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Practice exam · AWS

AWS AI Practitioner AIF-C01 practice exam

65
questions
90
minutes
70%
to pass the practice exam
111
questions in the bank

Short answer

The AWS AI Practitioner AIF-C01 practice exam has 65 questions in 90 minutes, with a 70% passing mark, as configured here. The fastest way to know whether you are ready is to sit a full exam on the clock and read your score by domain, not only the total.

Domains covered

  • Fundamentals of AI and ML
  • Applications of Foundation Models
  • Fundamentals of Generative AI
  • Guidelines for Responsible AI
  • Security, Compliance, and Governance for AI Solutions

Sample questions, with explanations

Three questions from the bank, open to read.

Question 1 · Fundamentals of AI and ML

A product manager lists several business problems and wants to identify which one is a typical fit for a recommendation use case rather than another ML task. Which scenario best represents a recommendation problem?

  1. Suggesting products a user is likely to enjoy based on the behavior of similar users and the user's own history ✓ (correct)
  2. Sorting incoming support tickets into a fixed set of predefined categories
  3. Detecting whether a financial transaction is fraudulent or legitimate
  4. Predicting tomorrow's exact temperature in degrees from historical weather measurements
Why this is correct

Recommending items based on a user's history and the behavior of similar users is the canonical recommendation (personalization) use case. Predicting an exact temperature is a regression problem because the target is a continuous numeric value, not a personalized item suggestion. Sorting tickets into predefined categories is a classification problem, where the output is a discrete label rather than a ranked recommendation. Detecting fraudulent versus legitimate transactions is also a classification (specifically anomaly/fraud detection) task, not a recommendation, since the goal is to assign a category, not to suggest items tailored to a user.

Question 2 · Fundamentals of AI and ML

A data team has a dataset of past customer records, each labeled with whether the customer eventually churned. They want to train a model that predicts churn for new customers. Which type of machine learning does this scenario describe?

  1. Supervised learning, because the training data includes labeled outcomes the model learns to predict ✓ (correct)
  2. Semi-supervised learning, because no labels are present in the dataset at all
  3. Unsupervised learning, because the model discovers hidden groupings without any guidance
  4. Reinforcement learning, because the model learns through trial-and-error rewards over time
Why this is correct

Each record carries a known label (churned or not), and the goal is to predict that label for new inputs, which is the defining characteristic of supervised learning. Unsupervised learning is wrong because it works on unlabeled data to find structure, but here labels exist. Reinforcement learning is wrong because there is no agent interacting with an environment to collect rewards; the model simply learns from a fixed labeled dataset. Semi-supervised learning is wrong (and self-contradictory) because the dataset is fully labeled, not unlabeled.

Question 3 · Fundamentals of AI and ML

A modeling team must decide between a classification model and a regression model for two separate problems: (1) predicting the exact dollar amount a customer will spend next month, and (2) predicting whether an email is spam or not spam. Which assignment is correct?

  1. Regression for predicting the dollar amount, and classification for spam vs. not spam ✓ (correct)
  2. Classification for both problems, since both produce a model output
  3. Regression for both problems, since both predict customer behavior
  4. Classification for predicting the dollar amount, and regression for spam vs. not spam
Why this is correct

Regression predicts a continuous numeric value, which fits the dollar-amount prediction, while classification predicts a discrete category, which fits the spam-versus-not-spam decision. Swapping them is wrong because a dollar amount is continuous (not a category) and spam/not-spam is a discrete label (not a continuous number). Using regression for both is wrong because spam detection has discrete outcomes, not a numeric range. Using classification for both is wrong because predicting an exact dollar amount is a continuous-value problem, not a categorical one.

Take the full practice exam

  • 65 questions drawn from the bank, with a 90-minute timer
  • Score by domain and a review of every question
  • An AI tutor to talk through each question you miss
  • What you miss feeds your study plan
Start the practice examStudy the path first

Independent, unofficial practice exam. AWS and the exam name are trademarks of their owners; UpSkillScore is not affiliated with them. Format and passing mark are the exam's configuration in UpSkillScore, not an official statement.

Frequently asked questions

How many questions does the AWS AI Practitioner AIF-C01 practice exam have?

It has 65 questions and a 90-minute limit. The passing mark of the practice exam is 70%.

Is this the official AWS AI Practitioner AIF-C01 exam?

No. It is an independent practice exam built to match the structure of the real one. It does not replace the official exam.

Do I need an account to take it?

You can read the structure and sample questions without an account. Taking the full timed exam, scoring by domain and the AI tutor need one.