS account. In the new account, use the SageMaker Model Registry as the central catalog. Attach a cross-account resource policy to each model group in the initial AWS accounts. 
D. Use an AWS Glue Data Catalog to store the models. Run an AWS Glue crawler to migrate the models from the ECR repositories to the Data Catalog. Configure crossaccount access to the Data Catalog. 

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Question # 5

A healthcare company wants to detect irregularities in patient vital signs that could indicate early signs of a medical condition. The company has an unlabeled dataset that includes patient health records, medication history, and lifestyle changes. Which algorithm and hyperparameter should the company use to meet this requirement? 

A. Use the Amazon SageMaker AI XGBoost algorithm. Set max_depth to greater than 100 to regulate tree complexity. 
B. Use the Amazon SageMaker AI k-means clustering algorithm. Set k to determine the number of clusters. 
C. Use the Amazon SageMaker AI DeepAR algorithm. Set epochs to the number of training iterations. 
D. Use the Amazon SageMaker AI Random Cut Forest (RCF) algorithm. Set num_trees to greater than 100. 

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Question # 6

A company uses the Amazon SageMaker AI Object2Vec algorithm to train an ML model. The model performs well on training data but underperforms after deployment. The company wants to avoid overfitting the model and maintain the model's ability to generalize. Which solution will meet these requirements? 

A. Decrease the early_stopping_patience hyperparameter. 
B. Increase the mini_batch_size hyperparameter. 
C. Decrease the dropout rate.
 D. Increase the number of epochs. 

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Question # 7

A company needs an AWS solution that will automatically create versions of ML models as the models are created. Which solution will meet this requirement?

A. Amazon Elastic Container Registry (Amazon ECR) 
B. Model packages from Amazon SageMaker Marketplace 
C. Amazon SageMaker ML Lineage Tracking 
D. Amazon SageMaker Model Registry 

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Question # 8

A company launches a feature that predicts home prices. An ML engineer trained a regression model using the SageMaker AI XGBoost algorithm. The model performs well on training data but underperforms on real-world validation data. Which solution will improve the validation score with the LEAST implementation effort?

A. Create a larger training dataset with more real-world data and retrain. 
B. Increase the num_round hyperparameter. 
C. Change the eval_metric from RMSE to Error. 
D. Increase the lambda hyperparameter. 

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Question # 9

A company is planning to use Amazon Redshift ML in its primary AWS account. The source data is in an Amazon S3 bucket in a secondary account. An ML engineer needs to set up an ML pipeline in the primary account to access the S3 bucket in the secondary account. The solution must not require public IPv4 addresses. Which solution will meet these requirements?

A. Provision a Redshift cluster and Amazon SageMaker Studio in a VPC with no public access enabled in the primary account. Create a VPC peering connection between the accounts. Update the VPC route tables to remove the route to 0.0.0.0/0. 
B. Provision a Redshift cluster and Amazon SageMaker Studio in a VPC with no public access enabled in the primary account. Create an AWS Direct Connect connection and a transit gateway. Associate the VPCs from both accounts with the transit gateway. Update the VPC route tables to remove the route to 0.0.0.0/0. 
C. Provision a Redshift cluster and Amazon SageMaker Studio in a VPC in the primary account. Create an AWS Site-to-Site VPN connection with two encrypted IPsec tunnels between the accounts. Set up interface VPC endpoints for Amazon S3. 
D. Provision a Redshift cluster and Amazon SageMaker Studio in a VPC in the primary account. Create an S3 gateway endpoint. Update the S3 bucket policy to allow IAM principals from the primary account. Set up interface VPC endpoints for SageMaker and Amazon Redshift. 

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Question # 10

An ML engineer wants to re-train an XGBoost model at the end of each month. A data team prepares the training data. The training dataset is a few hundred megabytes in size. When the data is ready, the data team stores the data as a new file in an Amazon S3 bucket. The ML engineer needs a solution to automate this pipeline. The solution must register the new model version in Amazon SageMaker Model Registry within 24 hours. Which solution will meet these requirements?

A. Create an AWS Lambda function that runs one time each week to poll the S3 bucket for new files. Invoke the Lambda function asynchronously. Configure the Lambda function to start the pipeline if the function detects new data. 
B. Create an Amazon CloudWatch rule that runs on a schedule to start the pipeline every 30 days.
 C. Create an S3 Lifecycle rule to start the pipeline every time a new object is uploaded to the S3 bucket. 
D. Create an Amazon EventBridge rule to start an AWS Step Functions TrainingStep every time a new object is uploaded to the S3 bucket. 

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