AWS Certified Associate MLA-C01 Updated Exam

This problem describes severe class imbalance in an image classification task, where the minority class has poor predictive performance. In such cases, accuracy is a misleading metric, because a model can achieve high accuracy by predicting only the majority class. AWS ML best practices recommend using F1 score, which balances precision and recall and is more appropriate for imbalanced classification problems.

To improve performance on the minority image class, image augmentation is the preferred approach. Augmentation techniques—such as rotation, cropping, flipping, and brightness adjustment—create realistic new training examples while preserving semantic meaning. AWS documentation recommends augmentation for computer vision workloads to improve generalization without collecting new data.

SMOTE (Options C and D) is designed for tabular data, not image data, and generating synthetic pixel-level images using SMOTE is not appropriate or supported in typical computer vision pipelines.

Option A is incorrect because optimizing for accuracy does not address minority-class performance. Option D is incorrect because SMOTE is unsuitable for images.

Therefore, optimizing for F1 score and using image augmentation on the minority class is the correct solution.

AWS Glue DataBrew provides an easy-to-use interface for preparing and transforming data, including masking or obfuscating sensitive information. It offers built-in data masking features, allowing the ML engineer to handle sensitive data securely while retaining its structure and meaning. This solution is efficient and requires minimal coding, making it ideal for ensuring sensitive data is masked before model building begins.

The core requirement is to identify and remove sensitive data from content stored in Amazon S3 with minimal operational overhead. Amazon Macie is purpose-built to automatically discover, classify, and protect sensitive data (such as PII) in Amazon S3 using machine learning. Macie continuously scans S3 objects and produces findings that identify sensitive data types and locations without requiring custom ML pipelines or infrastructure.

Once Macie identifies sensitive data, AWS Lambda can be used to automate remediation—such as redacting, masking, or deleting sensitive fields—based on Macie findings. This event-driven approach is serverless, scales automatically, and minimizes operations.

Option A increases overhead by moving the model and building custom detection logic. Option B introduces unnecessary compute orchestration (ECS/Fargate and Batch). Option D uses Amazon Comprehend for entity detection, which is not optimized for broad S3 data discovery and requires EC2 management.

Therefore, using Amazon Macie for detection and Lambda for remediation is the most efficient and AWS-recommended solution.

The key requirement is semantic search over text documents that already reside in Amazon S3. AWS provides Amazon Kendra, a fully managed service specifically designed for semantic and natural language search across unstructured text.

Amazon Kendra natively supports S3 connectors, which can ingest documents directly from an S3 bucket, automatically process the text, generate embeddings, and index the content for semantic retrieval. This removes the need for the company to manage embedding generation, vector storage, or similarity search infrastructure. Queries can be expressed in natural language, making Kendra well suited for RAG-style applications.

Option A and B require building and maintaining a custom embedding pipeline and do not provide a true vector similarity search engine using SQL. SageMaker Feature Store is not intended to function as a vector database for semantic search.

Option D is incorrect because Amazon Textract is an OCR service for extracting text from scanned documents and images; it does not support semantic search.

Therefore, ingesting documents using the Amazon Kendra S3 connector and querying Kendra is the correct and AWS-recommended solution.