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

This dataset shows severe class imbalance, with only 2% of records representing patients with the disease. AWS ML best practices recommend correcting imbalance only in the training dataset, while keeping validation and test sets representative of real-world distributions.

Synthetic Minority Oversampling Technique (SMOTE) generates synthetic samples of the minority class by interpolating between existing minority examples. This improves the model’s ability to learn disease-related patterns without discarding data.

PCA is a dimensionality reduction method, not an oversampling technique. Oversampling the majority class worsens imbalance. Altering the test dataset would invalidate evaluation results.

Therefore, applying SMOTE to the training dataset is the correct approach.

A lift-and-shift migration requires minimal architectural changes. The company already uses Kubernetes and standalone Docker images, which are fully compatible with Amazon Elastic Kubernetes Service.

The lowest-overhead approach is to push the existing Docker images to Amazon Elastic Container Registry and deploy them directly to the EKS cluster using standard Kubernetes manifests or Helm charts. This approach preserves the existing ML workflows, training logic, and inference services without refactoring.

Option A requires redesigning services with Kubeflow, significantly increasing complexity. Option C changes both data storage and training methodology. Option D requires retraining models and changing operational workflows.

Therefore, uploading Docker images to Amazon ECR and deploying them to EKS is the correct lift-and-shift solution.

SageMaker Asynchronous Inference is designed for processing large payloads, such as images up to 50 MB, and can handle requests that do not require an immediate response.

It scales automatically based on the demand, minimizing operational overhead while ensuring cost-efficiency.

A script can be used to send inference requests for each image, and the results can be retrieved asynchronously. This approach is ideal for accommodating varying levels of traffic with minimal manual intervention.

The described behavior—strong performance on training data but poor performance in production—is a classic sign of overfitting. AWS documentation for Amazon SageMaker Object2Vec highlights early stopping as a key regularization technique to prevent models from learning noise in the training data.

The early_stopping_patience hyperparameter controls how many additional epochs the training job will run after the validation loss stops improving. Decreasing this value causes training to stop earlier, reducing the chance that the model overfits to the training dataset.

Option B increases batch size, which may improve training efficiency but does not directly address overfitting. Option C decreases the dropout rate, which actually increases overfitting risk, since dropout is a regularization mechanism. Option D increases epochs, which further worsens overfitting.

AWS best practices emphasize early stopping combined with validation metrics as one of the most effective ways to maintain generalization performance in neural embedding models such as Object2Vec.

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

ï‚· Problem Description:

The training dataset has a class imbalance, meaning one class (e.g., fraudulent transactions) has fewer samples compared to the majority class (e.g., non-fraudulent transactions). This imbalance affects the model’s ability to learn patterns from the minority class.

ï‚· Why SageMaker Data Wrangler?

SageMaker Data Wrangler provides a built-in operation called " Balance Data, " which includes oversampling and undersampling techniques to address class imbalances.

Oversampling the minority class replicates samples of the minority class, ensuring the algorithm receives balanced inputs without significant additional operational overhead.

ï‚· Steps to Implement:

Import the dataset into SageMaker Data Wrangler.

Apply the " Balance Data " operation and configure it to oversample the minority class.

Export the balanced dataset for training.

ï‚· Advantages:

Ease of Use: Minimal configuration is required.

Integrated Workflow: Works seamlessly with the SageMaker ecosystem for preprocessing and model training.

Time Efficiency: Reduces manual effort compared to external tools or scripts.

Step 1: Use AWS Glue crawlers to infer the schemas and available columns.

Step 2: Use AWS Glue DataBrew for data cleaning and feature engineering.

Step 3: Store the resulting data back in Amazon S3.

Step 1: Use AWS Glue Crawlers to Infer Schemas and Available Columns

Why? The data is stored in .csv files with unlabeled columns, and Glue Crawlers can scan the raw data in Amazon S3 to automatically infer the schema, including available columns, data types, and any missing or incomplete entries.

How? Configure AWS Glue Crawlers to point to the S3 bucket containing the .csv files, and run the crawler to extract metadata. The crawler creates a schema in the AWS Glue Data Catalog, which can then be used for subsequent transformations.

Step 2: Use AWS Glue DataBrew for Data Cleaning and Feature Engineering

Why? Glue DataBrew is a visual data preparation tool that allows for comprehensive cleaning and transformation of data. It supports imputation of missing values, renaming columns, feature engineering, and more without requiring extensive coding.

How? Use Glue DataBrew to connect to the inferred schema from Step 1 and perform data cleaning and feature engineering tasks like filling in missing rows/columns, renaming unlabeled columns, and creating derived features.

Step 3: Store the Resulting Data Back in Amazon S3

Why? After cleaning and preparing the data, it needs to be saved back to Amazon S3 so that it can be used for training machine learning models.

How? Configure Glue DataBrew to export the cleaned data to a specific S3 bucket location. This ensures the processed data is readily accessible for ML workflows.

Order Summary:

Use AWS Glue crawlers to infer schemas and available columns.

Use AWS Glue DataBrew for data cleaning and feature engineering.

Store the resulting data back in Amazon S3.

This workflow ensures that the data is prepared efficiently for ML model training while leveraging AWS services for automation and scalability.

Amazon SageMaker Model Registry is a feature designed to manage machine learning (ML) models throughout their lifecycle. It allows users to catalog, version, and deploy models systematically, ensuring efficient model governance and management.

Key Features of SageMaker Model Registry:

Centralized Cataloging: Organizes models into Model Groups, each containing multiple versions.

Version Control: Maintains a history of model iterations, making it easier to track changes.

Metadata Association: Attach metadata such as training metrics and performance evaluations to models.

Approval Status Management: Allows setting statuses like PendingManualApproval or Approved to ensure only vetted models are deployed.

Seamless Deployment: Direct integration with SageMaker deployment capabilities for real-time inference or batch processing.

Implementation Steps:

Create a Model Group: Organize related models into groups to simplify management and versioning.

Register Model Versions: Each model iteration is registered as a version within a specific Model Group.

Set Approval Status: Assign approval statuses to models before deploying them to ensure quality control.

Deploy the Model: Use SageMaker endpoints for deployment once the model is approved.

Benefits:

Centralized Management: Provides a unified platform to manage models efficiently.

Streamlined Deployment: Facilitates smooth transitions from development to production.

Governance and Compliance: Supports metadata association and approval processes.

By leveraging the SageMaker Model Registry, the company can ensure organized management of models, version control, and efficient deployment workflows with minimal operational overhead.

AWS Documentation: SageMaker Model Registry

AWS Blog: Model Registry Features and Usage

This use case involves large payloads (high-resolution images), variable processing time, and a requirement to notify users upon completion. AWS documentation recommends Amazon SageMaker asynchronous inference for exactly this scenario.

Asynchronous inference allows clients to submit inference requests without waiting for a response. The request payload is stored in Amazon S3, and the inference result is written back to S3 when processing is complete. This approach is designed for workloads that cannot meet real-time latency requirements and where payload sizes exceed real-time limits.

SageMaker asynchronous inference integrates natively with Amazon SNS to send notifications when inference completes or fails, which directly satisfies the user notification requirement.

Batch transform jobs are intended for offline, scheduled processing of large datasets and do not provide built-in user notification mechanisms. Amazon EFS is not required because SageMaker natively integrates with S3 for inference input and output.

AWS explicitly documents asynchronous inference as the preferred solution for large image inference with completion notifications.

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