SQL AI in a Day — Lesson 2: Feature Preparation and Analytical SQL

Learning Objectives

• Create derived features, categories, time windows, statistics, and reusable query logic
• Use subqueries, CTEs, windows, views, and controlled transformations
• Validate leakage, correctness, granularity, and reproducibility

Guided Demonstration

1. Review the requirement, environment, dependencies, roles, risks, and expected result.
2. Demonstrate the relevant configuration, development, analysis, migration, security, or support workflow.
3. Capture settings, outputs, logs, screenshots, code, reports, or test evidence.
4. Compare the observed result with the acceptance criteria and identify gaps.

Hands-On Activity

Build an analytical SQL feature dataset and document its assumptions.

• Document assumptions and the planned sequence before making changes.
• Complete the activity and preserve evidence of normal and exception behavior.
• Record issues, corrective actions, and reusable operational or development notes.

Scenario Analysis

• Identify business, user, data, security, compliance, and operational impacts.
• Compare alternative decisions and explain the selected approach.
• Define ownership, communication, rollback, support, and improvement actions.

Troubleshooting Focus

• Separate symptoms from causes and collect evidence before applying a fix.
• Check configuration, permissions, data, dependencies, capacity, connectivity, and recent changes.
• Validate service restoration, user experience, and prevention measures.

Validation Checklist

• The requirement and expected result are clear.
• The selected configuration, design, code, query, migration, or process is documented.
• Normal, boundary, failure, security, compliance, and operational conditions are reviewed.
• The result is supported by evidence and the next improvement is prioritized.

Lesson Outcome

Learners can explain and apply Feature Preparation and Analytical SQL in a realistic scenario and support the result with documented evidence.