AWS Certified Machine Learning Engineer Associate (MLA-C01)

1. A machine learning engineer needs to organize raw training datasets in Amazon S3 so that SageMaker training jobs can efficiently load only the data belonging to a specific experiment without scanning the entire bucket. Which S3 design practice best achieves this?

2. A data engineering team stores ML training files in Amazon S3 using the Parquet format. They want to ensure SageMaker training jobs can read the data as fast as possible and reduce Amazon S3 API costs. Which S3 access pattern should they implement?

3. A machine learning engineer needs to train a model to predict whether a customer will churn (yes/no) based on 50 structured features. The dataset has 500,000 rows and the team requires high accuracy with minimal tuning effort. Which SageMaker built-in algorithm is the best starting point?

4. A data science team wants to segment customers into distinct groups based on purchasing behavior without any labeled data. Which SageMaker built-in algorithm should they use?

5. A machine learning team fine-tunes a pre-trained image classification model using SageMaker for a medical imaging task. The original model was trained on ImageNet (1,000 general categories). The team has only 500 labeled medical images. To prevent catastrophic forgetting and achieve best performance, which fine-tuning strategy should the team apply?