AIF-C01 Exam Results

The verified selections are Few-shot prompting, Chain-of-thought prompting, and Zero-shot prompting. The first description matches few-shot prompting because AWS describes few-shot prompting as a technique that includes example outputs or demonstrations in the initial prompt so the model can understand the expected pattern before generating a response. The phrase “provide a small number of examples” is the key indicator. A few-shot prompt gives the model limited examples of the desired task, format, or reasoning style, and the model uses those examples as context for the next output.

The second description matches chain-of-thought prompting. AWS describes chain-of-thought prompting as a technique that helps a model solve a problem by following a series of intermediate reasoning steps before reaching the final answer. The wording “break down the step-by-step process” directly points to chain-of-thought prompting. This method is commonly associated with reasoning, arithmetic, logic, planning, and multi-step problem solving because the prompt encourages the model to work through intermediate steps rather than immediately outputting a final answer.

The third description matches zero-shot prompting because AWS describes zero-shot prompting as asking the model to perform a task without providing examples in the prompt. The model relies only on the instruction and its pre-trained knowledge. The phrase “without providing examples” is the decisive clue.

Role-based prompting is not used here because none of the descriptions asks the model to act as a specific persona, job role, or domain expert, such as “Act as a financial analyst” or “You are a security engineer.” Single-shot prompting is also not used because the first description says a “small number of examples,” which indicates few-shot prompting, not exactly one example. Therefore, the three correct hotspot mappings are Few-shot prompting, Chain-of-thought prompting, and Zero-shot prompting.

Explainability refers to the ability of an AI system to make its decision-making process clear and understandable to humans.

A is correct:

" Explainability is crucial for human-centered AI, especially in healthcare, to ensure that doctors and patients understand the rationale behind AI-driven recommendations. "

(Reference: AWS Responsible AI)

B relates to protecting data, not explanations.

C is about treating groups equally.

D is about managing data lifecycle, not providing rationales.

Transfer learning is the correct technique because it reuses knowledge already learned by a pre-trained model and adapts that model to a new, related task through additional training.

AWS SageMaker AI documentation explicitly states: “Fine-tuning trains a pretrained model on a new dataset without training from scratch.” AWS further explains that this process is “also known as transfer learning.”

This directly matches the scenario. The existing foundation model has already learned useful general representations and task capabilities. The company does not need to discard those learned parameters and build a second model from the beginning. Instead, it supplies a smaller dataset associated with the related downstream task and adjusts the existing model ' s parameters through fine-tuning.

Transfer learning can significantly reduce the amount of task-specific data, compute resources, and training time required compared with full pre-training. AWS notes that fine-tuning pre-trained foundation models provides an economical way to use their broad capabilities while specializing them for business needs, domain-specific terminology, or particular tasks.

Hyperparameter tuning , option A, searches for effective values of configuration parameters such as learning rate, batch size, or other training settings. It can improve a fine-tuning job, but it does not describe the fundamental technique of transferring learned knowledge to a related task.

Pre-training , option B, refers to the initial large-scale learning stage, typically performed using enormous datasets and substantial computational resources. Repeating full pre-training would defeat the principal advantage of starting with an existing FM.

Reinforcement learning , option D, trains behavior through feedback or reward signals. Techniques such as reinforcement learning from human feedback can align model behavior, but reinforcement learning is not the general method described when adapting an existing pretrained model to a related supervised downstream task.

Therefore, because the organization wants to retain learned representations and adapt the existing model efficiently to a related task, the correct answer is C. Transfer learning .

Unsupervised learning involves training a model on unlabeled data, letting it find patterns or groupings on its own, without explicit outputs provided. Clustering is a primary unsupervised learning technique.

Option A is correct: Grouping customers based on purchase history (without predefined categories) is clustering, a classic unsupervised task.

B and C are supervised learning (classification and regression, respectively).

D is reinforcement learning, not unsupervised learning.

" Unsupervised learning involves training on data without labels and is often used for clustering or dimensionality reduction. "

(Reference: AWS Certified AI Practitioner Official Study Guide, AWS ML Concepts)