GENERATIVE AI LEADER CERTIFICATION EXAM STUDY GUIDE (GENERATIVE
ARTIFICIAL INTELLIGENCE LEADER CERTIFICATION) WITH COMPLETE SOLUTIONS
100% VERIFIED!!
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 (GENERATIVE
ARTIFICIAL INTELLIGENCE LEADER CERTIFICATION) WITH COMPLETE SOLUTIONS
100% VERIFIED!!
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.
Data ingestion and preparation - (ANSWER)The process of collecting, cleaning, and transforming raw
data into a usable format for analysis or model training.
Gen AI applications - (ANSWER)Can be multimodal, enabling them to process and generate different
types of data like text, images, and code simultaneously.
Exam focus - (ANSWER)The exam assesses your knowledge in four key areas: 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).
Key features of foundation models - (ANSWER)Trained on diverse data, flexible to a wide range of use
cases, adaptable to specialized domains through additional, targeted training.
Generative AI capabilities - (ANSWER)Summarize, Discover, Automate, Create, Generate new content,
Condense information into concise summaries, Find information at the right time, Automate previously
manual tasks.
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.
ARTIFICIAL INTELLIGENCE LEADER CERTIFICATION) WITH COMPLETE SOLUTIONS
100% VERIFIED!!
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 (GENERATIVE
ARTIFICIAL INTELLIGENCE LEADER CERTIFICATION) WITH COMPLETE SOLUTIONS
100% VERIFIED!!
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.
Data ingestion and preparation - (ANSWER)The process of collecting, cleaning, and transforming raw
data into a usable format for analysis or model training.
Gen AI applications - (ANSWER)Can be multimodal, enabling them to process and generate different
types of data like text, images, and code simultaneously.
Exam focus - (ANSWER)The exam assesses your knowledge in four key areas: 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).
Key features of foundation models - (ANSWER)Trained on diverse data, flexible to a wide range of use
cases, adaptable to specialized domains through additional, targeted training.
Generative AI capabilities - (ANSWER)Summarize, Discover, Automate, Create, Generate new content,
Condense information into concise summaries, Find information at the right time, Automate previously
manual tasks.
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.
