AWS Certified AI Practitioner Practice Exams

1 of 10 Free AWS AI Practitioner Exams | Over 500 Certification Exam Questions

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AWS AI Practitioner Exam Facts

  • 50 scored questions plus 15 unscored questions
  • Question include multiple choice ordering, matching, and case study
  • Scaled score ranges from 100 to 1000
  • Minimum passing score is 700
  • No penalty for guessing on unanswered items (So guess!)

AWS AI Practitioner Exam Domains

  • Domain 1: Fundamentals of AI and ML – 20%
  • Domain 2: Fundamentals of generative AI – 24%
  • Domain 3: Applications of foundation models – 28%
  • Domain 4: Guidelines for responsible AI – 14%
  • Domain 5: Security, compliance, and governance – 14%

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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

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