Final Exam: AI Apprentice

Expected Duration
Lesson Objectives
Course Number
Expertise Level


Final Exam: AI Apprentice will test your knowledge and application of the topics presented throughout the AI Apprentice track of the Skillsoft Aspire AI Apprentice to AI Architect Journey.

Expected Duration (hours)

Lesson Objectives

Final Exam: AI Apprentice

  • classify different types of Convolutional Neural Networks by their structure and purpose
  • compare artificial superintelligence with artificial general intelligence and specify the multiple factors needed to achieve them
  • compare cognitive modeling and artificial intelligence
  • compare image processing to Computer Vision
  • compare image processing to traditional methods of solving image problems
  • compare multiple approaches to AI development to distinguish key differences between them
  • compare the major differences between intelligent systems including search algorithms, machine learning systems, probabilistic models, neural networks, and reinforcement learning systems
  • compare the performance and functionality of Python AI toolbox to R AI toolbox
  • configure the Python environment for developing AI
  • define general intelligence in terms of AI tools known today and recognize the amount of work needed to achieve any AGI
  • define Human-Computer Interaction as a multidisciplinary field essential to computer science and describe its importance for the success of software companies
  • define hybrid learning and describe examples of its use
  • define narrow artificial intelligence, describe multiple areas of its use in the modern world, and recognize the latest research
  • define reactive and limited memory systems and describe reactive AI, limited memory AI, and a combination of both
  • define symbolic learning and describe examples of its use
  • describe and distinguish between different types of modeling tools
  • describe and distinguish between multiple Python AI libraries
  • describe basic concepts in Computer Vision
  • describe distinguishing features of adaptive, interactive, iterative, and contextual cognitive models
  • describe factors that make Python one of the most popular programming languages
  • describe how a CV is used in electronics and why cheap consumer electronics are not possible without CV
  • describe how a CV is used in the aerospace industry and list the responsibilities of a CV system on an aircraft
  • describe how a CV is used in the automotive industry and its role in the development of self-driving cars
  • describe how the success of AI solutions in narrow fields is a combination of adequate task, good data, and appropriate tools and list fields that are most impacted by AI
  • describe the principles of prototyping and distinguish between a prototype and a demonstration product
  • describe the principles of the anthropomorphic approach to HCI
  • describe the Python programming language and recognize its role in AI development
  • describe the role Computer Vision plays in the industry and associated trends
  • describe the role of a user-oriented approach in the success of AI applications
  • describe the steps needed to create deep learning models and identify guidelines for using them
  • describe the steps needed to create machine learning models and identify guidelines for using them
  • describe true research on self-aware AI and compare it with common views on the future of AI
  • describe why big data improve AI performance and accuracy by specifying how collecting large amounts of data creates opportunities for new AI development and research
  • describe why using artificial intelligence is becoming important today and list multiple factors that make the use of AI in business necessary for competitive advantage
  • differentiate between interpreted and compiled programming languages
  • distinguish between an intelligent system and pre-programmed logic using several definitions of artificial intelligence and specify the scope of AI applications
  • identify and describe problems that can be solved using Computer Vision
  • identify different types of cognitive models and name popular cognitive modeling applications
  • identify reasons why the iterative approach has shown to be most practical when designing software applications
  • identify the advantages of using Python when developing AI
  • identify the main steps in the HCI process and name multiple methodologies used
  • illustrate how AI can be part of a Computer Vision solution
  • illustrate how computer science is connected with cognitive modeling
  • list the components involved in human-computer interaction (HCI) studies and specify their role
  • list the steps needed to create an object detection neural network and describe how object detection is performed
  • list the tools commonly used for HCI studies and specify their purpose
  • name and describe basic concepts in and cognition and cognitive modeling
  • name and describe different types of cognitive learning
  • recognize how CI/CD became essential to any kind of software company and list multiple factors that make CI/CD important for AI companies
  • recognize how the performance of Convolutional Neural Network revolutionized CV
  • recognize major AI tools used in the industry
  • recognize the most recent research breakthroughs in AI and how they might be used, and list applications of AI that are already on the market
  • recognize the multidisciplinary nature of HCI and list the areas most involved in the studies
  • specify how AI has affected cognitive modeling and enhanced its power
  • specify multiple disciplines involved in cognitive modeling and describe their role
  • specify the advantages of Jupyter Notebooks and create Jupyter Notebook files connected to the appropriate kernel environment
  • specify the advantages of the Google Collab environment and create files in the environment
  • specify the role of Anaconda in keeping clear working environments
  • specify why explainability research in AI is required for developing user-friendly applications
  • troubleshoot usability of an AI application prototype
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    Expertise Level