MLA-C01 Exam Dumps - Amazon Web Services AWS Certified Associate Questions and Answers

The primary requirement is dimensionality reduction on high-dimensional structured data to uncover hidden patterns. Principal Component Analysis (PCA) is a linear dimensionality reduction technique specifically designed for this purpose and is available as a built-in algorithm in Amazon SageMaker.

PCA transforms the original features into a smaller set of orthogonal components that preserve the maximum possible variance. This makes PCA ideal for large tabular datasets such as census data, where hundreds of correlated variables are common.

t-SNE (Option B) is mainly used for visualization in very low dimensions (2D or 3D) and does not scale well for large datasets or production analysis. k-means (Option C) is a clustering algorithm, not a dimensionality reduction method. Random Cut Forest (Option D) is used for anomaly detection.

Therefore, PCA is the correct and AWS-recommended solution.

AWS enterprise ML best practices recommend using Amazon SageMaker Model Registry to manage models throughout their lifecycle. The Model Registry is designed specifically to catalog models, track versions, and associate metadata such as training metrics, approval status, and deployment history.

Model Registry introduces the concept of model groups, which act as logical containers for different versions of the same model. Each model version within a group automatically inherits versioning, metadata tracking, and governance controls. This eliminates the operational burden of manually managing model versions and ensures consistent lineage and traceability across development, testing, and production environments.

Option A is less optimal because manually tagging model versions increases operational complexity and does not take full advantage of the built-in version management features provided by model groups.

Options C and D are incorrect because Amazon ECR is a container image repository, not a model governance or lifecycle management service. Using ECR to manage ML model versions would require custom tooling and manual metadata handling, significantly increasing operational overhead.

By using model groups within SageMaker Model Registry, the company gains a centralized, scalable, and AWS-native solution for enterprise AI governance. This approach directly aligns with AWS documentation for managing model catalogs, version control, and metadata association while minimizing manual intervention.

Amazon Q Business allows configuring blocked phrases to exclude specific terms or phrases from the responses. By adding the competitor's name as a blocked phrase, the company can ensure that it will not appear in the API responses, meeting the requirement efficiently with minimal configuration.

The primary goal is to produce the most complete dataset while handling missing values and extreme outliers appropriately. Dropping samples (Option A) or columns (Option D) would reduce data completeness and potentially remove valuable information, which contradicts the requirement.

Imputation is therefore the correct approach. Between mean and median imputation, AWS ML best practices recommend using the median when features contain outliers. The mean is sensitive to extreme values and can be skewed significantly, leading to imputed values that are not representative of the typical data distribution. In contrast, the median is robust to outliers, making it a better statistical estimator for central tendency in such datasets.

Amazon SageMaker Data Wrangler supports median imputation as a built-in transformation, enabling ML engineers to handle missing values consistently across large tabular datasets without custom code. This approach preserves all rows and columns while minimizing distortion caused by extreme values, which is particularly important for neural networks that are sensitive to input distributions.

Therefore, imputing missing values with the median value provides the most complete and statistically appropriate dataset for training.

Amazon SageMaker Serverless Inference is designed for irregular or unpredictable traffic patterns. It automatically provisions and scales compute resources based on request volume and scales down to zero when idle, making it the most cost-effective option.

Serverless inference supports payloads up to 6 MB and request durations up to 60 seconds, which comfortably meets the stated constraints. Customers are billed only for actual compute usage during inference execution, not for idle capacity.

Asynchronous inference is intended for long-running jobs (up to 1 hour) and large payloads (up to 1 GB). Real-time inference requires always-on instances, increasing cost during idle periods. Batch transform is designed for offline processing.

Therefore, serverless inference is the optimal choice.

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.

AWS best practices recommend event-driven architectures to automate ML workflows with minimal operational overhead. Amazon EventBridge natively integrates with Amazon S3 and Amazon SageMaker Pipelines, making it the most efficient solution for triggering retraining when new data arrives.

Amazon S3 automatically emits object creation events. By creating an EventBridge rule that listens for these events and targets a SageMaker Pipeline execution, the pipeline can start immediately when new training data is uploaded. This solution requires no custom code, no polling, and no infrastructure management.

Option A is incorrect because S3 lifecycle rules manage storage transitions, not workflow execution. Option B introduces custom code and periodic scanning, which increases operational complexity and cost. Option D (MWAA) is powerful but requires maintaining an Airflow environment and is unnecessary for a simple event-based trigger.

AWS documentation explicitly highlights EventBridge + SageMaker Pipelines as the recommended pattern for automated retraining workflows triggered by data arrival.

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

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.

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.

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.