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

AWS documentation strongly recommends using columnar storage formats to optimize analytical query performance on large datasets stored in Amazon S3. Apache Parquet is a columnar, compressed, and splittable file format that significantly improves query speed and reduces I/O compared to row-based formats such as CSV.

By using AWS Glue to convert CSV files into Parquet format, the company can achieve faster query execution with minimal operational overhead. Glue is fully managed, serverless, and integrates natively with S3, Amazon Athena, and Amazon Redshift Spectrum.

Option A does not improve query efficiency; splitting files still leaves the data in an inefficient row-based format. Option B may reduce data size but does not address the fundamental inefficiency of CSV for analytics. Option D introduces significant operational overhead because Amazon EMR requires cluster provisioning, scaling, and maintenance.

Therefore, converting CSV files to Apache Parquet using AWS Glue ETL is the most efficient and low-maintenance solution.

Amazon Macie is a fully managed data security and privacy service that uses machine learning to discover and classify sensitive data in Amazon S3. It is purpose-built to identify sensitive data with minimal operational overhead. After identifying the sensitive data, you can use AWS Lambda functions to automate the process of removing or redacting the sensitive data, ensuring efficiency and integration with the hybrid cloud environment. This solution requires the least development effort and aligns with the requirement to handle sensitive data effectively.

This dataset shows severe class imbalance, with only 2% of records representing patients with the disease. AWS ML best practices recommend correcting imbalance only in the training dataset, while keeping validation and test sets representative of real-world distributions.

Synthetic Minority Oversampling Technique (SMOTE) generates synthetic samples of the minority class by interpolating between existing minority examples. This improves the model’s ability to learn disease-related patterns without discarding data.

PCA is a dimensionality reduction method, not an oversampling technique. Oversampling the majority class worsens imbalance. Altering the test dataset would invalidate evaluation results.

Therefore, applying SMOTE to the training dataset is the correct approach.

Amazon MSK Serverless provides a fully managed Apache Kafka-compatible service that automatically handles provisioning, scaling, and capacity management. AWS documentation states that MSK Serverless is designed for customers who want Kafka functionality without managing infrastructure.

Option B requires capacity planning and scaling management. Option C uses proprietary technology rather than open source. Option D requires full infrastructure management.

MSK Serverless delivers near real-time streaming with minimal operational overhead while maintaining compatibility with open-source Kafka tooling.

Therefore, Option A is the correct solution.

Using custom tags allows you to organize and categorize models in the SageMaker Model Registry without altering their existing groupings or affecting the integrity of the model artifacts. Tags are a lightweight and scalable way to improve model discoverability at scale, enabling the data scientists to filter and identify models by category (e.g., computer vision, NLP, speech recognition). This approach meets the requirements efficiently without introducing structural changes to the existing model registry setup.