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SQL for Data Analysis and AI

Module 10 Knowledge Check

5 questions. Pass mark 4 out of 5. Answer every question before checking the answer key below, then retry after reading the feedback.

1. The single most useful reconciliation check after a join is…

2. Your total does not match the source system by 0.4%. The correct first move is…

3. A good sanity check for a revenue query is…

4. Edge cases worth testing before trusting a query include…

5. Why re-run validation after 'a small change' to a query?

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Answer Key and Explanations

Check these only after attempting every question.

1. B - Row count before versus after

An unintended row-count change is the signature of a grain change.

2. B - Find and explain the difference before publishing

An unexplained variance usually indicates filters, timezones, refunds, or status rules you have not accounted for.

3. B - That order count × plausible average order value is near the total

Magnitude checks catch order-of-magnitude errors that detailed review often misses.

4. B - Empty groups, NULLs, duplicates, boundary dates, and negative or refunded amounts

These are exactly the inputs that make otherwise correct SQL produce wrong reports.

5. B - Revisions can silently reintroduce a grain change or drop rows

Most reporting errors arrive in the final edit, after the careful review has already happened.

Practical Check

Apply this module to your own work: complete the module activity for *Query Validation: Reconciliation, Edge Cases, and Sanity Checks*, then write one sentence naming what your result shows and one naming what it does not.

Strong Answer Pattern

A strong answer names the task, the evidence used, the check performed, and the remaining limitation. It avoids "proved", "guaranteed", or "always" unless the evidence genuinely supports it.