Snowflake SNOWPROⓇ SPECIALTY:GEN AI Certification Practice 300 Questions & Answer: Includes Comprehensive Answer Explanations and Conceptual Insights
About this ebook
Guide Overview This study guide is structured to provide a clear roadmap for understanding Generative AI concepts within the Snowflake ecosystem. It details the specific Snowflake topics and subtopics covered on the exam, complemented by additional resources such as documentation, blogs, and exercises to deepen your understanding. The estimated study time to complete the guide is between 10 to 13 hours, with the understanding that the value of specific links may vary based on individual experience.
Target Audience The SnowPro Specialty: Gen AI Certification Beta Exam is specifically designed for professionals with one or more years of Gen AI experience in an enterprise environment, particularly within Snowflake. Successful candidates are expected to possess advanced proficiency in Python coding, alongside assumed knowledge of data engineering and SQL. This exam is ideal for:
AI or ML Engineers
Data Scientists
Data Engineers
Data Application Developers
Data Analysts with programming experience
Prerequisites To be eligible for the Specialty: Gen AI Certification Beta Exam, candidates must hold an active SnowPro Associate: Platform or SnowPro Core Certification in good standing.
Exam Content and Format The SnowPro Specialty: Gen AI Certification Beta Exam rigorously tests specialized knowledge, skills, and best practices for leveraging Gen AI methodologies within Snowflake. The assessment includes scenario-based questions, interactive questions, and real-world examples to evaluate a candidate's ability to:
Define and implement Snowflake Gen AI principles, capabilities, and best practices concerning infrastructure, data governance, and cost governance.
Leverage Snowflake Cortex AI features, Large Language Models (LLMs), and offerings to address customer use cases, including Cortex Analyst, Cortex Search, Cortex Fine-tuning, and Snowflake Copilot.
Build open-source models using Snowpark Container Services and Snowflake Model Registry, such as those from Hugging Face.
Utilize Document AI to train and troubleshoot models tailored to specific customer requirements.
Key Knowledge Areas Candidates are expected to possess in-depth knowledge of:
The Snowflake Cortex suite of Gen AI features and their underlying models.
Retrieval Augmented Generation (RAG) applications that leverage LLMs.
