Amazon Web Services AWS Certified AI Practitioner AIF-C01 New Questions

Transfer learning is a machine learning technique that reuses knowledge learned from previous tasks to improve training efficiency and performance on new tasks. AWS documentation explains that transfer learning allows models to start from pretrained weights or representations, reducing training time and the amount of data required.

In this retail application scenario, the company wants to leverage information from prior tasks to increase learning speed, which is a defining characteristic of transfer learning. AWS emphasizes that transfer learning is especially effective when tasks are related, such as customer behavior analysis, product recommendations, or demand forecasting.

By initializing a model with learned features from an existing task, transfer learning enables faster convergence and improved accuracy compared to training from scratch. AWS frequently recommends this approach when computational efficiency and rapid iteration are important.

The other options do not satisfy the requirement. Supervised learning defines how labels are used but does not reuse prior knowledge. Hyperparameter tuning optimizes model configuration but does not leverage previous task outputs. Regularization techniques reduce overfitting but do not accelerate learning through knowledge reuse.

AWS documentation positions transfer learning as a foundational concept in modern ML workflows, particularly for retail, personalization, and natural language processing use cases. Therefore, transfer learning is the correct solution.

BLEU (Bilingual Evaluation Understudy) is a metric used to evaluate the accuracy of machine-generated translations by comparing them against reference translations. It is commonly used for translation tasks to measure how close the generated output is to professional human translations.

Option A (Correct): "Bilingual Evaluation Understudy (BLEU)": This is the correct answer because BLEU is specifically designed to evaluate the quality of translations, making it suitable for the company's use case.

Option B: "Root mean squared error (RMSE)" is incorrect because RMSE is used for regression tasks to measure prediction errors, not translation quality.

Option C: "Recall-Oriented Understudy for Gisting Evaluation (ROUGE)" is incorrect as it is used to evaluate text summarization, not translation.

Option D: "F1 score" is incorrect because it is typically used for classification tasks, not for evaluating translation accuracy.

AWS AI Practitioner References:

Model Evaluation Metrics on AWS: AWS supports various metrics like BLEU for specific use cases, such as evaluating machine translation models.

Amazon SageMaker Model Cards are specifically designed to support transparency, responsible AI, and governance throughout the machine learning lifecycle. According to AWS AI documentation, Model Cards provide a structured way to document critical information about machine learning models, including intended use cases, business context, training data assumptions, evaluation metrics, ethical considerations, and known limitations or risks. This makes Model Cards the correct feature for the requirements described.

AWS emphasizes that Model Cards help organizations meet internal governance standards and external regulatory or compliance expectations by improving model interpretability and accountability. They allow AI practitioners to clearly explain why a model was built, how it was trained, what data it relies on, and what risks or biases may exist. This aligns directly with the need to document business problems, training considerations, and usage risks.

Other options do not fully address these needs. Amazon SageMaker Model Registry focuses on versioning, approval workflows, and deployment tracking of models, but it does not provide comprehensive documentation for transparency or ethical considerations. Amazon SageMaker Model Monitor is designed to detect data drift, bias drift, and performance degradation in production, not to document assumptions or governance details. Model Dashboard offers a centralized view of deployed models and endpoints but does not provide structured, narrative documentation of model intent or risks.

AWS positions Model Cards as a core component of Responsible AI, enabling organizations to promote fairness, explainability, and trust in machine learning systems. By capturing standardized, auditable documentation, Model Cards ensure that stakeholders—including developers, auditors, and business leaders—can understand how a recommendation system should be used and where caution is required.

Fairness refers to the absence of bias in AI models. Using non-representative datasets leads to biased predictions, affecting specific demographics unfairly. Explainability, privacy, and transparency are important but not directly related to this scenario. References: AWS Responsible AI Framework.