Amazon AWS Certified Machine Learning Engineer - Associate MLA-C01 Exam Questions

Answer : D

This scenario indicates overfitting. AWS documentation for XGBoost recommends increasing the L2 regularization parameter (lambda) to reduce overfitting and improve generalization.

Increasing num_round worsens overfitting. Changing evaluation metrics does not change model behavior. Collecting more data is effective but requires significant effort.

Regularization is a low-effort, high-impact fix.

Therefore, Option D is correct.

Answer : D

Scenario: The dataset contains duplicate records that need to be detected with minimal code development.

Why FindMatches in AWS Glue?

Purpose-Built for Deduplication: The FindMatches transform in AWS Glue is specifically designed to identify duplicate records in structured or semi-structured datasets.

Machine Learning-Based: It uses ML to identify duplicates based on configurable thresholds and provides flexibility for tuning accuracy.

Low Code Overhead: Minimal development effort is required as Glue provides an interactive console for configuring and running FindMatches transforms.

Steps to Implement:

Prepare the Data: Upload the unstructured dataset to an S3 bucket and define a schema if needed.

Create a Glue Job:

Use the AWS Glue Studio to create a job and select the FindMatches transform.

Specify key attributes for deduplication.

Run and Evaluate: Execute the Glue job, and review the results for duplicates.

Resolve Duplicates: Export results to an S3 bucket or process them as needed.

AWS Glue FindMatches Documentation

FindMatches Transform Example

Answer : C

AWS strongly recommends converting large CSV datasets into columnar formats such as Apache Parquet to improve query performance. Parquet reduces I/O by reading only the required columns and applies compression, which significantly speeds up analytics workloads.

AWS Glue ETL jobs provide a fully managed, serverless way to perform this conversion with minimal operational overhead. Once converted, the Parquet files can be queried efficiently by services such as Amazon Athena, Redshift Spectrum, and SageMaker processing jobs.

Splitting CSV files does not address inefficient storage format. Dropping columns risks data loss. Amazon EMR introduces infrastructure management overhead and is unnecessary for a straightforward format conversion.

AWS documentation clearly identifies CSV-to-Parquet conversion using Glue ETL as a best practice for scalable analytics.

Therefore, Option C is the correct answer.

Answer : C

Amazon SageMaker Pipelines provides native orchestration of ML workflows with fine-grained control, DAG-based visualization, and seamless integration with SageMaker Studio. AWS documentation explicitly states that Pipelines is designed for end-to-end ML workflow automation and visualization.

SageMaker ML Lineage Tracking records relationships between datasets, models, training jobs, and endpoints, enabling full auditability and governance, which is essential for compliance.

SageMaker Experiments tracks experiment metrics but does not provide lineage-level governance. CodePipeline is a general CI/CD service and lacks ML-specific DAG visualization and lineage tracking.

AWS best practices recommend combining SageMaker Pipelines + SageMaker Studio + ML Lineage Tracking for enterprise-grade ML workflow management.

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

Answer : D

Different data types require different encoding strategies. The Feedback column contains long, unstructured text responses. According to AWS ML documentation, text data must first be converted into tokens before it can be vectorized using techniques such as embeddings or bag-of-words. Tokenization is the correct preprocessing step for textual features.

The Satisfaction Level column is categorical but has a natural ordering (Low < Medium < High). AWS best practices recommend ordinal encoding for such ordered categorical variables because it preserves the inherent ranking information.

Option A is incorrect because one-hot encoding is not suitable for free-form text and would create an unmanageable number of features. Option B has the same issue for the Feedback column. Option C incorrectly applies label encoding to text and binary encoding to a three-class ordinal variable.

Therefore, tokenization for text data and ordinal encoding for satisfaction levels is the correct solution.

Answer : A

AWS documentation identifies Amazon SageMaker Clarify as the primary service for detecting, measuring, and explaining bias in ML models, particularly across demographic and sensitive attributes such as age, gender, and location. Clarify can analyze bias before training, after training, and during inference, making it suitable for audit and compliance requirements.

SageMaker Clarify generates bias reports using established fairness metrics such as difference in positive proportions, disparate impact, and conditional demographic disparity. These reports are exportable and auditor-friendly, directly meeting the requirement to explain bias to an external party.

AWS Glue DataBrew focuses on data preparation and quality, not bias detection. Amazon QuickSight does not provide ML fairness metrics. Amazon CloudWatch captures operational metrics, not demographic bias indicators.

AWS best practices explicitly recommend SageMaker Clarify for model transparency, fairness evaluation, and regulatory reporting.

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

Answer : D

Apache Parquet is a columnar storage file format optimized for complex and large datasets. It provides efficient reading and processing by accessing only the required columns, which reduces I/O and speeds up data handling. This makes it ideal for use with Amazon SageMaker Canvas, where minimizing processing time is important for training ML models. Parquet is also compatible with S3 and widely supported in data analytics and ML workflows.