Amazon Web Services Updated AIF-C01 Exam Questions and Answers by akram

The verified answer is B. F1 score. This is a classic imbalanced classification problem. Only 5% of customers make purchases, so a model can achieve 95% accuracy by predicting “will not purchase” for nearly everyone. That looks good numerically but fails the business purpose because the model rarely identifies actual buyers. AWS documentation describes F1 as a balanced measure that takes class balance into account and defines it as the harmonic mean of precision and recall.

AWS machine learning documentation also explains that F1 is commonly used for classification tasks and that a high F1 score indicates the model performs well for both precision and recall. If precision or recall is weak, the F1 score drops, which helps expose problems that accuracy hides.

Option A is incorrect because overall accuracy is already misleading in the scenario. The model’s 95% accuracy is probably caused by the majority class: customers who do not buy. Accuracy does not show whether the model identifies the minority class, which is the actual buyer group.

Option C is incorrect because training time for each epoch measures training efficiency, not prediction quality. A model can train quickly and still fail to identify buyers.

Option D is incorrect because cost per inference request is an operational cost metric. It does not evaluate whether the model correctly identifies purchasing customers.

Because the dataset is imbalanced and the model misses the minority class, the company should use F1 score instead of accuracy.

Unsupervised learning is the correct methodology for classifying customers into tiers when the data is unlabeled, as it does not require predefined labels or outputs.

Unsupervised Learning:

This type of machine learning is used when the data has no labels or pre-defined categories. The goal is to identify patterns, clusters, or associations within the data.

In this case, the company has petabytes of unlabeled customer data and needs to classify customers into different tiers. Unsupervised learning techniques like clustering (e.g., K-Means, Hierarchical Clustering) can group similar customers based on various attributes without any prior knowledge or labels.

Why Option B is Correct:

Handling Unlabeled Data: Unsupervised learning is specifically designed to work with unlabeled data, making it ideal for the company’s need to classify customer data.

Customer Segmentation: Techniques in unsupervised learning can be used to find natural groupings within customer data, such as identifying high-value vs. low-value customers or segmenting based on purchasing behavior.

Why Other Options are Incorrect:

A. Supervised learning: Requires labeled data with input-output pairs to train the model, which is not suitable since the company ' s data is unlabeled.

C. Reinforcement learning: Focuses on training an agent to make decisions by maximizing some notion of cumulative reward, which does not align with the company ' s need for customer classification.

D. Reinforcement learning from human feedback (RLHF): Similar to reinforcement learning but involves human feedback to refine the model’s behavior; it is also not appropriate for classifying unlabeled customer data.

Model fine-tuning adapts a pre-trained model to a specific domain or task using labeled data. This is the preferred approach when you want highly customized behavior for a particular application or subject area.

(Reference: Amazon Bedrock Fine-Tuning)

Data augmentation is used to artificially increase the size of a labeled dataset, usually by transforming or generating variations of the original data. This helps improve model generalization when labeled data is limited.

(Reference: AWS Data Preparation Techniques, AWS AI Practitioner Guide)

Continued pre-training (also called domain-adaptive pre-training) further trains a foundation model on large amounts of unlabeled data from a specific domain, improving the model’s language understanding or generation for that domain.

(Reference: Amazon Bedrock Customization Options)