Expert Answers to Amazon Web Services Exam MLA-C01 Questions

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AWS Certified Associate AWS Certified Machine Learning Engineer - Associate

AWS Certified Machine Learning Engineer - Associate

Last Update Apr 30, 2026
Total Questions : 230

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Questions 2

Case Study

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?

Options:

Create a separate Amazon Elastic Container Registry (Amazon ECR) repository for each model.

Use Amazon Elastic Container Registry (Amazon ECR) and unique tags for each model version.

Use the SageMaker Model Registry and model groups to catalog the models.

Use the SageMaker Model Registry and unique tags for each model version.

Discussion 0

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Questions 3

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?

Options:

Use the Amazon SageMaker AI XGBoost algorithm. Set max_depth to greater than 100 to regulate tree complexity.

Use the Amazon SageMaker AI k-means clustering algorithm. Set k to determine the number of clusters.

Use the Amazon SageMaker AI DeepAR algorithm. Set epochs to the number of training iterations.

Use the Amazon SageMaker AI Random Cut Forest (RCF) algorithm. Set num_trees to greater than 100.

Discussion 0

Questions 4

A company is planning to create several ML prediction models. The training data is stored in Amazon S3. The entire dataset is more than 5 ТВ in size and consists of CSV, JSON, Apache Parquet, and simple text files.

The data must be processed in several consecutive steps. The steps include complex manipulations that can take hours to finish running. Some of the processing involves natural language processing (NLP) transformations. The entire process must be automated.

Which solution will meet these requirements?

Options:

Process data at each step by using Amazon SageMaker Data Wrangler. Automate the process by using Data Wrangler jobs.

Use Amazon SageMaker notebooks for each data processing step. Automate the process by using Amazon EventBridge.

Process data at each step by using AWS Lambda functions. Automate the process by using AWS Step Functions and Amazon EventBridge.

Use Amazon SageMaker Pipelines to create a pipeline of data processing steps. Automate the pipeline by using Amazon EventBridge.

Discussion 0

Questions 5

An ML engineer is working on an ML model to predict the prices of similarly sized homes. The model will base predictions on several features The ML engineer will use the following feature engineering techniques to estimate the prices of the homes:

• Feature splitting

• Logarithmic transformation

• One-hot encoding

• Standardized distribution

Select the correct feature engineering techniques for the following list of features. Each feature engineering technique should be selected one time or not at all (Select three.)

Questions 5