MLA-C01 Exam Dumps : AWS Certified Machine Learning Engineer - Associate

AWS documentation identifies SageMaker Pipelines as the native CI/CD service for ML workflows. Pipelines allow engineers to define automated steps for data processing, training, evaluation, and deployment, making them ideal for retraining models when new data arrives in Amazon S3.

For version tracking and auditing, SageMaker Model Registry is explicitly designed to manage model versions, metadata, approval status, and deployment history. This satisfies regulatory and audit requirements without custom tooling.

AWS Lambda is not suitable for handling large datasets (10 GB), and CodeBuild is not ML-aware and lacks built-in model governance. Manual notebook workflows do not meet CI/CD or automation requirements.

AWS best practices strongly recommend SageMaker Pipelines combined with the Model Registry for scalable, auditable, and production-grade ML CI/CD pipelines.

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

For near real-time anomaly detection on streaming IoT data, AWS recommends Amazon Managed Service for Apache Flink. Flink is designed for stateful, low-latency stream processing and is well suited for time-series sensor analytics.

A key requirement is application resilience during deployments. Managed Flink supports system rollback, which automatically reverts to the last stable application version if a new deployment fails. This capability ensures uninterrupted processing even if application bugs are introduced, meeting the company’s reliability requirement.

Manual rollback (Option B) introduces operational risk and delays. Amazon Data Firehose (Options C and D) is a delivery service and does not support complex anomaly detection logic or application version rollback.

Therefore, using Managed Flink with system rollback enabled is the correct solution.

AWS recommends Retrieval Augmented Generation (RAG) using Amazon Bedrock knowledge bases as the most cost-effective solution for incorporating frequently updated documents into LLM-powered applications.

A Bedrock knowledge base allows the company to store documents in Amazon S3, index them using vector embeddings, and retrieve relevant context dynamically at inference time. This approach eliminates the need for retraining or fine-tuning the model when documents change.

Training or fine-tuning an LLM is expensive, time-consuming, and unnecessary for frequently changing data. Bedrock guardrails are designed for safety and policy enforcement, not knowledge integration.

AWS documentation explicitly states that knowledge bases are the preferred method for dynamic, updatable enterprise content in chat applications.

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