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PCEI™ – Certified Entry-Level AI Specialist with Python — Information & Free Practice

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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

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