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The Amazon Certified Machine Learning Engineer - Associate certification is a globally recognized credential that validates the skills and knowledge required to install, configure, operate, and troubleshoot small to medium-sized enterprise networks. To earn this prestigious certification, candidates must pass the Amazon MLA-C01 exam, which covers a wide range of networking topics, including network fundamentals, network access, IP connectivity, IP services, security fundamentals, and automation and programmability.

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Amazon MLA-C01 Sample Questions

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. 



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 



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. 



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. 



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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