Google AI Essentials
Google AI fundamentals and applications
5
Modules
120
Practice Questions
4
Field Missions
Google AI
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Exam Details
Free Preview — Module 1
Module 1 — Introduction to AI
Understand the landscape — from rule-based systems to generative models — and develop the business judgment to know when AI creates real value versus when it is overkill.
Defining the Landscape: From Logic to Intuition
Discriminative AI (judge/classifier) vs. Generative AI (creator), and the three waves of AI development through to the current Generative Foundations era.
How LLMs Work: The Transformer Revolution
Tokens, vectors, self-attention, pre-training, and RLHF — why Transformers can maintain context across long passages and engage in meaningful dialogue.
Machine Learning vs. Deep Learning: The Hierarchy
AI → ML → Deep Learning — feature engineering in traditional ML vs. automatic feature discovery in neural networks, and where generative AI fits.
Real-World Business Value: Automation to Augmentation
Three pillars: Efficiency/Summarization, Creative Synthesis/Ideation, and Predictive Insight — plus the Human-in-the-Loop (HITL) model.
Sample Practice Questions
Question 1
A Netflix recommendation engine classifies movies as likely/unlikely matches based on a user's viewing history. Which type of AI does this represent?
Discriminative AI is the 'judge' — it classifies, categorizes, or predicts based on existing data. Generative AI creates new content. A recommendation engine sorts content into 'likely to enjoy' vs. 'not likely' — that is discrimination, not generation.
Question 2
Which 2017 architectural innovation allowed AI models to understand the relationship between words across long distances in text by evaluating all words simultaneously?
The Transformer (introduced in 'Attention Is All You Need') uses Self-Attention to evaluate every word's relationship with every other word simultaneously — solving the 'forgotten beginning' problem of earlier sequential models.
Question 3
During which LLM training phase do human trainers rank AI responses to teach the model to be helpful, polite, and safe?
RLHF (fine-tuning) is where human trainers evaluate and rank model outputs to reinforce helpful, safe behavior. Pre-training is the unsupervised 'fill in the blank' phase on raw internet data.
Full course includes 5 modules, 120 practice questions, and 4 field missions.
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