AIF-C01 Online Practice Questions

Explanation:
Accuracy is the most appropriate metric to measure the performance of an image classification model. It indicates the percentage of correctly classified images out of the total number of images. In the context of classifying plant diseases from images, accuracy will help the company determine how well the model is performing by showing how many images were correctly classified.
Option B (Correct): "Accuracy": This is the correct answer because accuracy measures the proportion of correct predictions made by the model, which is suitable for evaluating the performance of a classification model.
Option A: "R-squared score" is incorrect as it is used for regression analysis, not classification tasks.
Option C: "Root mean squared error (RMSE)" is incorrect because it is also used for regression tasks to measure prediction errors, not for classification accuracy.
Option D: "Learning rate" is incorrect as it is a hyperparameter for training, not a performance metric.
AWS AI Practitioner
Reference: Evaluating Machine Learning Models on AWS: AWS documentation emphasizes the use of appropriate metrics, like accuracy, for classification tasks.

Explanation:
With Amazon Bedrock, AWS handles infrastructure security and patching (shared responsibility model).
Customers are responsible for securing their data (encryption, IAM, policies) both in transit and at rest.
Provisioning infrastructure (D) and platform patching (A, B) are AWS responsibilities.
Reference: AWS Shared Responsibility Model

Explanation:
The evaluation stage of the generative AI model lifecycle involves testing the model to assess its performance, including accuracy, coherence, and other metrics. This stage ensures the model meets the desired quality standards before deployment.
Exact Extract from AWS AI Documents:
From the AWS AI Practitioner Learning Path:
"The evaluation phase in the machine learning lifecycle involves testing the model against validation or test datasets to measure its performance metrics, such as accuracy, precision, recall, or task-specific metrics for generative AI models."
(Source: AWS AI Practitioner Learning Path, Module on Machine Learning Lifecycle)
Detailed
Option A: Deployment Deployment involves making the model available for use in production. While monitoring occurs post-deployment, accuracy testing is performed earlier in the evaluation stage.
Option B: Data selection Data selection involves choosing and preparing data for training, not testing the model’s accuracy.
Option C: Fine-tuning Fine-tuning adjusts a pre-trained model to improve performance for a specific task, but it is not the stage where accuracy is formally tested.
Option D: Evaluation This is the correct answer. The evaluation stage is where tests are conducted to examine the model’s accuracy and other performance metrics, ensuring it meets requirements.
Reference: AWS AI Practitioner Learning Path: Module on Machine Learning Lifecycle
Amazon SageMaker Developer Guide: Model Evaluation (https://docs.aws.amazon.com/sagemaker/latest/dg/model-evaluation.html)
AWS Documentation: Generative AI Lifecycle (https://aws.amazon.com/machine-learning/)

Explanation:
Network isolation is a key security feature for SageMaker. It ensures that training and inference jobs run in a VPC and are not accessible from the internet. Per the official SageMaker documentation:
“When you enable network isolation, your model can’t make any outbound network calls. This is useful for security and regulatory compliance when working with sensitive data.”