AWS Certified AI Practitioner (AIF-C01)

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Sample AWS Certified AI Practitioner (AIF-C01) questions

A few real questions from the bank, each with the correct answer and a written explanation. Every question is free — no signup to start.

1. Fundamentals of AI and ML

   1. Which statement best captures the relationship between artificial intelligence, machine learning, and deep learning?

   • a.Artificial intelligence is a narrow branch that sits entirely inside the field of machine learning
   • b.Deep learning is the broad umbrella term that fully contains machine learning and artificial intelligence
   • c.Machine learning and deep learning are unrelated fields that each compete against artificial intelligence
   • d.Deep learning is a subset of machine learning, which is itself a subset of artificial intelligenceCorrect

   Why: AI is the broad goal of machines performing tasks that seem intelligent. Machine learning is a subset of AI in which systems learn patterns from data. Deep learning is a further subset of ML that uses multi-layer neural networks.

2. Fundamentals of AI and ML

   2. A team labels thousands of emails as 'spam' or 'not spam' and trains a model to predict the label for new emails. Which learning paradigm is this?

   • a.Reinforcement learning, because the model earns a reward signal for each email that it reads
   • b.Supervised learning, because the model learns from examples that include the correct output labelsCorrect
   • c.Transfer learning, because the model simply reuses stored weights taken from a completely unrelated source task
   • d.Unsupervised learning, because the model groups the incoming emails without guidance

   Why: Supervised learning trains on labeled examples (input plus the known correct output) so the model can predict labels for unseen inputs. Spam classification with labeled emails is a classic supervised task.

3. Fundamentals of AI and ML

   3. A retailer wants to segment customers into natural groups without any predefined categories. Which approach fits best?

   • a.Supervised learning using a regression algorithm to predict one continuous target value
   • b.Reinforcement learning using a reward function that scores each sequential action taken
   • c.Unsupervised learning using a clustering algorithm to group similar customers togetherCorrect
   • d.A rule-based expert system using fixed hand-written thresholds

   Why: Unsupervised learning finds structure in unlabeled data. Clustering groups similar records together without predefined labels, which matches customer segmentation where the groups are not known in advance.

4. Fundamentals of AI and ML

   4. In machine learning, what is the difference between a feature and a label?

   • a.A feature is the model's final prediction; a label is the raw input data that is fed to the model
   • b.A feature is an input variable used for prediction; a label is the target output being predictedCorrect
   • c.A feature is used only in deep learning, while a label is used only in classical machine learning models
   • d.A feature and a label are simply two interchangeable names for exactly the same input data column

   Why: Features are the input variables (columns) the model reads. The label is the target value the model learns to predict. In supervised training every example pairs features with a known label.

5. Fundamentals of AI and ML

   5. A model performs almost perfectly on its training data but poorly on new, unseen data. What is this called?

   • a.Convergence, where the model has finished training and reached its stable optimal parameter state
   • b.Underfitting, where the model stays far too simple to capture even the training data itself
   • c.Overfitting, where the model memorizes training noise instead of learning the general patternCorrect
   • d.Regularization, where the model is penalized for excess complexity

   Why: Overfitting occurs when a model learns the training data too closely, including its noise, and fails to generalize. A large train-versus-test performance gap is the classic symptom. Underfitting is the opposite problem.

6. Fundamentals of AI and ML

   6. Which task is an example of a regression problem rather than a classification problem?

   • a.Deciding whether an incoming card transaction is fraudulent or legitimate
   • b.Sorting written product reviews into positive, neutral, or negative sentiment buckets
   • c.Predicting the future selling price of a house as a continuous dollar amountCorrect
   • d.Identifying which one of ten animal species is in a photo

   Why: Regression predicts a continuous numeric value, such as a house price. Classification assigns inputs to discrete categories. The other options each choose among fixed classes and are therefore classification tasks.