Snowflake Updated MLA-C01 Exam Questions and Answers by ayrton

AWS recommends lifecycle configuration scripts as the simplest and most direct way to customize Amazon SageMaker Notebook Instances at creation time. Lifecycle configurations run automatically when a notebook instance is created or started, allowing scripts, packages, and system dependencies to be installed without manual intervention.

This approach is fully supported, requires no additional infrastructure, and integrates directly with the notebook creation workflow. The script can be reused across notebooks, ensuring consistency.

Options B, C, and D introduce unnecessary complexity, such as container management, private package repositories, or event-driven orchestration.

Therefore, lifecycle configuration scripts provide the least operational overhead solution.

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.

Amazon EC2 Spot Instances provide unused compute capacity at discounts of up to 90%. AWS documentation strongly recommends Spot Instances for interruptible workloads such as long-running ML training jobs that can resume from checkpoints.

By enabling automatic checkpointing, SageMaker saves model state periodically to Amazon S3. If the Spot Instance is interrupted, training can resume with minimal loss of progress.

Reserved Instances and Savings Plans require long-term commitment and are not as cost-effective for sporadic workloads. On-Demand Instances are the most expensive option.

Therefore, Option D is the most cost-effective solution.

Hyperband is a hyperparameter tuning strategy designed to minimize computation time by adaptively allocating resources to promising configurations and terminating underperforming ones early. It efficiently balances exploration and exploitation, making it ideal for large datasets and deep learning models where training can be computationally expensive.