CIW Certifications
> Artificial Intelligence Series > AI Associate
Artificial Intelligence Associate
Exam Now Available
Quick Reference
Certification Name:
CIW Artificial Intelligence Associate
Exam ID: 1D0-181
Number of Questions: 54
Passing Score: 74.07%
Time Limit: 75 minutes
Course Name:
Artificial Intelligence Associate
Prerequisites
- Knowledge of computers, networks, internet technologies, database creation, and basic programming languages.
- CIW Internet Business Associate or equivalent knowledge
- CIW Site Development Associate or equivalent knowledge
- CIW Database Design Specialist or equivalent knowledge
The CIW Artificial Intelligence Associate certification is the part of the CIW Artificial Intelligence series which provides a broad understanding into the world of AI careers. This exam validates in-depth knowledge of AI (history, definition, methods and algorithms, applications, careers, etc.), privacy concerns, ethical issues in AI, and responsible development; an understanding of the essentials of AI system design, program design structure, problem identification, and solution implementation; and a working knowledge of application deployment, testing, data management (dataset creation, selection and curation).
The CIW Artificial Intelligence Associate course* prepares candidates to take the CIW Artificial Intelligence Associate exam, which, if passed, earns the individual the CIW Artificial Intelligence Associate certification.
*The courseware is not required to sit for the certification exam.
Target Audience
- AI Research
- Software Engineers
- UI/UX Developers
- Data Analytics
- Big Data Engineers/Architects
- Machine Learning Engineers
- Business Intelligence Developers
Skills Assessed
- Basic knowledge of AI machine learning, deep learning and computer science
- How to solve problems using AI
- How to apply reasoning to deal with contingencies while planning
- How logic is used to build reasoning
- How to identify compromised information, misinformation and deepfakes.
- What social aspects of AI impact communities and AI impact on productivity
- How AI can improve user experience
- AI system design and other factors affecting the cost of developing ML models
- Selecting, curating, and creating datasets for cleaning, sampling, storage and other tasks
- Knowledge of how algorithms used in AI, and how to distinguish deep learning from other learning algorithms.
- Managing legal, ethical and privacy issues within AI systems design, development and deployment
- Basic Machine Learning models and how predictions and decisions are made
- Statistical concepts and foundations, including descriptive statistics, testing concepts and methods
