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| Section | Weight | Objectives |
|---|---|---|
| Data Preparation for Machine Learning | 28% | - Ensure data integrity and prepare for modeling
|
| ML Model Development | 26% | - Evaluate and analyze model performance
|
| ML Solution Monitoring, Maintenance, and Security | 24% | - Monitor model and data quality
|
| Deployment and Orchestration of ML Workflows | 22% | - Automate and orchestrate ML pipelines
|
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NEW QUESTION # 55
A company's ML engineer has deployed an ML model for sentiment analysis to an Amazon SageMaker endpoint. The ML engineer needs to explain to company stakeholders how the model makes predictions.
Which solution will provide an explanation for the model's predictions?
Answer: A
Explanation:
SageMaker Clarify is designed to provide explainability for ML models. It can analyze feature importance and explain how input features influence the model's predictions. By using Clarify with the deployed SageMaker model, the ML engineer can generate insights and present them to stakeholders to explain the sentiment analysis predictions effectively.
NEW QUESTION # 56
An ML engineer trained an ML model on Amazon SageMaker to detect automobile accidents from dosed- circuit TV footage. The ML engineer used SageMaker Data Wrangler to create a training dataset of images of accidents and non-accidents.
The model performed well during training and validation. However, the model is underperforming in production because of variations in the quality of the images from various cameras.
Which solution will improve the model's accuracy in the LEAST amount of time?
Answer: D
Explanation:
The model is underperforming in production due to variations in image quality from different cameras. Using the corrupt image transform with the impulse noise option in SageMaker Data Wrangler simulates real-world noise and variations in the training dataset. This approach helps the model become more robust to inconsistencies in image quality, improving its accuracy in production without the need to collect and process new data, thereby saving time.
NEW QUESTION # 57
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?
Answer: B
Explanation:
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
NEW QUESTION # 58
An ML engineer needs to deploy a trained model that is based on a genetic algorithm. The algorithm solves a complex problem and can take several minutes to generate predictions.
When the model is deployed, the model needs to access large amounts of data to process requests. The requests can involve as much as 100 MB of data.
Which deployment solution will meet these requirements with the LEAST operational overhead?
Answer: A
Explanation:
SageMaker Asynchronous Inference is designed for models with long processing times and large payloads. It can handle input data up to 1 GB and avoids holding open connections during long inference runs, reducing operational overhead compared to managing EC2 or ECS infrastructure.
This makes it the best fit for the genetic algorithm model that takes minutes and processes large requests.
NEW QUESTION # 59
A medical company ingests streams of data from devices that monitor patients' vital signs. The company uses Amazon SageMaker and plans to prepare ML models to predict adverse events for patients. The dataset is large with thousands of features.
An ML engineer needs to run several hundred training iterations with different sets of features, different algorithms, and many potential parameters. The ML engineer must implement a solution to log the characteristics and results of each training iteration.
Which solution will meet these requirements with the LEAST implementation effort?
Answer: C
Explanation:
SageMaker Experiments is specifically designed to track and organize ML experiments, including characteristics such as features, algorithms, parameters, and results. It provides experiment tracking with minimal implementation effort, making it the best fit for logging and comparing multiple training iterations.
NEW QUESTION # 60
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