AI Engineer I
Validate your skills in prompt engineering, introductory and advanced AI/ML, AI models, and natural language processing. Upon passing, you’ll receive an AI Engineer I digital certification—share it on LinkedIn, your personal site, or your resume. Prospective employers can verify its authenticity through our verification system. Learn more on the Certification Process page.
Study Materials:
College-level computer science coursework with a strong AI focus is recommended, but not required. The exam covers prompt engineering, introductory and advanced AI/ML concepts, AI models, and natural language processing. For further preparation, refer to the Entry Engineer Roadmap (AI Engineer sections) and consider our Study Guide, which outlines exactly what to study for each topic.
Availability Notice:
Our certification programs and exams are currently available exclusively to U.S. residents. We intend to broaden access internationally so feel free to sign up for our free newsletter to be notified. Thank you for your patience and understanding.
- Price: $179
- Test Link: [Live Mid-March]
- Total Questions: 74
- Passing Score: 73%
- Duration: 100 minutes
- Proctoring: This is a proctored exam. Our exams do not have a live proctor, we use webcam recording and screen recording + human review.
- Attempts: 1
- Expiration: Credential will expire after three years of issuance. Renewal Fee is $28 or you can choose to let expire.
- Results: Final results and certification may take up to ten business days due to manual processing.
You do not need to start your exam instantly. You have three months from date of purchase.
Exam Topics
1. Prompt Engineering – 26%
• Crafting Effective Prompts (Zero-Shot, One-Shot, Few-Shot)
• Advanced Prompting Techniques (Chain-of-Thought, Role-Based, Variable Templating)
• Non-Technical Considerations (Disclaimers, Feedback Handling, Clarity)
• Controlling Output (Format, Style, Parameters)
2. AI & ML Foundations – 21%
• Types of Learning (Supervised, Unsupervised, Reinforcement)
• Neural Networks & Deep Learning Basics (Architecture, Activations)
• Overfitting & Regularization (Dropout, Early Stopping)
• Optimization & Evaluation Metrics (SGD, Adam, Precision/Recall, F1)
3. Advanced AI & Advanced Machine Learning – 16%
• Reinforcement Learning (Q-Learning, Policy Gradients)
• Generative Models (GANs, Autoencoders) & Transfer Learning
• Interpretability & Data Challenges (Imbalanced Data, Covariate Shift)
• Residual/Skip Connections, Batch/Layer Norm (Network Enhancements)
4. AI Models – 15%
• Transformer Basics & Attention
• Key Architectures (GPT, BERT, CLIP)
• Model Capabilities & Limitations (Scaling, Bias, Hallucinations)
• Fine-Tuning & Zero-/Few-Shot Adaptation
5. Natural Language Processing (NLP) – 22%
• Tokenization & Basic NLP Tasks (POS Tagging, NER)
• Word Embeddings & Semantic Understanding (Word2Vec, Contextual Embeddings)
• Topic Modeling & Summarization (LDA, Extractive/Abstractive)
• Sentiment Analysis & Translation