MLA-C01 Question #25: Real Exam Question with Answer & Explanation

Question

A company uses Amazon Athena to query a dataset in Amazon S3. The dataset has a target variable that the company wants to predict. The company needs to use the dataset in a solution to determine if a model can predict the target variable. Which solution will provide this information with the LEAST development effort?

Options

• ACreate a new model by using Amazon SageMaker Autopilot. Report the model's achieved
• BImplement custom scripts to perform data pre-processing, multiple linear regression, and
• CConfigure Amazon Macie to analyze the dataset and to create a model. Report the model's
• DSelect a model from Amazon Bedrock. Tune the model with the data. Report the model's

Explanation

Amazon SageMaker Autopilot is an AutoML service that automatically handles data ingestion from S3, feature engineering, model selection, and hyperparameter tuning - requiring virtually no ML development effort. It directly answers whether a model can predict a target variable by producing trained candidates with performance metrics, making it the clear minimum-effort choice.

Why the distractors fail:

• B requires writing custom preprocessing and regression code, which is significant development effort - the opposite of "least effort."
• C Amazon Macie is a data security service that detects sensitive data (PII, credentials); it has no ML modeling capability whatsoever.
• D Amazon Bedrock serves foundation/generative AI models and is designed for language/image tasks, not supervised prediction on tabular datasets with a target variable.

Memory tip: Think of Autopilot as "AutoML on autopilot" - you point it at S3 + tell it the target column, and it does everything else. When an AWS exam question pairs S3 data + target variable prediction + least effort, Autopilot is almost always the answer. Macie = security scanning, never modeling.

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