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100+ Free AWS AI Practitioner Practice Questions

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Which AWS service detects online fraud in real time using ML, specifically designed for payment fraud, account takeover, and fake accounts?

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2026 Statistics

Key Facts: AWS AI Practitioner Exam

65

Total Questions

AWS certification overview

50

Scored Questions

AWS exam guide

700

Passing Score

AWS exam guide

90 min

Time Limit

AWS certification overview

$100

Exam Fee

AWS certification overview

28%

Largest Domain

AWS exam guide

AWS Certified AI Practitioner is a foundational AWS certification for AI literacy. The current AIF-C01 overview lists 65 questions, 90 minutes, $100 USD cost, Pearson VUE testing center or online proctored delivery, and 3-year validity; the official exam guide lists 50 scored questions, 15 unscored questions, a 700 scaled passing score, and five domains: AI/ML foundations 20%, GenAI foundations 24%, foundation model applications 28%, responsible AI 14%, and security/compliance/governance 14%.

Sample AWS AI Practitioner Practice Questions

Try these sample questions to test your AWS AI Practitioner exam readiness. Each question includes a detailed explanation. Start the interactive quiz above for the full 100+ question experience with AI tutoring.

1Which AWS service is a fully managed platform for building, training, and deploying machine learning models?
A.Amazon Comprehend
B.Amazon SageMaker
C.Amazon Rekognition
D.AWS Lambda
Explanation: Amazon SageMaker is AWS's 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. Comprehend and Rekognition are purpose-built AI services for specific tasks (NLP and image analysis), not general ML platforms. Lambda is a serverless compute service. Exam tip: SageMaker is the go-to answer for questions about the full ML workflow on AWS.
2What is the primary difference between supervised and unsupervised learning?
A.Supervised learning requires more computational power
B.Supervised learning uses labeled data; unsupervised learning discovers patterns in unlabeled data
C.Unsupervised learning always produces better results
D.Supervised learning can only be used for classification tasks
Explanation: Supervised learning trains models on labeled data where the correct answers are known, making it suitable for classification and regression. Unsupervised learning finds hidden patterns in unlabeled data, such as clustering and anomaly detection. Neither approach is inherently better — the choice depends on the problem and available data. Supervised learning applies to both classification and regression. Exam tip: If the question mentions 'labeled data' or 'known outcomes,' think supervised learning.
3A company wants to extract text and data from scanned documents and forms. Which AWS service should they use?
A.Amazon Textract
B.Amazon Comprehend
C.Amazon Polly
D.Amazon Translate
Explanation: Amazon Textract uses ML to automatically extract text, handwriting, tables, and forms from scanned documents and images. It goes beyond simple OCR by understanding the structure of documents. Comprehend analyzes text for sentiment, entities, and language but does not extract text from images. Polly converts text to speech, and Translate converts text between languages. Exam tip: Textract handles document extraction; Comprehend handles text analysis — they are often used together.
4Which concept describes a large pre-trained model that can be adapted for various downstream tasks through fine-tuning or prompting?
A.Feature engineering model
B.Foundation model
C.Decision tree
D.Rule-based system
Explanation: Foundation models (FMs) are large AI models trained on broad datasets that can be adapted to a wide range of downstream tasks. Examples include GPT, Claude, and Amazon Titan. They leverage transfer learning to reduce the need for task-specific training data. Foundation models can be customized through fine-tuning, prompt engineering, or retrieval-augmented generation (RAG). Exam tip: Foundation models are a core concept in the AIF-C01 exam — know how they relate to generative AI and AWS Bedrock.
5What is Amazon Bedrock?
A.A database service optimized for ML workloads
B.A fully managed service that offers foundation models from leading AI companies through a single API
C.A tool for labeling training data
D.A service for deploying ML models to edge devices
Explanation: Amazon Bedrock is a fully managed service that makes foundation models from Amazon (Titan), Anthropic (Claude), Meta (Llama), Mistral, Cohere, AI21 Labs, and Stability AI available through a unified API. It allows customization through fine-tuning and RAG without managing infrastructure. Bedrock is serverless, so you pay only for what you use. Exam tip: Bedrock is the primary AWS service for accessing and customizing foundation models — expect multiple questions about it.
6A data scientist notices that a model performs well on training data but poorly on new, unseen data. What is this problem called?
A.Underfitting
B.Overfitting
C.Data drift
D.Concept drift
Explanation: Overfitting occurs when a model learns the training data too well, including its noise and outliers, causing poor generalization to new data. The model memorizes the training set rather than learning underlying patterns. Solutions include regularization, cross-validation, more training data, or simpler model architectures. Underfitting is the opposite — the model is too simple to capture patterns. Data drift refers to changes in input data distribution over time. Exam tip: High training accuracy + low test accuracy = overfitting.
7Which AWS service provides pre-built natural language processing (NLP) capabilities such as sentiment analysis, entity recognition, and language detection?
A.Amazon Lex
B.Amazon Comprehend
C.Amazon Transcribe
D.Amazon Kendra
Explanation: Amazon Comprehend provides NLP capabilities including sentiment analysis, entity recognition, key phrase extraction, language detection, and topic modeling. It works on text input and requires no ML expertise. Lex builds conversational interfaces (chatbots), Transcribe converts speech to text, and 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.
8What is Retrieval-Augmented Generation (RAG) in the context of foundation models?
A.A method to compress model size for faster inference
B.A technique that enhances model responses by retrieving relevant information from external knowledge sources
C.A way to generate synthetic training data
D.A process for converting text to images
Explanation: RAG combines a foundation model's generation capabilities with the ability to retrieve relevant information from external knowledge bases, databases, or document collections. This allows the model to provide more accurate, up-to-date, and domain-specific responses without retraining. RAG reduces hallucinations by grounding responses in actual data. On AWS, RAG is commonly implemented using Bedrock Knowledge Bases with vector databases. Exam tip: RAG is a key concept for the AIF-C01 — know how it differs from fine-tuning and when to use each.
9Which metric measures the percentage of actual positive cases that a model correctly identifies?
A.Precision
B.Recall
C.Accuracy
D.F1 Score
Explanation: Recall (also called sensitivity or true positive rate) measures the proportion of actual positives that the model correctly identifies: TP/(TP+FN). High recall is critical in scenarios where missing positive cases is costly (e.g., disease detection, fraud detection). Precision measures the proportion of predicted positives that are actually positive. Accuracy measures overall 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?'
10A marketing team wants to build a chatbot that answers customer questions about company products. They have no ML experience. Which AWS service is most appropriate?
A.Amazon SageMaker
B.Amazon Lex with Amazon Bedrock
C.Amazon EC2 with a custom chatbot framework
D.Amazon Comprehend
Explanation: Amazon Lex provides a fully managed service for building conversational interfaces (chatbots and voice bots) with natural language understanding. When combined with Amazon Bedrock, Lex can leverage foundation models for more natural, generative responses to customer questions. This requires no ML expertise. SageMaker requires ML knowledge, EC2 would require custom development, and Comprehend analyzes text but doesn't build conversational interfaces. Exam tip: Lex handles conversation flow; Bedrock provides the AI intelligence behind responses.

About the AWS AI Practitioner Exam

The AWS Certified AI Practitioner (AIF-C01) validates foundational understanding of AI, machine learning, generative AI, AWS AI tools, responsible AI, and security/governance concepts for candidates who use but do not necessarily build AI/ML solutions on AWS.

Assessment

65 total questions: 50 scored and 15 unscored; multiple choice, multiple response, ordering, matching, and case-study style items may appear depending on the current delivery form.

Time Limit

90 minutes

Passing Score

700 scaled score on a 100-1000 scale

Exam Fee

$100 USD plus applicable taxes or foreign exchange adjustments (Amazon Web Services (AWS) / Pearson VUE)

AWS AI Practitioner Exam Content Outline

20%

Fundamentals of AI and ML

AI/ML terminology, learning types, data types, use-case fit, lifecycle, metrics, and practitioner-level MLOps.

24%

Fundamentals of GenAI

Foundation models, LLMs, embeddings, tokens, prompt engineering, inference parameters, hallucination, and customization concepts.

28%

Applications of Foundation Models

Amazon Bedrock, Amazon Q, RAG, Knowledge Bases, Agents, Guardrails, model selection, evaluation, and business application patterns.

14%

Guidelines for Responsible AI

Fairness, explainability, privacy, safety, transparency, human review, content safety, and bias detection.

14%

Security, Compliance, and Governance for AI Solutions

IAM, shared responsibility, encryption, logging, monitoring, compliance artifacts, prompt injection, and data governance.

How to Pass the AWS AI Practitioner Exam

What You Need to Know

  • Passing score: 700 scaled score on a 100-1000 scale
  • Assessment: 65 total questions: 50 scored and 15 unscored; multiple choice, multiple response, ordering, matching, and case-study style items may appear depending on the current delivery form.
  • Time limit: 90 minutes
  • Exam fee: $100 USD plus applicable taxes or foreign exchange adjustments

Keys to Passing

  • Complete 500+ practice questions
  • Score 80%+ consistently before scheduling
  • Focus on highest-weighted sections
  • Use our AI tutor for tough concepts

AWS AI Practitioner Study Tips from Top Performers

1Prioritize Applications of Foundation Models because AWS weights it at 28%, the largest AIF-C01 domain.
2Learn AI service selection as business judgment: managed AI service, Amazon Bedrock foundation-model application, SageMaker path, or no-AI recommendation.
3Practice RAG, prompt engineering, guardrails, model selection, and evaluation as decision frameworks rather than memorized definitions.
4Treat responsible AI and security as operational requirements: privacy, bias, content safety, logging, IAM, encryption, and human review.
5Use AWS official practice resources and timed review before scheduling the Pearson VUE exam.

Frequently Asked Questions

What is AWS Certified AI Practitioner?

AWS Certified AI Practitioner (AIF-C01) is a foundational AWS certification for people who want to demonstrate AI, machine learning, generative AI, AWS AI tools, responsible AI, and governance literacy without needing to build or tune ML models.

How many questions are on the AWS AI Practitioner exam?

AWS lists 65 total questions on the certification overview. The exam guide explains that 50 questions affect your score and 15 unscored questions are mixed in and not identified.

What is the passing score for AIF-C01?

The official AWS exam guide reports scores on a 100-1000 scaled range and lists 700 as the minimum passing score.

What domains does AIF-C01 cover?

The current AWS exam guide lists five domains: Fundamentals of AI and ML 20%, Fundamentals of GenAI 24%, Applications of Foundation Models 28%, Guidelines for Responsible AI 14%, and Security, Compliance, and Governance for AI Solutions 14%.

Does AWS publish an AWS AI Practitioner pass rate?

No official pass-rate percentage was available in the opened AWS sources. Use readiness evidence instead: official practice resources, timed practice, scenario review, and ability to explain service-selection tradeoffs.

Do I need coding experience for the AWS AI Practitioner exam?

No coding role is required. AWS says the target candidate uses but does not necessarily build AI/ML solutions, and the exam guide places coding models, feature engineering, hyperparameter tuning, and building ML pipelines out of scope for the target candidate.

How long is AWS AI Practitioner certification valid?

AWS states that the certification is valid for 3 years. Candidates should recertify before expiration by following current AWS recertification options.