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Amazon MLA-C01 시험요강:

주제소개
주제 1
  • ML Solution Monitoring, Maintenance, and Security: This section of the exam measures skills of Fraud Examiners and assesses the ability to monitor machine learning models, manage infrastructure costs, and apply security best practices. It includes setting up model performance tracking, detecting drift, and using AWS tools for logging and alerts. Candidates are also tested on configuring access controls, auditing environments, and maintaining compliance in sensitive data environments like financial fraud detection.
주제 2
  • Deployment and Orchestration of ML Workflows: This section of the exam measures skills of Forensic Data Analysts and focuses on deploying machine learning models into production environments. It covers choosing the right infrastructure, managing containers, automating scaling, and orchestrating workflows through CI
  • CD pipelines. Candidates must be able to build and script environments that support consistent deployment and efficient retraining cycles in real-world fraud detection systems.
주제 3
  • Data Preparation for Machine Learning (ML): This section of the exam measures skills of Forensic Data Analysts and covers collecting, storing, and preparing data for machine learning. It focuses on understanding different data formats, ingestion methods, and AWS tools used to process and transform data. Candidates are expected to clean and engineer features, ensure data integrity, and address biases or compliance issues, which are crucial for preparing high-quality datasets in fraud analysis contexts.
주제 4
  • ML Model Development: This section of the exam measures skills of Fraud Examiners and covers choosing and training machine learning models to solve business problems such as fraud detection. It includes selecting algorithms, using built-in or custom models, tuning parameters, and evaluating performance with standard metrics. The domain emphasizes refining models to avoid overfitting and maintaining version control to support ongoing investigations and audit trails.

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최신 AWS Certified Associate MLA-C01 무료샘플문제 (Q23-Q28):

질문 # 23
An ML engineer has deployed an Amazon SageMaker model to a serverless endpoint in production. The model is invoked by the InvokeEndpoint API operation.
The model's latency in production is higher than the baseline latency in the test environment. The ML engineer thinks that the increase in latency is because of model startup time.
What should the ML engineer do to confirm or deny this hypothesis?

정답：B


질문 # 24
Case Study
A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central model registry, model deployment, and model monitoring.
The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3.
The company must implement a manual approval-based workflow to ensure that only approved models can be deployed to production endpoints.
Which solution will meet this requirement?

정답：B


질문 # 25
An ML engineer at a credit card company built and deployed an ML model by using Amazon SageMaker AI.
The model was trained on transaction data that contained very few fraudulent transactions. After deployment, the model is underperforming.
What should the ML engineer do to improve the model's performance?

정답：A

설명：
This is a classic class imbalance problem, where fraudulent transactions (minority class) are severely underrepresented. AWS documentation for SageMaker Data Wrangler recommends SMOTE (Synthetic Minority Oversampling Technique) as an effective approach for improving model performance in such scenarios.
SMOTE generates synthetic minority samples by interpolating between existing minority class examples.
This improves the model's ability to learn decision boundaries without simply duplicating data, which can cause overfitting.
Random undersampling removes valuable majority class data, reducing overall model robustness. Random oversampling duplicates data and increases overfitting risk. Changing algorithms does not address the root cause.
AWS best practices highlight SMOTE as the preferred technique for fraud detection and other highly imbalanced datasets.
Therefore, Option C is the correct and AWS-verified answer.


질문 # 26
A company needs to deploy a custom-trained classification ML model on AWS. The model must make near real-time predictions with low latency and must handle variable request volumes.
Which solution will meet these requirements?

정답：D

설명：
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.


질문 # 27
Case Study
A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central model registry, model deployment, and model monitoring.
The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3.
The company needs to use the central model registry to manage different versions of models in the application.
Which action will meet this requirement with the LEAST operational overhead?

정답：A

설명：
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


질문 # 28
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