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SCM Final
| Question | Answer |
|---|---|
| Missing months in merged time-series data distort metrics because: | Line charts cannot plot missing timestamps |
| Rewriting a RIGHT JOIN as an equivalent LEFT JOIN is possible because: | Every RIGHT JOIN can be rewritten as a LEFT JOIN by swapping table order and reversing the join direction |
| In Homework 6, when calculating average order value per customer, grouping was required. The grouping exists to: | Establish the granularity at which aggregated measures are meaningful |
| In the Coffee Cart assignment, discount caps were applied after identifying valid discounts. This ordering ensures that: | The cumulative discount respects business rules even when overlapping codes appear |
| If a WHERE clause filtering on the right table's columns is applied after a LEFT JOIN, it may nullify the effect of the LEFT JOIN. This occurs because: | WHERE conditions are evaluated after join rows are materialised, potentially filtering out NULL-extended rows |
| Interpolating quarterly data into monthly frequency introduces risk because: | It creates artificial structure that may be mistaken for genuine signal |
| A CASE expression is used in SQL when: | A query requires conditional classification or rule-based labelling |
| In Homework 4, each business request required selecting specific columns relevant to the manager's need. The conceptual reason for avoiding unnecessary columns is: | Superfluous columns obscure the analytical focus and introduce noise |
| Complex analyses used layered CTEs because CTEs: | Allow the analyst to partition logic into stable, inspectable intermediate relations |
| When computing KPIs directly in SQL, the choice to encode logic into computed columns reflects the principle that: | Business rules should be executed where the data resides to minimise movement and ensure consistency |
| In Homework 7, quartile thresholds were calculated using PERCENTILE_CONT rather than manually defining breakpoints. This ensures that: | Quartile boundaries emerge from the actual data distribution rather than arbitrary fixed values |
| When generating a customer–order–territory report in Homework 5, a LEFT JOIN was required to identify customers with no recent orders. If an INNER JOIN were used instead, the conceptual error would be that: | Customers without orders would be excluded entirely, undermining the retention analysis |
| Distinguishing warnings from errors in the Coffee Cart pipeline reflects that: | A robust pipeline separates structural violations from semantic irregularities |
| In Homework 5, you constructed a report listing all employees, including those without department assignments. Which join ensures such employees remain in the output? | LEFT JOIN |
| The NTILE(4) function was used to classify territories into quartiles. Conceptually, NTILE ensures that: | Rows are distributed as evenly as possible across the specified number of groups |
| When merging series of different frequencies in Homework 8, the integration step required normalising to a common temporal frequency because: | Without a uniform frequency, temporal joins produce structurally ambiguous rows |
| Aggregate functions such as SUM or AVG are essential in business reporting because they: | Summarise granular transactional data into meaningful metrics |
| Analytic pipelines treat input data as untrusted because: | Analytical correctness depends on data integrity and provenance |
| In the Coffee Cart assignment, taxes were extracted from the net amount using a tax multiplier (1.105) rather than applying a percentage to the pre-tax base. This approach is necessary because: | The prices already include embedded taxes, so the pre-tax base must be derived by division |
| In Homework 9, the caching logic refreshed entries older than seven days. The conceptual justification is that: | Without date-based invalidation, the cache risks diverging from the evolving state of the underlying API |
| An INNER JOIN on two tables excludes rows when: | Rows in one table have no matching value in the join column of the other table |
| Linking cached reports to the agencies table via a foreign key is conceptually sound because: | It ties cached artefacts to canonical domain entities, preventing drift and duplication |
| In Homework 3, sub-questions were required to build logically toward the primary research question. This hierarchical structure ensures that: | The analysis forms a coherent narrative where incremental findings synthesise into a defensible conclusion |
| When combining indicators measured at different aggregation levels (e.g., metropolitan unemployment with national GDP), analysts must exercise caution because: | Temporal alignment does not imply analytical compatibility; comparing local and national measures risks ecological fallacy |
| Computing KPIs inside SQL rather than in application code preserves correctness because: | Transformations performed where data reside preserve grain, reduce movement, and prevent misalignment |
| Before merging unemployment, business formation, and FRED data, all date fields were normalised. The conceptual necessity for this is: | Consistent datetime formats prevent misalignment and ensure temporal comparability. |
| When identifying customers who may not have placed recent orders, the LEFT JOIN was applied before filtering for "no orders or orders in the last year." Why? | Filtering before the join would remove customers with no orders entirely |
| In the Coffee Cart assignment, lines with non-positive quantities were rejected because such entries: | Violate fundamental business constraints and would distort all revenue metrics |
| Functions such as DATEDIFF are essential when analysing tenure because they: | Functions such as DATEDIFF are essential when analysing tenure because they: |
| The caching logic in Homework 9 avoids re-requesting historical API data primarily because: | Repeated identical requests incur unnecessary latency and risk hitting rate limits |
Created by:
mfcaeroc