FINAL EXAM ONLY GENAI_INTRO_LA

Generative AI: Introduction and Applications – Exam

This professional course offers a comprehensive introduction to generative artificial intelligence, covering its theoretical fundamentals and key practical applications. Learners will explore the differences between traditional AI and generative AI, understanding how generative models such as Large Language Models and diffusion models operate, along with prompt engineering techniques to optimize system interactions. Real-world use cases will be studied including text generation, summarization and translation, image creation, and virtual assistant automation. Special emphasis is placed on evaluating output quality, managing risks like hallucinations, and security concerns such as prompt injections. The course also covers crucial topics in data handling, privacy, copyright, and intellectual property. The integration of generative AI into business processes is examined, including workflows, Robotic Process Automation (RPA), and API usage, supported by an overview of popular tools and platforms like ChatGPT and Copilot. The program concludes with an exam designed to assess applied competencies through realistic professional and complex implementation scenarios.

Exam duration60 min

Pass mark70%

Available questions200

CIWZ PROFESSIONAL ASSESSMENT

High-integrity learning environment

The pathway is designed for managers, executives, function owners, auditors and professionals. Lessons use validated minimum study time and a contextual tutor; assessments use server-side random selection of questions and answers, session-continuity controls, camera and local audio-activity monitoring during protected tests.

Traceability and reliability: integrity events are logged for authorized review; automated signals do not determine the result on their own.

OFFICIAL PATHWAY INFORMATION

Course technical sheet

Purpose, references, delivery method, commercial terms and certificate value, presented clearly and transparently.

Learning Objectives

Understand the fundamentals of generative AI and differences from traditional AI
Learn how generative models like LLMs and diffusion models work
Develop foundational prompt engineering skills and best practices
Analyze use cases including text, image generation, summarization, translation, and automation
Evaluate output quality and mitigate risks such as hallucinations and security vulnerabilities
Manage regulatory and governance aspects related to sensitive data, privacy, and intellectual property
Integrate generative AI in business processes with workflows, RPA, and APIs

Skills Acquired

Technical knowledge of generative models and their operation
Ability to design and optimize effective prompts
Understanding of risks and best practices in security and governance
Practical application of generative AI in real and business contexts
Critical evaluation of generated outputs and quality management

Target Audience

ICT professionals, data analysts, developers
Innovation managers and leaders
Consultants and digital trainers

Prerequisites

Basic knowledge of artificial intelligence and machine learning suggested
Familiarity with digital business processes

Program

Fundamentals of generative AI and comparison with traditional AI
Generative models: LLM, Transformer, Diffusion
Prompt engineering: techniques and best practices
Use cases: text, images, translation, automation
Output quality evaluation, risk management, and security
Privacy, copyright, and intellectual property
Business process integration and tools overview

Teaching Methodology

Theoretical lessons and case studies
Practical exercises and analysis of real scenarios
Critical discussion and comparison

Assessment Method

Final exam with open questions and applied scenarios
Passing threshold: 70%

Duration

60 minutes

Certification

Certificate upon exam passing (fee: €50)

Expected Outcomes

Ability to apply generative AI in professional environments
Awareness of risks and control methods
Preparedness for complex implementation scenarios