Free Amazon Web Services MLA-C01 Practice Exam with Questions & Answers

Tag model packages and use model package groups that include container images for training and deployment → SageMaker Model Registry

Store predefined language packages, kernels, and relevant dependencies → Amazon Elastic Container Registry (Amazon ECR)

Organize models and their images into model groups for better discoverability → SageMaker Model Registry

Pull built-in SageMaker AI images for model training → Amazon Elastic Container Registry (Amazon ECR)

The correct hotspot mapping is based on the different purposes of SageMaker Model Registry and Amazon ECR.

For model packages, model package groups, and model discoverability, the correct choice is SageMaker Model Registry. AWS documentation states that a model package group is a collection of versioned model packages, and each version can contain the model artifacts, metadata, and container information used for deployment. AWS also documents that registered models can be organized into groups to support governance, discoverability, and lifecycle management. That is why both “tag model packages and use model package groups” and “organize models and their images into model groups” map to SageMaker Model Registry.

For storing software environments and pulling training images, the correct choice is Amazon ECR. AWS documentation says SageMaker uses Docker container images, and these images contain the software stack such as frameworks, language packages, kernels, and dependencies. AWS also states that SageMaker provides prebuilt Docker images for training and inference, and these are referenced through Amazon ECR image URIs. Therefore, both “store predefined language packages, kernels, and relevant dependencies” and “pull built-in SageMaker AI images for model training” map to Amazon ECR.

To minimize communication overhead during distributed training:

1. Same VPC Subnet: Ensures low-latency communication between training instances by keeping the network traffic within a single subnet.

2. Same AWS Region and Availability Zone: Reduces network latency further because cross-AZ communication incurs additional latency and costs.

3. Data in the Same Region and AZ: Ensures that the training data is accessed with minimal latency, improving performance during training.

This configuration optimizes communication efficiency and minimizes overhead.

AWS Glue DataBrew is specifically designed to provide no-code and low-code data preparation for analytics and machine learning. It supports common file formats such as Apache Parquet and integrates directly with Amazon S3.

Using DataBrew, users can visually create recipes that apply transformations such as one-hot encoding without writing any code. Once the recipe is defined, a DataBrew job can be run to process the dataset and store the transformed output back into Amazon S3.

Options B, C, and D all require writing SQL or code, which violates the no-code requirement. AWS documentation clearly identifies DataBrew as the correct service for interactive, visual data transformation at scale.

Therefore, Option A is the correct solution.

AWS Bedrock Knowledge Bases support multiple chunking strategies to optimize retrieval quality. Hierarchical chunking is specifically designed for structured documents such as PDFs with headings, sections, and subsections.

Hierarchical chunking allows fine-grained retrieval at the subsection level while automatically preserving parent section context. This ensures that when a small portion is retrieved, the surrounding section is also provided to the foundation model for better understanding.

Fixed-size and maximum-token chunking can split content arbitrarily, breaking semantic and structural boundaries. Semantic chunking focuses on meaning but does not guarantee structured context preservation without additional logic.

AWS documentation highlights hierarchical chunking as the preferred strategy when documents are structured and contextual integrity is required.

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

One-hot encoding is the appropriate technique for transforming categorical data, such as color information, into a format suitable for input to a neural network. This technique creates a binary vector representation where each unique category (color) is represented as a separate binary column, ensuring that the model does not infer ordinal relationships between categories. This approach preserves the categorical nature of the data and avoids introducing unintended biases.

AWS recommends Amazon SageMaker Model Monitor for detecting data drift and model drift in deployed models. Model Monitor works by comparing live inference data against a baseline, which must first be created from the training dataset.

AWS documentation clearly specifies the required order:

Create a baseline using training data statistics

Create a monitoring schedule to compare incoming data against the baseline

Option A reverses this order and is therefore incorrect. Option C is incorrect because SageMaker Clarify focuses on bias and explainability, not ongoing drift detection. Option D is reactive and does not provide continuous monitoring.

Model Monitor integrates with Amazon CloudWatch, enabling automated alerts and downstream retraining workflows. This proactive approach allows companies to detect degradation early and maintain model quality.

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

AWS Secrets Manager is designed for securely storing, managing, and automatically rotating secrets, including API tokens. By configuring a Lambda function for custom rotation logic, the solution can automatically rotate the API tokens every 3 months as required. Secrets Manager simplifies secret management and integrates seamlessly with other AWS services, making it the ideal choice for this use case.

Logging the metrics to Amazon CloudWatch allows the metrics to be tracked and monitored effectively.

CloudWatch Alarms can be configured to trigger when metrics breach a predefined threshold.

The alarm can be set to notify through Amazon Simple Notification Service (SNS), which can send email messages to the configured recipients.

This is the standard and most efficient way to achieve the desired functionality.

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.

In the Amazon SageMaker Python SDK, the fit() method is used to start a training job after an estimator has been configured. AWS documentation explicitly states that once an estimator (such as PyTorch or TensorFlow) is defined with parameters like instance type, framework version, and hyperparameters, the fit() method is responsible for launching the training process.

The fit() method accepts the training data location (commonly an Amazon S3 URI) and initiates the managed training job on SageMaker infrastructure. SageMaker then provisions the required compute resources, stages the data, executes the training script, and stores model artifacts in Amazon S3.

The create_model() method is used after training to create a SageMaker model object from trained artifacts. The deploy() method deploys a trained model to an endpoint for inference. The predict() method is used only after deployment to request predictions from an endpoint.

AWS documentation clearly separates these lifecycle steps and identifies fit() as the correct method to initiate training.

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