GENERATIVE AI LEADER CERTIFICATION EXAM STUDY GUIDE
QUESTIONS AND CORRECT ANSWERS!!!! ALREADY GRADED A+
Generative AI - (answer)An application of ML that focuses on creating new content.
Artificial intelligence (AI) - (answer)Building machines that can perform tasks that typically require
human intelligence, such as learning, problem-solving, and decision-making.
Machine learning (ML) - (answer)A subfield of AI where machines learn from data to perform specific
tasks.
Deep learning - (answer)A subset of ML that uses artificial neural networks with many layers to extract
complex patterns from data.
Foundation models - (answer)Powerful ML models trained on massive amounts of unlabeled data,
allowing them to develop a broad understanding of the world.
Large language models (LLMs) - (answer)A type of foundation model that is designed to understand and
generate human language.
Labeled data - (answer)Data that has associated tags, such as a name, type, or number.
Unlabeled data - (answer)Raw, unprocessed information that hasn't been tagged and lacks meaning by
itself such as unorganized photos or streams of audio recordings.
Prompting - (answer)The method of interacting with foundation models and guiding them by providing
instructions or inputs to generate desired outputs.
Supervised learning - (answer)Trains models on labeled data to predict outputs for new inputs.
Unsupervised learning - (answer)Uses unlabeled data to find natural groupings and patterns.
, GENERATIVE AI LEADER CERTIFICATION EXAM STUDY GUIDE
QUESTIONS AND CORRECT ANSWERS!!!! ALREADY GRADED A+
Reinforcement learning - (answer)Learns through interaction and feedback to maximize rewards and
minimize penalties.
Prompt engineering - (answer)The art and science of creating effective inputs, known as prompts, for
generative AI models to maximize their value and tailor responses to specific needs.
ML lifecycle - (answer)The process involving data ingestion, preparation, model training, and evaluation.
Data ingestion and preparation - (answer)The process of collecting, cleaning, and transforming raw data
into a usable format for analysis or model training.
Key features of foundation models - (answer)Trained on diverse data, flexible to a wide range of use
cases, and adaptable to specialized domains through additional, targeted training.
Generative AI applications - (answer)Can summarize, discover, automate, create, and generate new
content.
Exam focus areas - (answer)Fundamentals of generative AI (~30% of the exam), Google Cloud's
generative AI offerings (~35% of the exam), Techniques to improve gen AI model output (~20% of the
exam), Business strategies for a successful gen AI solution (~15% of the exam).
Structured data - (answer)Data that is organized and easy to search, often stored in relational databases.
Model training - (answer)The process of creating your ML model using data.
Model deployment - (answer)The process of making a trained model available for use.
Unstructured data - (answer)Data that lacks a predefined structure and requires sophisticated analysis
techniques.
QUESTIONS AND CORRECT ANSWERS!!!! ALREADY GRADED A+
Generative AI - (answer)An application of ML that focuses on creating new content.
Artificial intelligence (AI) - (answer)Building machines that can perform tasks that typically require
human intelligence, such as learning, problem-solving, and decision-making.
Machine learning (ML) - (answer)A subfield of AI where machines learn from data to perform specific
tasks.
Deep learning - (answer)A subset of ML that uses artificial neural networks with many layers to extract
complex patterns from data.
Foundation models - (answer)Powerful ML models trained on massive amounts of unlabeled data,
allowing them to develop a broad understanding of the world.
Large language models (LLMs) - (answer)A type of foundation model that is designed to understand and
generate human language.
Labeled data - (answer)Data that has associated tags, such as a name, type, or number.
Unlabeled data - (answer)Raw, unprocessed information that hasn't been tagged and lacks meaning by
itself such as unorganized photos or streams of audio recordings.
Prompting - (answer)The method of interacting with foundation models and guiding them by providing
instructions or inputs to generate desired outputs.
Supervised learning - (answer)Trains models on labeled data to predict outputs for new inputs.
Unsupervised learning - (answer)Uses unlabeled data to find natural groupings and patterns.
, GENERATIVE AI LEADER CERTIFICATION EXAM STUDY GUIDE
QUESTIONS AND CORRECT ANSWERS!!!! ALREADY GRADED A+
Reinforcement learning - (answer)Learns through interaction and feedback to maximize rewards and
minimize penalties.
Prompt engineering - (answer)The art and science of creating effective inputs, known as prompts, for
generative AI models to maximize their value and tailor responses to specific needs.
ML lifecycle - (answer)The process involving data ingestion, preparation, model training, and evaluation.
Data ingestion and preparation - (answer)The process of collecting, cleaning, and transforming raw data
into a usable format for analysis or model training.
Key features of foundation models - (answer)Trained on diverse data, flexible to a wide range of use
cases, and adaptable to specialized domains through additional, targeted training.
Generative AI applications - (answer)Can summarize, discover, automate, create, and generate new
content.
Exam focus areas - (answer)Fundamentals of generative AI (~30% of the exam), Google Cloud's
generative AI offerings (~35% of the exam), Techniques to improve gen AI model output (~20% of the
exam), Business strategies for a successful gen AI solution (~15% of the exam).
Structured data - (answer)Data that is organized and easy to search, often stored in relational databases.
Model training - (answer)The process of creating your ML model using data.
Model deployment - (answer)The process of making a trained model available for use.
Unstructured data - (answer)Data that lacks a predefined structure and requires sophisticated analysis
techniques.
