Free Practice Questions for Snowflake GES-C02 Certification

Define key AI and ML concepts including supervised, unsupervised, and reinforcement learning.

Explain the difference between generative AI and discriminative AI.

Describe common AI tasks such as classification, regression, clustering, and generation.

Outline the stages of the AI lifecycle: data preparation, model training, evaluation, deployment, and monitoring.

Explain MLOps principles and practices for managing AI models in production.

Identify challenges in AI model governance and versioning.

Discuss ethical considerations in AI including bias, fairness, transparency, and accountability.

Describe techniques for mitigating bias in AI models.

Explain the importance of explainability and interpretability in AI systems.

Describe Snowflake Cortex AI capabilities for building and deploying AI models.

Explain how to use Cortex AI for tasks such as forecasting, anomaly detection, and classification.

Demonstrate knowledge of Cortex AI functions and their integration with Snowflake data.

Define Snowflake Intelligence and its role in enabling AI-driven insights.

Explain how to use Snowflake Intelligence for natural language querying and automated insights.

Describe the integration of Snowflake Intelligence with other Snowflake features.

Explain the purpose and functionality of Cortex Code for AI application development.

Describe Model Context Protocol (MCP) and its use in connecting AI models to data sources.

Demonstrate how to use Cortex Code and MCP to build AI-powered applications.

Describe the use cases for AI_TRANSCRIBE, AI_REDACT, and AI_FILTER functions.

Explain how to apply these functions to process and transform data.

Demonstrate knowledge of syntax and parameters for each function.

Explain data preparation steps including cleaning, normalization, and feature engineering.

Describe techniques for handling missing data and outliers.

Demonstrate knowledge of data transformation using Snowflake SQL and functions.

Design and implement data pipelines for AI model training and inference.

Explain the use of Snowpipe, tasks, and streams for continuous data loading.

Describe best practices for data versioning and lineage in AI pipelines.

Define data quality metrics and validation techniques for AI datasets.

Explain data governance frameworks and their application in AI projects.

Describe Snowflake features for data masking, tagging, and access control.

Explain methods for deploying AI models within Snowflake, including using external functions and Snowpark.

Describe the process of registering and invoking models in Snowflake.

Demonstrate knowledge of model serving and inference options.

Describe patterns for integrating AI models with applications using APIs and connectors.

Explain how to use Snowflake drivers and SDKs for AI integration.

Discuss considerations for real-time vs. batch inference.

Explain security best practices for AI data and models in Snowflake.

Describe encryption, access controls, and network policies for AI workloads.

Discuss compliance requirements for AI data handling.

Describe techniques for monitoring AI model performance and drift.

Explain governance processes for model approval, auditing, and documentation.

Demonstrate knowledge of Snowflake features for tracking model usage and lineage.