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Exam Details: AI-Native Trainer Exam - AI-Native Trainer

Written by Kris McKinney

Requirements

  • Review and acceptance of the Candidate Agreement are required to start the exam

  • Exam functionality instructions are provided following the Candidate Agreement

Exam Format

  • Questions are structured in a multiple choice, single select format

  • Exams are timed and the timer is displayed once questions are presented

  • Exams will be submitted when the timer ends, regardless of the number of questions answered

  • Scores are calculated by the number of correctly answered questions

  • Unanswered questions will be marked as incorrect

  • “Submit” button ends the exam and a score will be calculated

Practice Test

  • Format reflects the same number of questions, level of difficulty, timebox, and domain areas as the exam

  • Unlimited attempts

  • Passing the practice test does not guarantee passing the exam and is provided as a preparation resource

Details are subject to change at any time

Duration

120 minutes

Number of Questions

60

Passing Score

80%

Delivery

Web-based, closed book, no outside assistance

Access

Learning Plan (after course completion)

Cost

The first attempt is included in your course registration fee when taken within 60 days of course completion. Additional attempts are available for a fee. Please refer to the Retake Policy below.

Retake Fee

$250

Retake Policy

  • First retake: Available immediately after the second failed attempt

  • Second retake: Available 10 days after the first retake

  • Third retake: Available 30 days after the second retake

  • Additional Retakes: Each requires a 30-day waiting period between attempts

Domain

Topics

AI-Native Mindset (14-16%)

  • Explain why AI-Native training differs from traditional content delivery.

  • Identify the core competencies of an AI-Native trainer and self-diagnose strengths/gaps.

  • Apply the "Make It Yourself" principles (stay current, contextualize, be authentic) to a delivery scenario.

Foundations Course Design and Experience Map

(18-22%)

  • Identify the 5 components of the Experience Map and their facilitation demands.

  • Sequence or map the 7 Foundations lessons to the 2-day journey structure.

  • Predict which experience-map components are hardest to execute live and why.

Tooling and Technical Readiness

(18-22%)

  • Identify Companion App features and how/when to introduce them to learners.

  • Adapt tooling guidance for learners resistant to or do not have access to using an LLM.

  • Complete a pre-class readiness checklist accurately

  • Real time troubleshooting of issues as they arise in the classroom

  • Select an appropriate backup plan for a common technical failure.

  • Permitted tool uses, tool frameworks, and associated tool use risks

Advanced Facilitation and Adaptation

(28-32%)

  • Apply the 4Cs framework to build or critique a session plan against the actual Foundations course.

  • Distinguish adaptations required for public vs. private/internal cohorts.

  • Determine the single most valuable pre-class question for a given context.

  • Choose appropriate techniques for engaging anxious or resistant learners.

  • Make sound in-the-moment facilitation decisions in a first-delivery scenario (case analysis).

  • Readiness concepts and application, and general training facilitation

Continuous Upskilling

(14-16%)

  • Identify the habits most correlated with staying current on AI tooling.

  • Evaluate a sample 90-day plan for completeness and sequencing.

  • Recognize when and how to escalate for help versus self-resolve.

  • Importance of trainer's personal statement and information that should be included

  • Importance and features of adopting/using AI Native Connect

  • Importance of trainer feedback and reflection to improve trainings

URL name

Exam-Details-AINT

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