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

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.

This problem describes a recommendation system with millions of records, many categorical variables, and a high-dimensional sparse feature space created by one-hot encoding. AWS documentation explicitly recommends Amazon SageMaker Factorization Machines (FM) for such use cases.

Factorization Machines are designed to handle sparse datasets efficiently and to model interactions between categorical features without explicitly enumerating all feature combinations. This capability makes FM particularly well-suited for recommendation problems such as predicting user-item interactions, including destination recommendations.

With 2,000 possible airport destinations, the target space is large and sparse. One-hot encoding further increases sparsity. Factorization Machines address this challenge by learning latent factors that capture relationships between features, even when many feature combinations are rarely observed.

Option A (CatBoost) is not an Amazon SageMaker built-in algorithm and therefore does not meet the requirement. Option B (DeepAR) is a time-series forecasting algorithm, not intended for recommendation or classification problems. Option D (k-means) is an unsupervised clustering algorithm and cannot directly predict a specific destination label.

AWS documentation explicitly lists recommendation systems and click prediction as primary use cases for the SageMaker Factorization Machines algorithm.

Therefore, Option C is the correct and AWS-verified choice.

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.

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.

Amazon Comprehend is a fully managed natural language processing (NLP) service that includes a built-in sentiment analysis feature. It can quickly and efficiently analyze text data to determine whether the sentiment is positive, negative, neutral, or mixed. Using Amazon Comprehend requires minimal setup and provides accurate results without the need to train and deploy custom models, making it the fastest and most efficient solution for this task.

The large gap between training accuracy (99%) and validation accuracy (82%) is a textbook case of overfitting. The model has learned patterns that fit the training data extremely well but do not generalize to unseen data.

AWS ML best practices recommend regularization techniques to address overfitting. Dropout layers randomly deactivate neurons during training, preventing the network from relying too heavily on specific paths. L1 and L2 regularization penalize large weights, reducing model complexity and improving generalization. k-fold cross-validation provides a more robust evaluation by training and validating the model across multiple data splits.

Option A increases complexity, which would worsen overfitting. Option C mixes valid ideas (dimensionality reduction) with unrelated changes (loss function choice) and is less targeted. Option D focuses on data quality but does not directly address model variance.

Therefore, implementing dropout, regularization, and k-fold cross-validation is the correct solution.