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[NEW] AWS Certified AI Practitioner – Exam Preparation 2026

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AWS Certified AI Practitioner (AIF-C01) – 6 Practice Exams with Detailed Explanations for Every Question and Option
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1/5
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100 students
Created by Certification Interview Academy
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What you'll learn

  • Understand core AI, machine learning, deep learning, and generative AI concepts.
  • Explain the fundamentals of large language models and foundation models.
  • Select appropriate AWS AI and generative AI services for common business use cases.
  • Apply basic prompt engineering and foundation model adaptation techniques.
  • Identify responsible AI principles, including fairness, bias, transparency, and safety.
  • Understand security, compliance, governance, and responsible use of AI solutions on AWS.
This course includes:
160 questions on-demand video
0 articles
0 downloadable resources
0 lessons
Full lifetime access
Access on mobile and TV
Certificate of completion
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Course content

Requirements

  • No prior AI, machine learning, or AWS experience is required; basic computer and cloud computing familiarity is helpful but not necessary.

Description

The AWS Certified AI Practitioner (AIF-C01) validates foundational knowledge of AI, machine learning, generative AI, and responsible AI on AWS. It is a foundational‑level exam that tests conceptual understanding, service selection, and governance of AI solutions.

Exam Domains & Sample Topics:

– Fundamentals of AI and ML (20%)

  Topics: AI vs. ML vs. deep learning vs. generative AI, Supervised, unsupervised, and reinforcement learning, Common ML algorithms (classification, regression, clustering), ML development lifecycle (data collection, preparation, training, evaluation, deployment, monitoring), Practical AI use cases (forecasting, recommendation, anomaly detection, computer vision, NLP)

– Fundamentals of Generative AI (24%)

  Topics: Core concepts of large language models, Prompt engineering basics, Generative AI service offerings on AWS (e.g., Amazon Bedrock), Use cases for generative AI in business, Limitations and ethical considerations of generative AI

– Applications of Foundation Models (28%)

  Topics: Architecture of foundation models, Fine‑tuning and adaptation techniques, Mapping business problems to appropriate foundation models, Performance evaluation metrics for foundation models, Integration of foundation models with AWS services

– Guidelines for Responsible AI (14%)

  Topics: Fairness, bias detection, and mitigation, Inclusivity and diverse training data, Transparency, explainability, and interpretability, Safety, robustness, and human oversight, AWS tools for responsible AI (SageMaker Clarify, Bedrock Guardrails, Model Cards)

– Security, Compliance, and Governance for AI Solutions (14%)

  Topics: Identity and access management for AI workloads, Data encryption and protection in AI pipelines, Compliance frameworks relevant to AI (e.g., GDPR, HIPAA), Monitoring and audit logging for AI services, Governance features like model versioning and policy enforcement

Who this course is for:

  • Beginners who want to learn the fundamentals of AI, machine learning, and generative AI on AWS.
  • IT professionals preparing for the AWS Certified AI Practitioner (AIF-C01) certification.
  • Cloud professionals who want to understand AWS AI and machine learning services.
  • Developers interested in integrating generative AI and foundation models into applications.
  • Business and technology professionals exploring practical AI use cases.
  • Students and career changers looking to build foundational AI and AWS knowledge.
  • Data and machine learning professionals seeking a structured introduction to AWS AI concepts.
  • Project managers and technical leaders involved in AI or cloud-based initiatives.
  • Professionals who want to understand responsible AI, security, compliance, and governance.
  • Anyone seeking exam-focused practice and foundational knowledge for the AWS Certified AI Practitioner certification.
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