AWS AI Practitioner (AIF-C01) · 20% practice allocation

AWS AIF-C01: Fundamentals of AI and ML Practice Questions

5 free sample questions from the Fundamentals of AI and ML domain, with answers and explanations. The full AIF-C01 bank has 60 questions across 5 domains. Questions are scenario-based and written from the official AWS exam guide.

1.

An algorithm groups customers into segments based on similarities in their behavior, with no predefined labels provided. Which learning type is this?

  • A. Reinforcement learning
  • B. Transfer learning
  • C. Supervised learning
  • D. Unsupervised learning✓ Correct
Explanation

Discovering groups or structure in unlabeled data is unsupervised learning (e.g., clustering). Supervised learning (C) requires labels. Reinforcement learning (A) uses rewards. Transfer learning (B) reuses a pretrained model for a new task — not a match for unlabeled segmentation.

2.

A model learns from a dataset where each example is labeled with the correct answer (e.g., emails labeled spam/not-spam). What type of machine learning is this?

  • A. Generative pre-training only
  • B. Reinforcement learning
  • C. Supervised learning✓ Correct
  • D. Unsupervised learning
Explanation

Learning from labeled examples is supervised learning. Unsupervised learning (D) finds structure in unlabeled data. Reinforcement learning (B) learns from rewards through interaction. Option A is not a learning paradigm in this taxonomy.

3.

In a supervised dataset, which term refers to the OUTPUT the model is trained to predict?

  • A. The hyperparameter
  • B. The embedding
  • C. The label (target)✓ Correct
  • D. The feature
Explanation

The label (or target) is the known output the model learns to predict; features are the inputs. Hyperparameters (A) are training settings, and embeddings (B) are numeric vector representations of data — neither is the prediction target.

4.

A company runs predictions on a large accumulated dataset overnight, where immediate per-record responses are not required. Which type of inferencing is MOST appropriate?

  • A. Real-time (online) inference
  • B. Batch inference✓ Correct
  • C. Reinforcement learning
  • D. Continued pre-training
Explanation

Processing a large dataset on a schedule without needing instant responses is batch inference. Real-time inference (A) is for low-latency, per-request predictions (e.g., a live app). Reinforcement learning (C) and continued pre-training (D) are training-side concepts, not inference modes.

5.

An e-commerce site must return a personalized product recommendation the instant a user loads a page, with sub-second latency. Which inference type is required?

  • A. Batch inference
  • B. Offline data labeling
  • C. Real-time (online) inference✓ Correct
  • D. Unsupervised clustering only
Explanation

Immediate, per-request, low-latency predictions require real-time (online) inference. Batch inference (A) processes accumulated data on a schedule — too slow for a live page load. Options B and D are not inference-serving modes for this scenario.

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