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Amazon MLA-C01 Dumps PDF
AWS Certified Machine Learning Engineer - Associate- 241 Questions & Answers
- Update Date : July 16, 2026
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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.LambdaLinear10PercentEvery10MinutesB. 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.
Reviews
Practice material explained optimizing GPU utilization during model training.
I faced troubleshooting model drift scenarios using Clarify and Model Monitor.
The exam included monitoring deployed models with SageMaker Model Monitor.
Practice sets clarified model evaluation metrics such as F1-score, precision, and recall.
Practice sets clarified online vs offline evaluation for ML models.
I had scenarios on integrating ML models with API Gateway and Lambda.
I had to troubleshoot failed SageMaker training jobs during case study scenarios.
The exam tested reinforcement learning use cases within SageMaker RL.
I faced scenarios on customizing pretrained models with transfer learning.
I faced knowledge checks on data imbalance techniques such as SMOTE and class weights.
Practice sets explained real-time scaling for inference endpoints.
Practice sets clarified cross-validation techniques in model training.
What career benefits can professionals expect after completing MLA-C01 certification?
I faced scenarios on preparing embeddings for recommendation systems.
I faced questions on automating ML workflows with SageMaker Pipelines.
I faced scenarios on deploying models to SageMaker endpoints for real-time inference.
I had to answer questions about handling bias and fairness in ML pipelines.
I had to handle large dataset ingestion with Kinesis Data Streams.
I had to optimize training costs using managed spot training in SageMaker.
The exam included troubleshooting feature store integration with models.
Practice material explained tuning embedding layers for text models.
I faced questions on securing ML data with encryption at rest and in transit.
The exam tested SageMaker Debugger for analyzing training failures.
Practice material explained distributed training strategies for deep learning models.
The exam included federated learning principles for distributed ML.
The exam tested advanced metrics like AUC for imbalanced datasets.
The exam tested explainability tools like SHAP and SageMaker Clarify.
The exam included designing ML pipelines with CI/CD integration.
The exam tested data augmentation strategies for image datasets.
Practice questions covered image classification pipelines with SageMaker.