IT Specialist Artificial Intelligence - Certification Exam Voucher

Overview of IT Specialist Artificial Intelligence Certification 

Candidates for this certification have a foundational knowledge of the procedures used to develop an artificial intelligence (AI) solution, as well as an understanding of the issues surrounding the governance, transparency, security, and ethics of AI.  Successful candidates will be able to analyze and classify a problem.  They should be able to demonstrate knowledge of data collection, data processing, and feature engineering strategies. 

About this ITS Artificial Intelligence Certification Exam

The ITS Artificial Intelligence Certification assesses your ability to choose an appropriate algorithm for training a model, and understand the metrics used to evaluate model performance.  They should understand the AI development lifecycle and how a production pipeline is used to allow for continuous improvement.  

Why gain Certification?

Those who take and pass ITS certifications are empowered to stand out in the competitive job market, while simplifying daily tasks, boosting productivity and fostering career growth.  

Useful Information

• ITS Artificial Intelligence Exam is 50 minutes
• ITS Artificial Intelligence Exam has 45-50 questions 

Topics the exam tests you on

AI Problem Definition

• Identify the problem you are trying to solve using AI
• Classify the problem
• Identify the areas of expertise needed to solve the problem
• Build a security plan
• Ensure that AI is used appropriately
• Choose transparency and validation activities

Data Collection, Processing, and Engineering

• Choose the way to collect data
• Assess data quality
• Ensure that data are representative
• Identify resource requirements
• Convert data into suitable formats
• Select features for the AI model
• Engage in feature engineering
• Identify training and test datasets
• Document data decisions

AI Algorithms and Models

• Consider applicability of specific algorithms
• Train a model using the selected algorithm
• Select specific model after experimentation, avoiding over engineering
• Tell data stories
• Evaluate model performance
• Look for potential sources of bias in the algorithm
• Evaluate model sensitivity
• Confirm adherence to regulatory requirements, if any
• Obtain stakeholder approval

Application Integration and Deployment

• Train customers on how to use product and what to expect from it
• Plan to address potential challenges of models in production
• Design a production pipeline, including application integration
• Support the AI solution

Maintaining and Monitoring AI in Production

• Engage in oversight
• Assess business impact
• Measure impacts on individuals and communities
• Handle feedback from users
• Consider improvement or decommission on a regular basis

Additional Information

• Find Objective Domains here: IT Specialist Artificial Intelligence
• Exam tutorial here:  IT Specialist exam tutorial