AWS AI Practitioner Certification Exam Topics & Tests

Five Star Reviews

"Quality shines through feedback."

Cups of Coffee

"Fuel for late-night study sessions."

Lives Changed

"Learning transforms futures."

Pass Percentage

"Preparation is the key to success."

Five-Star Raves

"Mom says we're ‘pretty good,’ too."

Cups of Coffee Consumed

"Uptime: 99.9% caffeine."

Lives (and LinkedIns) Upgraded

"Side effects may include better offers."

Pass Rate Shenanigans

"We can’t take the test for you. Yet."

Questions Served

"Practice makes progress."

Practice Exams

"Every test is a step closer."

Study Hours Logged

"Consistency beats intensity."

Active Learners

"Together we grow stronger."

Certifications Earned

"Proof of knowledge, power for growth."

Communities Built

"Learning is stronger together."

Mock Exams Taken

"Simulation builds confidence."

Career Boosts

"Knowledge opens new doors."

AWS Certified AI Practitioner Exam Topics

Exam basics

Format includes 50 scored questions and 15 unscored questions used for calibration
Question types include multiple choice, multiple response, ordering, matching, and case study
Scaled scoring ranges from 100 to 1000 with a passing score of 700
The scoring model is compensatory and you pass based on your overall score
The target candidate has up to six months of exposure to AI and ML on AWS
There is no penalty for guessing so you should answer every question

Domain 1: fundamentals of AI and ML (20%)

Explain key terms such as AI, ML, deep learning, neural networks, NLP, training, inference, bias, and LLM
Differentiate supervised, unsupervised, and reinforcement learning and describe batch and real-time inference
Identify data types including labeled, unlabeled, tabular, time series, image, text, structured, and unstructured
Map use cases to techniques such as regression, classification, and clustering
Describe the ML lifecycle including data collection, EDA, preprocessing, feature engineering, training, tuning, evaluation, deployment, and monitoring
Recognize AWS services for each stage such as SageMaker, Data Wrangler, Feature Store, and Model Monitor

Domain 2: fundamentals of generative AI (24%)

Understand tokens, chunking, embeddings, vectors, prompt engineering, transformers, multimodal and diffusion models
Identify use cases including generation, summarization, chat, translation, code, agents, search, and recommendations
Describe the foundation model lifecycle from data selection and pretraining to deployment and feedback
Assess advantages and limitations including adaptability, hallucinations, interpretability, accuracy, and nondeterminism
Evaluate model selection factors including capability, performance, constraints, and compliance and define value metrics such as conversion rate and CLV
Recognize AWS options such as Amazon Bedrock, PartyRock, Amazon Q, and SageMaker JumpStart

Domain 3: applications of foundation models (28%)

Select pre-trained models by cost, modality, latency, multilingual support, size, customization, and context length
Tune inference parameters such as temperature and input or output length to control responses
Define RAG and apply Bedrock knowledge bases with suitable vector stores
Choose embedding storage with OpenSearch Service, Aurora, Neptune, DocumentDB, or RDS for PostgreSQL
Compare customization approaches including pretraining, fine-tuning, in-context learning, and RAG based augmentation
Use agents to orchestrate multi-step tasks with Agents for Amazon Bedrock

Domain 4: guidelines for responsible AI (14%)

Identify pillars of responsible AI such as bias, fairness, inclusivity, robustness, safety, and veracity
Use guardrails and tooling including Guardrails for Amazon Bedrock, SageMaker Clarify, Model Monitor, and Amazon A2I
Consider sustainability and environmental factors when selecting models
Recognize legal risks including IP issues, biased outputs, loss of trust, and hallucinations
Assess dataset characteristics for inclusivity, diversity, balance, and curation quality
Apply human-centered design principles for explainable and transparent systems

Domain 5: security, compliance, and governance (14%)

Secure AI systems with IAM roles and policies, encryption, Macie, PrivateLink, and shared responsibility awareness
Track data lineage and origins using catalogs and SageMaker Model Cards
Apply secure data engineering practices including privacy enhancing techniques, access control, and integrity checks
Account for threats such as prompt injection and ensure encryption at rest and in transit
Align to regulations and frameworks using AWS Config, Inspector, Audit Manager, Artifact, CloudTrail, and Trusted Advisor
Plan governance with policies, reviews, transparency standards, training, and the Generative AI Security Scoping Matrix

Out of scope

Developing or coding models or algorithms
Implementing data engineering or feature engineering techniques
Hyperparameter tuning or detailed model optimization
Building and deploying AI or ML pipelines and infrastructure
Mathematical or statistical analysis of models or implementing AI security protocols
Creating governance frameworks and policies for AI solutions

How to prepare

Study the official exam guide and the task statements for all five domains
Practice recognizing use cases and matching them to appropriate AWS services
Experiment with Amazon Bedrock, SageMaker JumpStart, and prompt engineering in sandboxes
Review responsible AI topics including guardrails, transparency, and evaluation methods
Do mixed-format practice with multiple choice, multiple response, ordering, matching, and case study items

AWS Certified AI Practitioner Practice Exams

AWS AI Practitioner Exam Facts

AWS AI Practitioner Exam Domains

The Trick to IT Certification Success