Amazon MLA-C01 Exam Dumps

AWS recommends Amazon SageMaker Model Monitor as the native service for detecting data drift, model drift, and bias drift in deployed ML models. Model Monitor continuously compares incoming inference data against a baseline dataset captured during training.

When Model Monitor detects drift beyond configured thresholds, it can emit Amazon CloudWatch events. These events can trigger an AWS Lambda function, which is a common AWS-documented pattern for orchestrating automated workflows such as model retraining.

This Lambda function can then initiate a SageMaker Pipeline execution, starting a retraining job with updated data. This architecture aligns with AWS best practices for building automated, event-driven ML pipelines.

Option A is incorrect because AWS Glue is designed for data cataloging and ETL, not for ML-specific drift detection. Option B is unnecessary and overly complex for this use case. Option D is incorrect because Amazon QuickSight anomaly detection is intended for business intelligence analytics, not ML model monitoring.

AWS documentation explicitly positions SageMaker Model Monitor + Lambda automation as the recommended approach for continuous ML monitoring and retraining.

Therefore, Option C is the correct and AWS-verified answer.

For near real-time inference with low latency and variable traffic, AWS recommends deploying models to managed SageMaker endpoints. By enabling auto scaling, the endpoint automatically adjusts the number of instances based on request volume, ensuring consistent performance while optimizing cost.

Amazon SageMaker Endpoints abstracts infrastructure management, health checks, scaling, and model deployment. This provides lower operational overhead than managing EC2 instances manually.

Batch transform is for offline inference. API Gateway with S3 cannot serve ML models. EC2-based deployments require manual scaling and monitoring.

Therefore, a SageMaker endpoint with auto scaling is the correct solution.

Dynamic data masking allows you to control how sensitive data is presented to users at query time, without modifying or storing transformed versions of the source data. Amazon Redshift supports dynamic data masking, which can be implemented with minimal effort. This solution ensures that the data scientist can access the required information while sensitive data remains protected, meeting the requirements efficiently and with the least implementation effort.

The key requirement in this scenario is detecting customer intent without having any training data. According to AWS Machine Learning and Generative AI documentation, zero-shot learning is specifically designed for situations where labeled training data is unavailable. Zero-shot learning allows a pre-trained large language model (LLM) to perform tasks it has not been explicitly trained on by leveraging its general knowledge and language understanding.

Amazon Bedrock provides fully managed access to foundation models (FMs) and LLMs that support zero-shot and few-shot learning. By using an LLM from Amazon Bedrock, the company can directly infer customer intent from natural language inputs without building, training, or fine-tuning a custom model. This approach is ideal for conversational interfaces where rapid deployment and scalability are required.

Option A is incorrect because fine-tuning a sequence-to-sequence (seq2seq) model in Amazon SageMaker JumpStart still requires labeled training data. Since the company explicitly does not have training data, this option does not meet the requirement.

Option C is also incorrect because the Amazon Comprehend DetectEntities API is designed for named entity recognition (NER), such as detecting names, dates, locations, or monetary values. It does not perform intent detection and is not suitable for conversational AI intent classification.

Option D is partially misleading. While it is technically possible to run an LLM on Amazon EC2, this does not inherently solve the problem of intent detection without training data. Additionally, Amazon Bedrock already abstracts infrastructure management, scaling, and model hosting, making direct EC2 deployment unnecessary and less efficient.

Therefore, using an LLM from Amazon Bedrock with zero-shot learning is the most appropriate, scalable, and AWS-recommended solution for intent detection without training data.

When GPU resources are limited and the hyperparameter search space is large, AWS documentation strongly recommends Bayesian optimization combined with early stopping. Bayesian optimization uses past evaluation results to intelligently select the next set of hyperparameters to test, focusing exploration on promising regions of the search space rather than testing all combinations.

In Amazon SageMaker, Bayesian optimization is the default and recommended strategy for hyperparameter tuning jobs. It significantly reduces the number of training runs required compared to grid or random search, making it highly cost-efficient for deep learning workloads.

Early stopping further improves efficiency by terminating training jobs that show poor validation performance in early epochs. This prevents wasted GPU time on configurations that are unlikely to perform well. AWS explicitly documents early stopping as a key feature for controlling training cost and duration.

Grid search and exhaustive search are computationally expensive and impractical for large hyperparameter spaces. Manual tuning is slow, error-prone, and does not scale.

By combining Bayesian optimization with early stopping, the company can rapidly converge on high-performing hyperparameter configurations while minimizing resource usage.

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