MLA-C01通過考試 - MLA-C01考試題庫

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Amazon MLA-C01 考試大綱：

主題簡介
主題 1
  • 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.
主題 2
  • 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.
主題 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.
主題 4
  • 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.

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最新的 AWS Certified Associate MLA-C01 免費考試真題 (Q54-Q59):

問題 #54
A company needs to create a central catalog for all the company's ML models. The models are in AWS accounts where the company developed the models initially. The models are hosted in Amazon Elastic Container Registry (Amazon ECR) repositories.
Which solution will meet these requirements?

答案：B

解題說明：
The Amazon SageMaker Model Registry is designed to manage and catalog ML models, including those hosted in Amazon ECR. By creating a model group for each model in the SageMaker Model Registry and setting up cross-account resource policies, the company can establish a central catalog in a new AWS account.
This allows all models from the initial accounts to be accessible in a unified, centralized manner for better organization, management, and governance. This solution leverages existing AWS services and ensures scalability and minimal operational overhead.


問題 #55
A company has a large collection of chat recordings from customer interactions after a product release. An ML engineer needs to create an ML model to analyze the chat data. The ML engineer needs to determine the success of the product by reviewing customer sentiments about the product.
Which action should the ML engineer take to complete the evaluation in the LEAST amount of time?

答案：A


問題 #56
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?

答案：D

解題說明：
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 intoModel 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.
References:
* AWS Documentation: SageMaker Model Registry
* AWS Blog: Model Registry Features and Usage


問題 #57
A company is using ML to predict the presence of a specific weed in a farmer's field. The company is using the Amazon SageMaker linear learner built-in algorithm with a value of multiclass_dassifier for the predictorjype hyperparameter.
What should the company do to MINIMIZE false positives?

答案：B

解題說明：
Thetarget_precisionhyperparameter in the Amazon SageMaker linear learner controls the trade-off between precision and recall for the model. Increasing the target_precision prioritizes minimizing false positives by making the model more cautious in its predictions. This approach is effective for use cases where false positives have higher consequences than false negatives.


問題 #58
A company uses a hybrid cloud environment. A model that is deployed on premises uses data in Amazon 53 to provide customers with a live conversational engine.
The model is using sensitive data. An ML engineer needs to implement a solution to identify and remove the sensitive data.
Which solution will meet these requirements with the LEAST operational overhead?

答案：B

解題說明：
Amazon Macie is a fully managed data security and privacy service that uses machine learning to discover and classify sensitive data in Amazon S3. It is purpose-built to identify sensitive data with minimal operational overhead. After identifying the sensitive data, you can use AWS Lambda functions to automate the process of removing or redacting the sensitive data, ensuring efficiency and integration with the hybrid cloud environment. This solution requires the least development effort and aligns with the requirement to handle sensitive data effectively.


問題 #59
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