AIF-C01 — AWS Certified AI Practitioner
Amazon

AWS Certified AI Practitioner (AIF-C01) Practice Questions

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100 questions
June 10, 2026 updated
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Domain coverage

  • Fundamentals of AI and ML (20%)
  • Fundamentals of GenAI (24%)
  • Applications of Foundation Models (28%)
  • Guidelines for Responsible AI (14%)
  • Security, Compliance, and Governance for AI Solutions (14%)

Sample Questions (10 of 100 shown)

Q1 Fundamentals of AI and ML (20%)
Which AWS service is a fully managed platform for building, training, and deploying machine learning models?
  1. Amazon Comprehend
  2. Amazon SageMaker
  3. Amazon Rekognition
  4. AWS Lambda
✓ Correct Answer: B
Amazon SageMaker is a fully managed ML platform that provides tools for every step of the ML lifecycle — from data labeling and preparation, to model building, training, tuning, and deployment. Amazon Comprehend and Amazon Rekognition are specialized AI services for specific tasks (NLP and image analysis), not general-purpose ML platforms. AWS Lambda is a serverless compute service, not an ML platform. When a question involves a complete ML workflow on AWS, SageMaker is the go-to answer.
Q2 Fundamentals of AI and ML (20%)
What is the primary difference between supervised and unsupervised learning?
  1. Supervised learning requires more computational power
  2. Supervised learning uses labeled data; unsupervised learning discovers patterns in unlabeled data
  3. Unsupervised learning always produces better results
  4. Supervised learning can only be used for classification tasks
✓ Correct Answer: B
Supervised learning trains models on labeled data where the correct answer is known, making it suitable for classification and regression tasks. Unsupervised learning discovers hidden patterns in unlabeled data, such as clustering and anomaly detection. Neither approach is inherently superior — the choice depends on the problem and available data. Supervised learning can be used for both classification and regression. If a question mentions "labeled data" or "known results," consider supervised learning.
Q3 Fundamentals of AI and ML (20%)
A data scientist notices that a model performs well on training data but poorly on new, unseen data. What is this problem called?
  1. Underfitting
  2. Overfitting
  3. Data drift
  4. Concept drift
✓ Correct Answer: B
Overfitting occurs when a model learns the training data too well, including its noise and outliers, resulting in poor generalization to new data. The model has memorized the training set rather than learned underlying patterns. Solutions include regularization, cross-validation, adding more training data, or simplifying the model architecture. Underfitting is the opposite — the model is too simple to capture patterns. Data drift refers to input data distribution changing over time. Exam tip: high training accuracy + low test accuracy = overfitting.
Q4 Fundamentals of AI and ML (20%)
A company trained an ML model on Amazon SageMaker to predict customer credit risk. The model shows 90% recall on training data and 40% recall on unseen testing data. Which conclusion can the company draw from these results?
  1. The model is underfitting on the training data
  2. The model has insufficient training data
  3. The model has insufficient testing data
  4. The model is overfitting on the training data
✓ Correct Answer: D
The large gap between training performance (90% recall) and testing performance (40% recall) is a classic indicator of overfitting. The model has memorized patterns specific to the training data that do not generalize to new data. Underfitting would show poor performance on both training and testing data. While insufficient training or testing data could contribute to overfitting, the direct conclusion from these metrics is that overfitting has occurred.
Q5 Fundamentals of AI and ML (20%)
Which evaluation metric measures the percentage of actual positive cases that a model correctly identifies?
  1. Precision
  2. Recall
  3. Accuracy
  4. F1 Score
✓ Correct Answer: B
Recall (also known as sensitivity or true positive rate) measures the proportion of actual positive cases that the model correctly identifies: TP/(TP+FN). High recall is critical when missing positive cases is costly (e.g., disease detection, fraud detection). Precision measures the proportion of predicted positives that are actual positives. Accuracy measures the overall proportion of correct predictions. F1 Score is the harmonic mean of precision and recall. Exam tip: Recall answers "of all actual positives, how many did the model catch?"
Q6 Fundamentals of AI and ML (20%)
A company has built an image classification model to predict plant diseases from photos of plant leaves. The company wants to evaluate how many images the model classified correctly. Which evaluation metric should the company use?
  1. Accuracy
  2. Learning rate
  3. R-squared score
  4. Root mean squared error (RMSE)
✓ Correct Answer: A
Accuracy is the most appropriate metric for evaluating an image classification model's overall performance, measuring the proportion of correctly classified images out of total images. Learning rate is a training hyperparameter, not an evaluation metric. R-squared score is used for regression models to measure goodness of fit. RMSE is also a regression metric measuring prediction error magnitude. For balanced classification problems, accuracy is the simplest and most intuitive metric.
Q7 Fundamentals of AI and ML (20%)
What does an F1 score measure in the context of foundation model performance?
  1. Energy efficiency of the model's computations
  2. Model precision and recall
  3. Model speed in generating responses
  4. Financial cost of operating the model
✓ Correct Answer: B
The F1 score is the harmonic mean of precision and recall, providing a balanced measure of model performance when both false positives and false negatives matter. It ranges from 0 to 1, with 1 being perfect. F1 is especially useful for imbalanced datasets where accuracy alone can be misleading. It does not measure efficiency, speed, or cost — those are operational metrics, not performance evaluation metrics.
Q8 Fundamentals of AI and ML (20%)
Which AWS service provides pre-built natural language processing (NLP) capabilities such as sentiment analysis, entity recognition, and language detection?
  1. Amazon Lex
  2. Amazon Comprehend
  3. Amazon Transcribe
  4. Amazon Kendra
✓ Correct Answer: B
Amazon Comprehend provides NLP capabilities including sentiment analysis, entity recognition, key phrase extraction, language detection, and topic modeling. It processes text input without requiring ML expertise. Amazon Lex builds conversational interfaces (chatbots). Amazon Transcribe converts speech to text. Amazon Kendra provides intelligent enterprise search. Exam tip: Comprehend analyzes text meaning; Lex builds chatbots; Transcribe converts audio to text — don't confuse these NLP-adjacent services.
Q9 Fundamentals of AI and ML (20%)
A company wants to extract text and data from scanned documents and forms. Which AWS service should they use?
  1. Amazon Textract
  2. Amazon Comprehend
  3. Amazon Polly
  4. Amazon Translate
✓ Correct Answer: A
Amazon Textract uses ML to automatically extract text, handwriting, tables, and forms from scanned documents and images. It goes beyond simple OCR to understand document structure. Amazon Comprehend analyzes text sentiment, entities, and language but cannot extract text from images. Amazon Polly converts text to speech. Amazon Translate converts text between languages. Exam tip: Textract handles document extraction; Comprehend handles text analysis — they are often used together.
Q10 Fundamentals of AI and ML (20%)
What is the purpose of a confusion matrix in machine learning?
  1. To display model training time and epochs
  2. To visualize the performance of a classification model by showing true vs. predicted labels
  3. To show the correlation between input features
  4. To track model version history
✓ Correct Answer: B
A confusion matrix is a table that visualizes a classification model's performance by displaying true positives, true negatives, false positives, and false negatives. It provides detailed insight into where the model is making errors, enabling calculation of precision, recall, F1 score, and accuracy. It does not track training time, feature correlation, or model versioning. For binary classification, a confusion matrix is a 2×2 grid; for multi-class problems it expands accordingly.

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Frequently Asked Questions

The AWS Certified AI Practitioner exam is considered foundational level, which means it's designed for individuals with 6 months or less of experience with AI/ML technologies on AWS. The exam tests your conceptual understanding rather than hands-on technical skills. With proper preparation using our practice tests and study materials, most candidates find the exam manageable. The key is to thoroughly understand the exam domains and practice with questions that mirror the actual exam format.

Our product provides a comprehensive exam preparation experience including: hundreds of practice questions with detailed explanations for both correct and incorrect answers, exam simulation mode that mimics the actual test environment, PDF study guide that you can download for offline studying, progress tracking to monitor your improvement over time, and domain-wise practice to focus on specific areas where you need the most help. All content is regularly updated to align with the latest exam blueprint.

Yes! Our practice questions are carefully designed to closely mirror the actual exam in terms of format, difficulty level, and content coverage. We regularly review the official exam blueprint and update our questions to ensure alignment with the latest exam objectives. Our questions cover all five exam domains with appropriate weightings, helping you build confidence and identify areas for improvement before taking the real exam.

There are no mandatory prerequisites for taking the AWS Certified AI Practitioner exam. However, AWS recommends that candidates have at least 6 months of experience using AI/ML technologies on AWS, familiarity with basic cloud concepts, and understanding of AI/ML terminology. Prior experience with AWS services like Amazon SageMaker, Amazon Bedrock, or Amazon Kendra is helpful but not required. Our practice test product is designed to help you build the necessary knowledge regardless of your starting point.

For optimal preparation, we recommend starting with domain-wise practice to identify your strengths and weaknesses. Then, take full-length practice exams under timed conditions to build stamina and get comfortable with the exam format. Review the detailed explanations for each question to understand the reasoning behind correct and incorrect answers. Use the PDF study guide for offline review, and focus your final preparation on domains where you scored lowest. Most candidates prepare for 4-6 weeks before taking the exam.

Absolutely! Our product includes a downloadable PDF study guide that you can access offline anytime, anywhere. The PDF contains comprehensive coverage of all exam domains, key concepts, and practice questions with explanations. This is perfect for studying during your commute, while traveling, or in locations with limited internet access. The PDF is also printable, allowing you to create physical study materials if you prefer traditional note-taking methods.

Earning the AWS Certified AI Practitioner certification demonstrates to employers and clients that you understand AI/ML concepts and can identify appropriate AWS services for AI/ML workloads. This certification can help you advance your career in roles such as AI/ML project manager, business analyst, product manager, or sales professional specializing in AI solutions. It also serves as a stepping stone to more advanced AWS certifications like AWS Certified Machine Learning Engineer or AWS Certified Solutions Architect. In today's AI-driven job market, this certification can significantly enhance your career prospects and earning potential.

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