SQL AI in a Day — Lesson 1: SQL Data Foundations for AI Workloads

Lesson Overview

This lesson develops practical knowledge of SQL Data Foundations for AI Workloads within SQL AI in a Day.

Learners connect the concepts to implementation steps, operational evidence, troubleshooting decisions, and measurable outcomes.

Learning Objectives

Review relational structures, keys, data types, quality, and analytical requirements
Write SELECT, filter, sort, aggregate, and join queries for model-ready data
Identify missing, duplicate, inconsistent, biased, and sensitive data

Guided Demonstration

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

Hands-On Activity

Prepare and validate a compact dataset for an AI-oriented analytical scenario.

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 SQL Data Foundations for AI Workloads in a realistic scenario and support the result with documented evidence.