[100% Off] Aws Certified Ai Practitioner (Aif-C01) - Mock Tests 2025

6 Full-Length Mock Exams with 390 Questions – Pass AWS AIF-C01 AI Practitioner Certification

Added on November 6, 2025 IT & Software 13 min read

What you’ll learn

Master AI and ML concepts on AWS to pass the AWS Certified AI Practitioner (AIF-C01) exam
Learn foundation models
generative AI
and prompt engineering for real-world applications.
Practice AWS AI/ML services including SageMaker
Bedrock
and Comprehend
Understand Responsible AI practices: bias detection
fairness
explainability (XAI)
and ethics.
Gain hands-on experience with ML workflows
RAG
and vector databases in AWS.
Gain hands-on experience with ML workflows
RAG
and vector databases in AWS.
Enhance confidence and speed for exam success with realistic practice questions
Learn AWS AI security
governance
and compliance best practices for enterprise solutions
Master scenario-based AI testing
model evaluation
and performance validation
Build career-ready skills for cloud AI/ML roles and AWS AIF-C01 certification.

Requirements

Basic understanding of AI
ML
or cloud computing recommended.
Familiarity with AWS core services (S3
IAM
EC2) helpful but not mandatory.
Computer with internet access for hands-on mock exams.
Interest in AI/ML
Responsible AI
and AWS generative AI services.
Willingness to practice timed
real-world scenario-based questions.
No coding experience required; concepts explained practically.
Familiarity with ML workflow
RAG
and vector databases helpful.
Ability to follow full-length timed exams simulating real AWS exam conditions.
Ideal for cloud professionals
AI/ML enthusiasts
and developers targeting AIF-C01.
Motivation to master all exam domains
multi-format questions
and AWS AI services.

Description

Are you preparing for the AWS Certified AI Practitioner (AIF-C01) exam and looking for high-quality, exam-focused practice questions to help you pass on your first attempt? Look no further! This course offers 6 full-length mock exams with a total of 390 questions, carefully designed to simulate the real AWS exam environment and boost your confidence in AI and Generative AI services on AWS.

These AWS Certified AI Practitioner Practice Exams mirror the latest AIF-C01 exam blueprint, ensuring complete coverage of the most relevant topics — including AI/ML concepts, AWS AI technologies, data science fundamentals, responsible AI, and Generative AI services such as Amazon Bedrock, SageMaker, and Comprehend.

Each question is crafted to challenge your understanding of core AI and machine learning concepts within the AWS ecosystem, while reinforcing your practical knowledge of how AI and ML solutions are applied in real-world business scenarios.

With detailed explanations for every answer, this course not only helps you identify your weak areas but also strengthens your conceptual understanding of AI principles, AWS Foundation topics, and AI-driven decision-making. Whether you’re new to AI or an experienced professional expanding your cloud skillset, these mock exams provide everything you need to succeed.

Comprehensive Coverage

This course is perfect for professionals in IT and non-IT roles, including marketing, sales, HR, finance, project and product management, and technical teams, providing them with a foundational understanding of AI and AWS services. The practice exams cover:

AI fundamentals – Core concepts of AI, ML workflows, model evaluation, and Generative AI.
AWS AI and ML services – Hands-on understanding of SageMaker, Bedrock, Comprehend, Rekognition, Polly, Textract, and Transcribe.
Ethics and Responsible AI – Bias detection, fairness, transparency, and explainability.
Business applications – Real-world scenarios for AI adoption across industries.
Practical AI problem-solving – Scenario-based, case study, and service-based questions for applied learning.

Why This AWS Certified AI Practitioner Practice Exam Course is Unique

6 Full-Length Mock Exams: Total 390 questions, reflecting the real AIF-C01 exam structure.
100% Syllabus Coverage: Covers all AIF-C01 domains, from AI fundamentals to Generative AI, including AWS services, AI ethics, and business use cases.
Diverse Question Categories: Prepares you across multiple knowledge and application levels:
- Ordering questions: Sequence AWS AI workflows and ML processes correctly.
- Scenario questions: Apply AI and ML concepts to practical business situations.
- AWS service-based questions: Map the right AWS service to the correct AI/ML task.
- Matching questions: Connect concepts, services, or data workflows accurately.
- Case study questions: Analyze real-world examples of AI deployments on AWS.
- Concept-based questions: Test theoretical knowledge of AI, ML, and Generative AI principles.
Real Exam-Like Format: Multiple-choice and multiple-response questions designed to simulate timing, format, and difficulty.
Comprehensive Explanations: Each question includes rationales for all answer options, helping learners understand why answers are correct or incorrect.
Latest Syllabus Alignment: Fully updated with 2025 AWS Certified AI Practitioner exam objectives.
Every Question Mapped to Domains: Helps track coverage and focus preparation strategically.
Scenario-Based & Practical Questions: Real-world examples replicate challenges you’ll encounter on the exam and in AI deployments.
Exam Weightage Distribution: Questions follow official domain weightage for optimized preparation.
Timed Practice: Simulate real exam durations to develop time management skills.
Ideal for IT & Non-IT Professionals: Build AI literacy and practical AWS AI skills across job roles.
Randomized Question Bank: Prevent memorization and encourage active problem-solving.
Performance Analytics: Receive insights into strengths and weaknesses across AI domains, including AWS services, Generative AI, and ethical AI practices.
Practical, Real-World Application: Reinforce learning through applied scenarios, case studies, and problem-solving questions across all domains.

Exam Details

Exam Body: Amazon Web Services (AWS)
Exam Name: AWS Certified AI Practitioner (AIF-C01)
Prerequisite Certification: None
Recommended Experience: Up to 6 months of exposure to AI/ML technologies on AWS
Exam Format: Multiple Choice, Multiple Response, Ordering, Matching, and Case Study questions
Certification Validity: Three years (requires recertification)
Number of Questions: 65 (50 scored + 15 unscored)
Passing Score: 700 (on a scaled score of 100-1000)
Exam Duration: 130 minutes
Language: English
Exam Availability: Online proctored exam or at Pearson VUE test centers

SUBSCRIPTION COUPON

Coupon Code: 512E7A2DCE7416215EBE
Validity: 31 Days
Starts: 09/20/2025 12:00 AM PDT (GMT -7)
Expires: 10/21/2025 12:00 PM PDT (GMT -7)

Detailed Syllabus and Topic Weightage

The AWS Certified AI Practitioner exam validates overall knowledge of AI/ML, generative AI technologies, and associated AWS services. The target candidate uses but does not necessarily build AI/ML solutions. The syllabus is divided into 5 Domains, with question distribution reflecting topic weightage.

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

Explain basic AI concepts and terminologies (AI, ML, Deep Learning, NLP, Computer Vision, LLMs).
Identify practical use cases for AI and determine when it is appropriate.
Describe the ML development lifecycle and MLOps fundamentals.
Explain the capabilities of AWS managed AI/ML services (e.g., Amazon SageMaker, Transcribe, Comprehend).
Deep Dive: Learn the differences between supervised, unsupervised, and reinforcement learning. Understand the various types of data used in AI (tabular, image, text) and the complete ML pipeline from data collection and model training to deployment and monitoring in a production environment.

Domain 2: Fundamentals of Generative AI (24%)

Explain foundational generative AI concepts (tokens, embeddings, prompt engineering, foundation models).
Identify potential use cases and understand the capabilities and limitations of generative AI.
Describe the advantages, disadvantages, and business value of generative AI solutions.
Describe AWS infrastructure and technologies for building generative AI applications (e.g., Amazon Bedrock, SageMaker JumpStart).
Deep Dive: Grasp the entire foundation model lifecycle, from pre-training to fine-tuning. Explore the business impact of generative AI, including its key advantages like adaptability and critical challenges such as “hallucinations.” Understand the cost-benefit analysis of using managed services versus building from scratch.

Domain 3: Applications of Foundation Models (28%)

Describe design considerations for applications using foundation models, including model selection and cost tradeoffs.
Choose effective prompt engineering techniques (e.g., chain-of-thought, few-shot) and understand their risks.
Describe the training, fine-tuning, and evaluation process for foundation models.
Define Retrieval Augmented Generation (RAG) and identify AWS services for vector databases.
Deep Dive: Master the art of prompt engineering to reliably improve model responses and mitigate security risks like prompt injection. Discover how RAG enhances model accuracy by connecting it to proprietary data sources. Evaluate the performance of foundation models using automated metrics and human feedback.

Domain 4: Guidelines for Responsible AI (14%)

Explain the development of responsible AI systems (fairness, bias, robustness, safety).
Identify features, legal risks, and tools for responsible AI (e.g., Guardrails for Amazon Bedrock, SageMaker Clarify).
Recognize the importance of transparent and explainable models and the principles of human-centered design.
Deep Dive: Identify and mitigate different forms of bias in datasets and models. Understand legal and reputational risks of AI, including intellectual property infringement and loss of customer trust. Learn to use AWS tools to promote fairness, robustness, and truthfulness throughout the AI lifecycle.

Domain 5: Security, Compliance, and Governance for AI Solutions (14%)

Explain methods to secure AI systems using AWS services (IAM, encryption, VPC).
Understand security and privacy considerations, including data lineage and secure data engineering.
Recognize governance and compliance regulations for AI systems (e.g., ISO, SOC).
Describe data governance strategies and AWS services that assist with compliance (e.g., AWS Artifact, CloudTrail, Config).
Deep Dive: Implement security best practices for AI systems, including controlling access with IAM, encrypting data, and using AWS PrivateLink for secure VPC connectivity. Establish a strong governance framework ensuring adherence to industry standards.

In-Scope AWS Services

Candidates should be familiar with the use cases for the following AWS services:

AI/ML Core: Amazon SageMaker, Amazon Bedrock, Amazon Q
AI Services: Amazon Comprehend, Amazon Lex, Amazon Polly, Amazon Rekognition, Amazon Transcribe, Amazon Translate, Amazon Kendra, Amazon Textract
Data & Analytics: Amazon S3, Amazon OpenSearch Service, Amazon RDS, Amazon Aurora, Amazon DynamoDB
Security & Governance: AWS IAM, AWS KMS, Amazon Macie, AWS CloudTrail, AWS Config, AWS Audit Manager, AWS Artifact, AWS Trusted Advisor

AWS Certified AI Practitioner – Domain Weightage

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 for AI Solutions – 14%

Practice Test Structure & Preparation Strategy

Prepare for the AWS Certified AI Practitioner (AIF-C01) certification exam with realistic, exam-style mock tests that build conceptual understanding, hands-on readiness, and exam confidence.

6 Full-Length Practice Tests: 6 complete mock exams with 65 questions each (390 Questions total), timed and scored, reflecting the real exam structure, style, and complexity.
Diverse Question Categories: Questions are designed across multiple types and skill levels to mirror the AWS AIF-C01 exam.
- Knowledge-Heavy Questions: Focus on recalling AI/ML fundamentals, AWS AI services, and generative AI concepts.
- Application & Analysis Questions: Scenario-based, case study, and AWS service-based questions test your ability to apply knowledge and reason through real-world AI problems.
- Hands-On Elements: Ordering, matching, and concept-based questions help reinforce practical understanding of AWS AI/ML services, foundation models, and responsible AI practices.
Comprehensive Explanations: Each question includes detailed reasoning for correct and incorrect options to deepen conceptual understanding.
Timed & Scored Simulation: Practice under realistic exam durations to develop focus, pacing, and endurance for the AIF-C01 certification.
Randomized Question Bank: Questions and answer options reshuffle in each attempt to prevent memorization and encourage active learning.
Performance Analytics: Gain domain-wise insights to identify strengths and areas for improvement, helping you strategically focus on topics like AWS AI services, generative AI, responsible AI, foundation models, and AI/ML workflows.

Sample Practice Questions

Question 1

Which AWS services help organizations maintain compliance and governance for their AI systems by providing configuration tracking, audit reporting, and compliance documentation?

Options:

A. Amazon Rekognition and Amazon Polly
B. AWS Config and AWS Audit Manager
C. Amazon S3 and Amazon EC2
D. AWS Lambda and Amazon SQS

Answer: B

Explanation:

A: Rekognition and Polly are AI services for computer vision and text-to-speech. They do not provide governance, compliance monitoring, or audit capabilities for managing AI systems.
B: AWS Config continuously monitors and records AWS resource configurations, enabling compliance checking against desired states. AWS Audit Manager automates evidence collection for audits and maps controls to compliance frameworks, together providing comprehensive governance capabilities for AI infrastructure.
C: S3 and EC2 provide storage and compute infrastructure but do not offer governance features. While they can be monitored by governance services, they do not themselves provide compliance tracking or audit reporting.
D: Lambda and SQS are compute and messaging services for application architectures. They do not provide compliance frameworks, configuration monitoring, or audit management functionality.

Domain: Security, Compliance, and Governance for AI Solutions
Question Type: Service-Based

Question 2

How does AWS Trusted Advisor support AI system governance by identifying security gaps, cost optimization opportunities, and best practice violations?

Options:

A. Trusted Advisor provides recommendations for optimizing AWS resources and security
B. Trusted Advisor replaces the need for security monitoring
C. Trusted Advisor only works with non-AI workloads
D. Trusted Advisor eliminates all security vulnerabilities automatically

Answer: A

Explanation:

A: AWS Trusted Advisor analyzes AWS environments against best practices for cost optimization, performance, security, fault tolerance, and service limits. It identifies issues like exposed resources, unused capacity, and security gaps, helping organizations maintain well-architected AI infrastructure.
B: Trusted Advisor provides recommendations but does not replace comprehensive security monitoring through services like CloudTrail, GuardDuty, and Security Hub. It complements rather than substitutes active threat detection and incident response capabilities
C: Trusted Advisor evaluates all AWS resources including those used for AI workloads like SageMaker, S3 buckets storing training data, and EC2 instances running models. Its recommendations apply universally across workload types.
D: Trusted Advisor identifies issues and provides recommendations but does not automatically remediate problems. Organizations must review findings and implement suggested fixes, with automation requiring additional tools like AWS Config rules or Lambda functions

Domain: Security, Compliance, and Governance for AI Solutions
Question Type: Multiple-choice

Question 3

Arrange the following prompt engineering techniques in order from least to most guidance provided to the model:

Few-shot prompting
Zero-shot prompting
Single-shot prompting
Chain-of-thought prompting with examples

Options:

A. 4, 1, 3, 2
B. 2, 3, 1, 4
C. 1, 2, 3, 4
D. 2, 1, 3, 4

Answer: B

Explanation:

A: This ordering incorrectly places chain-of-thought with examples as providing the least guidance, when it actually provides the most by including both examples and reasoning steps. Zero-shot is also misplaced at the end when it provides no examples at all.
B: This represents the correct progression of guidance levels. Zero-shot provides only instructions without examples, single-shot offers one example for reference, few-shot includes multiple examples to demonstrate patterns, and chain-of-thought with examples adds explicit reasoning steps alongside examples, providing the most comprehensive guidance for complex tasks requiring step-by-step logic.
C: This sequence incorrectly starts with few-shot instead of zero-shot, despite zero-shot providing no examples and therefore the least guidance. Few-shot inherently provides more guidance than single-shot by offering multiple examples, making this ordering logically inconsistent with how these prompting techniques function.
D: This ordering places few-shot before single-shot, which contradicts the guidance spectrum since few-shot provides multiple examples while single-shot provides only one. The correct progression should move from no examples to one example to multiple examples, making this sequence incorrect in its middle positions.

Domain: Fundamentals of Generative AI
Question Type: Ordering

Question 4

Compare the use cases and operational characteristics of Amazon Bedrock versus Amazon SageMaker for building AI applications. (Select TWO)

Options:

A. Bedrock provides serverless access to pre-trained foundation models
B. SageMaker cannot train custom models
C. Bedrock requires managing model training infrastructure
D. SageMaker offers more control over model training and deployment

Answer: A, D

Explanation:

A: Amazon Bedrock offers managed access to third-party and Amazon foundation models without requiring infrastructure setup or model training. This serverless approach simplifies generative AI application development with pay-per-use pricing.
B: Amazon SageMaker is specifically designed for building, training, and deploying custom ML models. It provides comprehensive tools for the entire ML lifecycle including data preparation, training, and model hosting.
C: • Amazon Bedrock abstracts infrastructure management entirely, providing a fully managed service where AWS handles model hosting, scaling, and availability. Users interact through APIs without managing underlying infrastructure.
D: • SageMaker provides extensive control over training algorithms, hyperparameters, infrastructure configuration, and deployment options. This flexibility supports custom model development and experimentation beyond what managed foundation model services offer..

Domain: Fundamentals of Generative AI
Question Type: Matching

Preparation Strategy & Study Guidance

Understand the Concepts, Not Just the Questions: Use mock exams to identify weak areas, but supplement your study with official AWS exam guide and AWS documentation.
Target 80%+ in Practice Tests: Real exam requires 65% passing; higher scores build confidence.
Review Explanations in Detail: Carefully study why each answer is correct or incorrect.
Simulate Real Exam Conditions: Attempt mock tests in timed, distraction-free sessions.
Hands-On Application: Reinforce AI/ML knowledge and AWS services expertise through SageMaker workflows, foundation model prompt engineering, generative AI applications, and bias detection.

Why This Course Is Valuable

Realistic exam simulation aligned with AWS AIF-C01 format, covering diverse question types (multiple-choice, ordering, matching, scenario, case study, concept-based).
Full syllabus coverage, including AI fundamentals, ML workflows, Generative AI, foundation models, responsible AI, and AWS AI services.
In-depth explanations for correct and incorrect answers.
Timed, scored tests with randomized questions.
Suitable for IT and non-IT professionals.
Updated as per the latest 2025 AWS AIF-C01 syllabus and exam objectives.

Top Reasons to Take This Practice Exam

6 full-length mock exams with 390 questions
100% coverage of official AWS AIF-C01 syllabus
Realistic multiple-choice, multiple-response, ordering, scenario, matching, case study, concept-based questions
Detailed rationales for correct and incorrect answers
Balanced question distribution across foundational, application, and analytical levels
Scenario-based, concept-based, and AWS service-based questions for practical learning
Timed simulations to replicate real exam conditions
Randomized question bank to encourage active learning and prevent memorization
Accessible anywhere, anytime on desktop or mobile devices
Lifetime updates included for syllabus changes

What This Course Includes

6 Full-Length Practice Tests: Simulate real exam conditions to test your readiness.
Access on Mobile: Study anytime, anywhere on your phone or tablet.
Full Lifetime Access: Learn at your own pace with no expiration.

Money-Back Guarantee

Your success is our priority. If this course doesn’t meet your expectations, you’re covered by a 30-day no-questions-asked refund policy.

Who This Course Is For

Professionals preparing for the AWS Certified AI Practitioner (AIF-C01) exam.
IT professionals with limited exposure to AI/ML and Generative AI who want to make informed decisions when building or managing AI solutions.
Non-IT professionals in marketing, sales, project/product management, HR, finance, accounting, and other domains seeking confidence in identifying AI opportunities or collaborating with technical teams.
Developers, data analysts, and cloud engineers enhancing AWS AI/ML skills.
Professionals addressing real-world AI challenges, including bias, explainability, and responsible AI practices.
Career changers aiming to develop expertise in AI applications, AWS services, and AI solution implementation.

What You’ll Learn

Core AI and ML principles, including supervised/unsupervised learning, deep learning, and foundation models.
Generative AI concepts, prompt engineering, and AWS AI/ML services like SageMaker, Bedrock, Comprehend, Rekognition, and Transcribe.
Practical application of AI/ML workflows, model evaluation, and business use cases on AWS.
Guidelines for responsible AI, including bias detection, fairness, explainability (XAI), and human-centered AI design.
Hands-on experience with scenario-based, AWS service-based, and concept-based questions.
Time management, exam strategies, and practice approaches for the AWS Certified AI Practitioner (AIF-C01) exam.
Practical knowledge to confidently pass the AWS AIF-C01 certification and apply AI solutions in real-world business scenarios.

Requirements / Prerequisites

Basic understanding of cloud computing or IT fundamentals is helpful but not mandatory.
Familiarity with AI/ML concepts, generative AI, or AWS services is beneficial but not required.
Computer with internet access for online mock exams.
Curiosity to learn AI concepts, AWS AI/ML services, foundation models, and generative AI applications.
Willingness to practice and apply knowledge using scenario, ordering, matching, and case-study based questions.