GENERATIVE AI LEADER
CERTIFICATION EXAM STUDY
GUIDE
1. Generative AI
Answer An application of ML that focuses on creating new content.
2. Artificial intelligence (AI)
Answer Building machines that can perform tasks that typically require human intelli- gence, such as
learning, problem-solving, and decision-making.
3. Machine learning (ML)
Answer A subfield of AI where machines learn from data to perform specific tasks.
4. Deep learning
Answer A subset of ML that uses artificial neural networks with many layers to extract complex patterns from
data.
5. Foundation models
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,Answer Powerful ML models trained on massive amounts of unlabeled data, allowing them to develop a
broad understanding of the world.
6. Large language models (LLMs)
Answer A type of foundation model that is designed to understand and generate human language.
7. Labeled data
Answer Data that has associated tags, such as a name, type, or number.
8. 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.
9. Prompting
Answer The method of interacting with foundation models and guiding them by providing instructions or inputs
to generate desired outputs.
10. Supervised learning
Answer Trains models on labeled data to predict outputs for new inputs.
11. Unsupervised learning
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, Answer Uses unlabeled data to find natural groupings and patterns.
12. Reinforcement learning
Answer Learns through interaction and feedback to maximize rewards and minimize penalties.
13. Prompt engineering
Answer The art and science of creating ettective inputs, known as prompts, for generative AI models to
maximize their value and tailor responses to specific needs.
14. Data ingestion and preparation
Answer The process of collecting, cleaning, and transforming raw data into a usable format for analysis or
model training.
15. Gen AI applications
Answer Can be multimodal, enabling them to process and generate ditterent types of data like text,
images, and code simultaneously.
16. Exam focus
The exam assesses your knowledge in four key areas
Answer Fundamentals of generative AI (~30% of the exam), Google Cloud's generative AI otterings (~35% of the
3/
25
CERTIFICATION EXAM STUDY
GUIDE
1. Generative AI
Answer An application of ML that focuses on creating new content.
2. Artificial intelligence (AI)
Answer Building machines that can perform tasks that typically require human intelli- gence, such as
learning, problem-solving, and decision-making.
3. Machine learning (ML)
Answer A subfield of AI where machines learn from data to perform specific tasks.
4. Deep learning
Answer A subset of ML that uses artificial neural networks with many layers to extract complex patterns from
data.
5. Foundation models
1/
25
,Answer Powerful ML models trained on massive amounts of unlabeled data, allowing them to develop a
broad understanding of the world.
6. Large language models (LLMs)
Answer A type of foundation model that is designed to understand and generate human language.
7. Labeled data
Answer Data that has associated tags, such as a name, type, or number.
8. 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.
9. Prompting
Answer The method of interacting with foundation models and guiding them by providing instructions or inputs
to generate desired outputs.
10. Supervised learning
Answer Trains models on labeled data to predict outputs for new inputs.
11. Unsupervised learning
2/
25
, Answer Uses unlabeled data to find natural groupings and patterns.
12. Reinforcement learning
Answer Learns through interaction and feedback to maximize rewards and minimize penalties.
13. Prompt engineering
Answer The art and science of creating ettective inputs, known as prompts, for generative AI models to
maximize their value and tailor responses to specific needs.
14. Data ingestion and preparation
Answer The process of collecting, cleaning, and transforming raw data into a usable format for analysis or
model training.
15. Gen AI applications
Answer Can be multimodal, enabling them to process and generate ditterent types of data like text,
images, and code simultaneously.
16. Exam focus
The exam assesses your knowledge in four key areas
Answer Fundamentals of generative AI (~30% of the exam), Google Cloud's generative AI otterings (~35% of the
3/
25
