How many questions are on the AWS Certified AI Practitioner exam?

The AWS Certified AI Practitioner exam has 65 questions, and the vendor's published description is: 65 questions (50 scored + 15 unscored), 90 minutes.

How long do you have to complete the AWS Certified AI Practitioner exam?

You get 90 minutes, which is about 83 seconds per question. The published format is: 65 questions (50 scored + 15 unscored), 90 minutes.

What score do you need to pass the AWS Certified AI Practitioner exam?

The passing score is 70% (700/1,000 scaled score).

What are the domains on the AWS Certified AI Practitioner exam?

There are 5 domains: ML Model Development, Data Preparation for ML, Fundamentals of AI and ML, Deployment and Operations, AI and ML Services on AWS. The official weights are ML Model Development 24%, Data Preparation for ML 12%, Fundamentals of AI and ML 20%, Deployment and Operations 20%, AI and ML Services on AWS 24%.

How should I use the domain weights when revising?

Weight by percentage, not by domain. 24% is the one where lost points cost you the most, and Certilum scores you per domain so you can see exactly where that is.

Does Certilum have practice questions for the AWS Certified AI Practitioner exam?

Yes: 65 questions in our bank for this certification, each tagged to the exam domain it belongs to, with the official domain weights applied.

Exam format

AWS Certified AI Practitioner Exam Format

AIF-C01

Everything the official exam blueprint specifies: question count, time limit, scoring and the weight each domain carries. The AWS Certified AI Practitioner (AIF-C01) exam validates foundational knowledge of artificial intelligence (AI) and machine learning (ML) services and workflows on AWS. It covers the entire ML lifecycle, including data preparation, model development, deployment, and operational monitoring. The exam also assesses understanding of core AI/ML concepts such as supervised and unsupervised learning, and practical application of AWS AI services like Amazon SageMaker, Amazon Rekognition, and Amazon Comprehend. The certification is suitable for individuals who want to demonstrate their ability to build, train, tune, and deploy ML models using AWS, as well as those who need to integrate AI capabilities into their applications. It is ideal for new cloud practitioners looking to specialize in AI/ML, developers, data scientists, and IT professionals seeking to validate their skills for role-based AI responsibilities. The exam is fully updated for 2025 and reflects current best practices in MLOps and responsible AI.

65 questions

90 min

70% to pass

5 domains

certilum

Exam facts at a glance

The published specifications for this certification.

Specification	Value
Questions	65
Time limit	90 minutes (83s per question)
Passing score	70% — 700/1,000 scaled score
Domains	5
Level	Foundation
Question bank here	65 questions
Published format	65 questions (50 scored + 15 unscored), 90 minutes
Source	Official exam specification, published by the vendor

Official domain weights

The percentage of the exam each domain carries. Weight your revision by these numbers, not by what you find hardest.

Domain	Weight	Topics	Practice Qs
ML Model Development	24%	Select appropriate algorithms for given business problems Implement training pipelines using Amazon SageMaker Perform hyperparameter tuning and model optimization +2 more	16
Data Preparation for ML	12%	Identify data types (structured, unstructured, semi-structured) Apply data preprocessing techniques (e.g., cleaning, normalization, feature engineering) Use AWS data services (e.g., Amazon S3, AWS Glue, Amazon Athena) for data preparation +1 more	8
Fundamentals of AI and ML	20%	Define basic AI and ML terms (e.g., supervised learning, unsupervised learning, reinforcement learning) Identify common AI use cases (e.g., computer vision, natural language processing, recommendation systems) Describe the ML pipeline (e.g., data collection, training, deployment, monitoring) +1 more	13
Deployment and Operations	20%	Deploy models to production using Amazon SageMaker endpoints Monitor models (e.g., data drift, concept drift) using Amazon SageMaker Model Monitor Manage model versions and automate CI/CD pipelines with MLOps +1 more	13
AI and ML Services on AWS	24%	Leverage pre-built AI services (e.g., Amazon Rekognition, Amazon Comprehend, Amazon Polly, Amazon Transcribe, Amazon Translate) Use Amazon SageMaker for custom model development Explore AWS AI services for business applications (e.g., Amazon Personalize, Amazon Forecast) +1 more	15

The heaviest domain is ML Model Development at 24%, and that is where lost points cost you the most. Certilum scores you per domain so you can see the difference between a knowledge gap and a formatting problem.

Note from the vendor record: Foundational-level pricing: USD 100 vs the USD 300 charged for Associate/Professional/Specialty exams. No AIF-C02 announced; Italian and German administrations retire after 2026-10-15. Official exam page