[NEW] Google Cloud Generative AI Leader - 6 Full Mock Exams By Vladimir Raykov

Name: [NEW] Google Cloud Generative AI Leader - 6 Full Mock Exams
Brand: Vladimir Raykov

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Generative AI · Online Course

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[NEW] Google Cloud Generative AI Leader - 6 Full Mock Exams · Program

About this program

[NEW] Google Cloud Generative AI Leader - 6 Full Mock Exams

Online Course

Learning format

Generative AI

Subcategory

English

Course language

$19.99 (list $9.99)

Price

Price may change · updated within 1–2 weeks

udemy.com

Official program page

What you'll learn in [NEW] Google Cloud Generative AI Leader - 6 Full Mock Exams

Define core Generative AI concepts and terminology.
Differentiate between supervised, unsupervised, and reinforcement learning.
Identify key stages of the machine learning lifecycle.
Explain characteristics of structured vs. unstructured data.
Recognize the importance of data quality and accessibility.
Choose appropriate foundation models for business needs.
Describe Google Cloud's AI-first approach and innovation.
Explain Google Cloud's enterprise-ready AI platform features.
Identify Google Cloud's AI-optimized infrastructure components.
Understand Google Cloud's comprehensive AI ecosystem.
Recognize the benefits of Google Cloud's open approach to AI.
Identify Google Cloud's prebuilt AI offerings for AI-powered work.
Explain Vertex AI Search for enterprise search and recommendations.
Understand Google's Customer Engagement Suite capabilities.
Utilize Vertex AI Platform for building and deploying ML models.
Describe Vertex AI Agent Builder for custom AI agents.
Determine when to use Google AI Studio vs. Vertex AI Studio.
Define the purpose and types of tooling for Gen AI agents.
Identify common limitations of foundation models (e.g., bias, hallucinations).
Apply prompt engineering techniques for improved AI outputs.
Explain the concept of grounding LLMs with various data types.
Describe how Retrieval-Augmented Generation (RAG) enhances model output.
Control AI model behavior using sampling parameters (e.g., temperature, Top-P).
Explain Human-in-the-Loop (HITL) for effective model oversight.
Recognize Google Cloud practices for continuous model monitoring.
Identify key factors influencing Gen AI solution development.
Explain the importance of security throughout the ML lifecycle.
Describe privacy considerations like data anonymization.
Understand the importance of responsible AI, accountability, and explainability.

What's Included in [NEW] Google Cloud Generative AI Leader - 6 Full Mock Exams

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