GENERATIVE AI LEADER
CERTIFICATION EXAM STUDY GUIDE
QUESTIONS AND ANSWERS 2026
VERIFIED.
Generative AI - ANS An application of ML that focuses on creating new content.
Artificial intelligence (AI) - ANS Building machines that can perform tasks that typically
require human intelligence, such as learning, problem-solving, and decision-making.
Machine learning (ML) - ANS A subfield of AI where machines learn from data to perform
specific tasks.
Deep learning - ANS A subset of ML that uses artificial neural networks with many layers to
extract complex patterns from data.
Foundation models - ANS Powerful ML models trained on massive amounts of unlabeled
data, allowing them to develop a broad understanding of the world.
Large language models (LLMs) - ANS A type of foundation model that is designed to
understand and generate human language.
Labeled data - ANS Data that has associated tags, such as a name, type, or number.
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, Unlabeled data - ANS Raw, unprocessed information that hasn't been tagged and lacks
meaning by itself such as unorganized photos or streams of audio recordings.
Prompting - ANS The method of interacting with foundation models and guiding them by
providing instructions or inputs to generate desired outputs.
Supervised learning - ANS Trains models on labeled data to predict outputs for new inputs.
Unsupervised learning - ANS Uses unlabeled data to find natural groupings and patterns.
Reinforcement learning - ANS Learns through interaction and feedback to maximize rewards
and minimize penalties.
Prompt engineering - ANS 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 - ANS The process involving data ingestion, preparation, model training, and
evaluation.
Data ingestion and preparation - ANS The process of collecting, cleaning, and transforming
raw data into a usable format for analysis or model training.
Key features of foundation models - ANS Trained on diverse data, flexible to a wide range of
use cases, and adaptable to specialized domains through additional, targeted training.
Generative AI applications - ANS Can summarize, discover, automate, create, and generate
new content.
Exam focus areas - ANS 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).
@COPYRIGHT ALL RIGHTS RESERVED PAGE 2 OF 14
CERTIFICATION EXAM STUDY GUIDE
QUESTIONS AND ANSWERS 2026
VERIFIED.
Generative AI - ANS An application of ML that focuses on creating new content.
Artificial intelligence (AI) - ANS Building machines that can perform tasks that typically
require human intelligence, such as learning, problem-solving, and decision-making.
Machine learning (ML) - ANS A subfield of AI where machines learn from data to perform
specific tasks.
Deep learning - ANS A subset of ML that uses artificial neural networks with many layers to
extract complex patterns from data.
Foundation models - ANS Powerful ML models trained on massive amounts of unlabeled
data, allowing them to develop a broad understanding of the world.
Large language models (LLMs) - ANS A type of foundation model that is designed to
understand and generate human language.
Labeled data - ANS Data that has associated tags, such as a name, type, or number.
@COPYRIGHT ALL RIGHTS RESERVED PAGE 1 OF 14
, Unlabeled data - ANS Raw, unprocessed information that hasn't been tagged and lacks
meaning by itself such as unorganized photos or streams of audio recordings.
Prompting - ANS The method of interacting with foundation models and guiding them by
providing instructions or inputs to generate desired outputs.
Supervised learning - ANS Trains models on labeled data to predict outputs for new inputs.
Unsupervised learning - ANS Uses unlabeled data to find natural groupings and patterns.
Reinforcement learning - ANS Learns through interaction and feedback to maximize rewards
and minimize penalties.
Prompt engineering - ANS 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 - ANS The process involving data ingestion, preparation, model training, and
evaluation.
Data ingestion and preparation - ANS The process of collecting, cleaning, and transforming
raw data into a usable format for analysis or model training.
Key features of foundation models - ANS Trained on diverse data, flexible to a wide range of
use cases, and adaptable to specialized domains through additional, targeted training.
Generative AI applications - ANS Can summarize, discover, automate, create, and generate
new content.
Exam focus areas - ANS 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).
@COPYRIGHT ALL RIGHTS RESERVED PAGE 2 OF 14
