Helping Hand Questions for AIF-C01

The correct answer is regression. In machine learning, regression models are designed to predict continuous numerical values based on input features. Common use cases include predicting house prices, sales forecasting, temperature trends, or medical risk scores. According to AWS SageMaker documentation, regression tasks fall under supervised learning where the output is a real-valued number rather than a class label. For instance, linear regression is one of the most commonly used models for predicting a single continuous output. By contrast, diffusion models are typically used in generative image tasks, transformers are architectures (not specific to numeric output), and multi-modal models process various data types like text, images, and audio. Only regression models are purpose-built for making precise numeric predictions, which aligns with AWS best practices when the output is a quantity, not a category.

Referenced AWS AI/ML Documents and Study Guides:

AWS Machine Learning Specialty Guide – Regression Models

Amazon SageMaker Built-in Algorithms – Linear Learner (Regression and Classification)

The company is developing an ML model to predict customer churn, a binary classification task (churn or no churn). The F1 score is an evaluation metric that balances precision and recall, making it suitable for assessing the performance of binary classification models, especially when dealing with imbalanced datasets, which is common in churn prediction.

Exact Extract from AWS AI Documents:

From the Amazon SageMaker Developer Guide:

"The F1 score is a metric for evaluating binary classification models, combining precision and recall into a single value. It is particularly useful for tasks like churn prediction, where class imbalance may exist, ensuring the model performs well on both positive and negative classes."

(Source: Amazon SageMaker Developer Guide, Model Evaluation Metrics)

Detailed Explanation:

Option A: F1 scoreThis is the correct answer. The F1 score is ideal for binary classification tasks like churn prediction, as it measures the model’s ability to correctly identify both churners and non-churners.

Option B: Mean squared error (MSE)MSE is used for regression tasks to measure the average squared difference between predicted and actual values, not for binary classification.

Option C: R-squaredR-squared is a metric for regression models, indicating how well the model explains the variability of the target variable. It is not applicable to classification tasks.

Option D: Time used to train the modelTraining time is not an evaluation metric for model performance; it measures the duration of training, not the model’s accuracy or effectiveness.

Data augmentation for imbalanced classes is the correct technique to address bias in input data affecting image generation.

Data Augmentation for Imbalanced Classes:

Involves generating new data samples by modifying existing ones, such as flipping, rotating, or cropping images, to balance the representation of different classes.

Helps mitigate bias by ensuring that the training data is more representative of diverse characteristics and scenarios.

Why Option A is Correct:

Balances Data Distribution: Addresses class imbalance by augmenting underrepresented classes, which reduces bias in the model.

Improves Model Fairness: Ensures that the model is exposed to a more diverse set of training examples, promoting fairness in image generation.

Why Other Options are Incorrect:

B. Model monitoring for class distribution: Helps identify bias but does not actively correct it.

C. Retrieval Augmented Generation (RAG): Involves combining retrieval and generation but is unrelated to mitigating bias in image generation.

D. Watermark detection for images: Detects watermarks in images, not a technique for addressing bias.

Amazon SageMaker Canvas is a visual, no-code tool for building and deploying ML models. According to the official SageMaker Canvas documentation:

“SageMaker Canvas provides a visual point-and-click interface that allows business analysts to generate accurate ML predictions without writing any code.”