MLA-C01 Exam Dumps : AWS Certified Machine Learning Engineer - Associate

The primary goal is to produce the most complete dataset while handling missing values and extreme outliers appropriately. Dropping samples (Option A) or columns (Option D) would reduce data completeness and potentially remove valuable information, which contradicts the requirement.

Imputation is therefore the correct approach. Between mean and median imputation, AWS ML best practices recommend using the median when features contain outliers. The mean is sensitive to extreme values and can be skewed significantly, leading to imputed values that are not representative of the typical data distribution. In contrast, the median is robust to outliers, making it a better statistical estimator for central tendency in such datasets.

Amazon SageMaker Data Wrangler supports median imputation as a built-in transformation, enabling ML engineers to handle missing values consistently across large tabular datasets without custom code. This approach preserves all rows and columns while minimizing distortion caused by extreme values, which is particularly important for neural networks that are sensitive to input distributions.

Therefore, imputing missing values with the median value provides the most complete and statistically appropriate dataset for training.

Amazon Q Business allows configuring blocked phrases to exclude specific terms or phrases from the responses. By adding the competitor's name as a blocked phrase, the company can ensure that it will not appear in the API responses, meeting the requirement efficiently with minimal configuration.

The core requirement is to identify and remove sensitive data from content stored in Amazon S3 with minimal operational overhead. Amazon Macie is purpose-built to automatically discover, classify, and protect sensitive data (such as PII) in Amazon S3 using machine learning. Macie continuously scans S3 objects and produces findings that identify sensitive data types and locations without requiring custom ML pipelines or infrastructure.

Once Macie identifies sensitive data, AWS Lambda can be used to automate remediation—such as redacting, masking, or deleting sensitive fields—based on Macie findings. This event-driven approach is serverless, scales automatically, and minimizes operations.

Option A increases overhead by moving the model and building custom detection logic. Option B introduces unnecessary compute orchestration (ECS/Fargate and Batch). Option D uses Amazon Comprehend for entity detection, which is not optimized for broad S3 data discovery and requires EC2 management.

Therefore, using Amazon Macie for detection and Lambda for remediation is the most efficient and AWS-recommended solution.