Amazon Web Services Updated MLA-C01 Exam Questions and Answers by agatha

AWS Glue DataBrew is designed specifically for no-code and low-code data preparation, making it the fastest and lowest-overhead solution for resolving common data quality issues. DataBrew provides built-in transformations for deduplication, missing value imputation, and outlier handling while preserving data integrity.

Option A uses standard statistical techniques such as median imputation and value normalization, which are widely accepted and maintain the distribution of the data. DataBrew jobs are fully managed and do not require infrastructure setup or maintenance.

Option B deletes records, which can lead to data loss and does not preserve integrity. Option C introduces unnecessary infrastructure complexity and uses poor data imputation practices. Option D provides advanced capabilities but requires more configuration and ML expertise, increasing operational overhead.

AWS documentation clearly positions DataBrew as the preferred solution for quick, reliable data cleaning with minimal effort.

Therefore, Option A is the correct answer.

AWS Glue Data Quality provides managed, declarative data quality checks with minimal configuration. Combined with Glue crawlers, it enables automatic schema discovery and quality validation without custom code.

Option A uses native AWS services designed for this exact purpose, minimizing operational overhead. Options B and C require custom code and maintenance. Option D is not designed for data validation.

AWS documentation explicitly recommends Glue Data Quality rules for scalable, automated data quality checks in analytics pipelines.

Therefore, Option A is the correct and AWS-aligned solution.

A sudden drop in model performance after a long period of stability is a classic indicator of data drift. According to AWS ML documentation, production data distribution drift occurs when the statistical properties of incoming data change over time compared to the training dataset.

This drift can be caused by changes in user behavior, market conditions, seasonal trends, or upstream data pipelines. When drift occurs, the model’s learned patterns no longer align with real-world data, leading to degraded accuracy and other performance metrics.

Lack of training data (Option A) and overfitting (Option D) are issues that typically manifest during initial training, not after months of stable production performance. Compute constraints (Option C) may affect latency but do not usually cause sustained accuracy degradation.

AWS recommends using SageMaker Model Monitor to detect such drift and trigger retraining workflows.

Therefore, drift in the production data distribution is the most likely cause of the observed performance degradation.