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

The company already has custom Python training scripts, proprietary datasets, and uses PyTorch, with significant domain-specific logic embedded in the model. The goal is to migrate these workloads to AWS with the least development effort.

According to AWS documentation, Amazon SageMaker AI script mode is explicitly designed for this scenario. Script mode allows customers to bring their existing training scripts with minimal or no code changes and run them using prebuilt SageMaker framework containers, including PyTorch. This approach eliminates the need to redesign models or rewrite training logic while still benefiting from SageMaker’s managed infrastructure, scalability, monitoring, and security.

Option A is incorrect because SageMaker built-in algorithms require adapting data formats and training logic to AWS-provided implementations, which would increase development effort and may not support proprietary domain logic.

Option C is also incorrect because building and maintaining a custom container requires additional effort for containerization, dependency management, security updates, and lifecycle maintenance—making it more complex than necessary.

Option D is not viable because purchasing models from AWS Marketplace would not support the company’s proprietary datasets or unique domain knowledge embedded in existing models.

Therefore, using SageMaker AI script mode with prebuilt PyTorch containers is the fastest, most efficient, and AWS-recommended migration path that minimizes development effort while preserving existing workflows.

Amazon SageMaker Data Wrangler provides a comprehensive solution for importing, consolidating, and preparing datasets for ML. It offers tools to handle missing values, duplicates, and outliers through its built-in cleansing and enrichment functionalities, allowing the ML engineer to efficiently prepare the data in a single environment with minimal manual effort.

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.

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.