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PCEI™ – Certified Entry-Level AI Specialist with Python

Entry-level certification validating foundational AI knowledge and practical reasoning with Python.

Licensing authority: OpenEDG Python Institute · Application information

Initial by examination
PCEI™ – Certified Entry-Level AI Specialist with Python
Award
Pass the PCEI-30-01 examination with cumulative average score of at least 75%; only a session approved after full verification is final for certification. Digital credentials are emailed and available in the candidate account. Credential validity is seven years; renewal is outside this task.
Credential Type
Industry skills certification, available worldwide including the United States.
Eligibility
No formal prerequisites; basic Python, basic data analysis and digital literacy equivalent to PCEP/PCED are recommended only. Under 13 cannot test or receive certification; ages 13–17 require parental/legal guardian consent; age 18+ has no age restriction; additional local data-protection restrictions may apply.
Registration
Create a Test Candidate account and buy an exam voucher, then enter its code, complete equipment diagnostics and check-in, and accept the Code of Conduct, NDA and Testing Policies. English examination via TestNow; follow the official technical and identification requirements.
Scheduling
Global online proctored delivery allows launch with an unexpired unredeemed voucher and does not require rescheduling. Partner delivery requires proctor authorization, secure venue and identity verification; arrive 15 minutes before the scheduled appointment and contact the provider at least 24 hours ahead to change it. Accommodation approval is required before an appointment; failed-exam retakes require a seven-day wait and may require a new voucher.

PCEI™ – Certified Entry-Level AI Specialist with Python

Provider
OpenEDG Testing Service – TestNow™
Format
Proctored computer-based examination in English; single-select, multiple-select, scenario-based and interactive items.
Exam questions
Total questions: 36.
Time limit
60 minutes + NDA
Passing score
Cumulative average score of at least 75% across all exam blocks.
Exam fee
Exam from USD 69; exam plus retake from USD 86.
Examination outline
  • Artificial Intelligence Fundamentals (14%)
    • Define Artificial Intelligence and Its Core Concepts
    • Distinguish Major Subfields of AI
    • Describe How AI Systems Learn From Data
    • Identify the Capabilities and Limitations of AI Systems
    • Identify Requirements for Building AI-Driven Solutions
  • Machine Learning Fundamentals (16.5%)
    • Distinguish Types of Machine Learning
    • Describe the Machine Learning Workflow
    • Identify Common Machine Learning Algorithms and Their Uses
    • Implement Simple Machine Learning Logic in Python
    • Evaluate Machine Learning Models Using Basic Metrics
  • Data Handling, Analysis, and Visualization (16.5%)
    • Handle and Process Data Using Python
    • Analyze Numerical and Categorical Data in Python
    • Work With Numerical Data and Distances for AI Tasks
    • Use Python Tools to Organize and Prepare Data for Machine Learning
    • Visualize Data Using Python
    • Explain the Importance of Data Quality in AI
  • Neural Networks, Deep Learning, and Generative AI (22.5%)
    • Describe the Foundations of Neural Networks
    • Distinguish Classical Machine Learning From Deep Learning
    • Describe the Fundamentals of Natural Language Processing (NLP)
    • Describe the Fundamentals of Computer Vision (CV)
    • Explain the Principles of Generative AI and Large Language Models
    • Apply Basic Prompt Engineering Techniques
    • Recognize How Deep Learning Models Are Used in Real Applications
  • Responsible AI, Ethics, and Critical Thinking (16.5%)
    • Identify Ethical Risks and Safety Concerns in AI
    • Apply Basic Safety and Security Practices When Using AI Systems
    • Analyze the Social and Economic Impact of AI
    • Explain Principles of Responsible, Transparent, and Human-Centered AI
    • Apply Critical Thinking to Evaluate AI Outputs
  • AI Projects, Collaboration, and Communication (14%)
    • Identify Opportunities for AI Solutions in Real-World Contexts
    • Recognize Basic Cost Considerations When Planning AI Projects
    • Apply Basic AI Development Steps to a Small Project
    • Collaborate Effectively in AI Projects
    • Communicate AI Findings Clearly to Technical and Non-Technical Audiences
Examination sources