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This action is appropriate when an operations team needs a parameterized sequence of supported SQL updates run as one controlled operation. Stored procedures encapsulate supported parameterized T-SQL operations. However, the question instead requires Create a supported SQL view over the curated warehouse tables. because views centralize a reusable query definition without manually copying results.

This action is appropriate when a developer writes a view and assumes its definition guarantees a materialized daily snapshot. A logical view generally runs a query against underlying data rather than storing a scheduled copy. However, the question instead requires Create a supported SQL view over the curated warehouse tables. because views centralize a reusable query definition without manually copying results.

This action is appropriate when a finance calculation returns a scalar tax category based on customer region in many SQL queries. Reusable functions can package common deterministic expressions for SQL consumers. However, the question instead requires Create a supported SQL view over the curated warehouse tables. because views centralize a reusable query definition without manually copying results.

This action is appropriate when a report needs every customer row even when no sales orders exist. A left join preserves unmatched rows from the left side. However, the question instead requires Use an equijoin on the validated dimension surrogate key. because key-based joins enrich facts without depending on unstable descriptive text.

This action is appropriate when a query joins order lines to customers using only a nonunique city name and duplicates measures. Joining on a nonunique descriptive column can create many-to-many row multiplication. However, the question instead requires Use an equijoin on the validated dimension surrogate key. because key-based joins enrich facts without depending on unstable descriptive text.

This action is appropriate when an analyst wants only orders with a corresponding eligible customer row, excluding unmatched orders. Inner joins return only records satisfying the matching predicate. However, the question instead requires Use an equijoin on the validated dimension surrogate key. because key-based joins enrich facts without depending on unstable descriptive text.

This action is appropriate when a customer code must be mapped to an approved category managed in a small reference table. Reference tables centralize frequently changed business mappings. However, the question instead requires Add an explicit fiscal period column based on the approved business calendar. because fiscal logic must follow the organization's calendar rather than default calendar-year labels.

This action is appropriate when a business wants normalized sales currency in one measure but the exchange-rate timestamp varies by transaction. Currency enrichment requires time-aligned exchange rates rather than one current rate for all history. However, the question instead requires Add an explicit fiscal period column based on the approved business calendar. because fiscal logic must follow the organization's calendar rather than default calendar-year labels.

This action is appropriate when an order feed contains net amount and tax rate, but the gold model needs gross amount. Enrichment can create business attributes from validated source values. However, the question instead requires Add an explicit fiscal period column based on the approved business calendar. because fiscal logic must follow the organization's calendar rather than default calendar-year labels.

This action is appropriate when a developer combines all customer and product descriptive attributes into the billion-row sales fact. Separating dimensions can reduce repeated attributes and improve model clarity. However, the question instead requires Model sales as a fact table with product, date and region dimensions. because a star schema separates measurable events from descriptive filter attributes.

This action is appropriate when a sales fact contains a customer business key but the slowly changing customer dimension has multiple historical versions. Historical facts must point to the appropriate dimensional state at event time. However, the question instead requires Model sales as a fact table with product, date and region dimensions. because a star schema separates measurable events from descriptive filter attributes.

This action is appropriate when a report needs order header totals and individual line-level revenue in one fact with ambiguous grain. Mixing header and line grains creates double counting and unreliable relationships. However, the question instead requires Model sales as a fact table with product, date and region dimensions. because a star schema separates measurable events from descriptive filter attributes.

This action is appropriate when a late-arriving transaction references an earlier customer state no longer marked current. Temporal lookup protects historical fact attribution. However, the question instead requires Implement a Type 2 history design with effective dates and surrogate keys. because type 2 preserves prior attribute versions for point-in-time reporting.

This action is appropriate when a Type 2 dimension refresh creates several active rows for the same business key. Nonoverlapping validity windows are essential for stable historical lookups. However, the question instead requires Implement a Type 2 history design with effective dates and surrogate keys. because type 2 preserves prior attribute versions for point-in-time reporting.

This action is appropriate when a product's spelling is corrected and historic reports should use the corrected label rather than preserve the typo. Type 1 replaces an attribute where old values are not analytically meaningful. However, the question instead requires Implement a Type 2 history design with effective dates and surrogate keys. because type 2 preserves prior attribute versions for point-in-time reporting.

This action is appropriate when an engineer proposes flattening every customer, product and supplier attribute into each event row. Denormalization trades simpler reads for higher storage and maintenance cost. However, the question instead requires Consider selectively denormalizing stable lookup attributes into the serving table. because materializing a small repeated lookup may reduce read-time joins where justified.

This action is appropriate when an aggregated reporting table contains duplicated category labels after a source mapping update. Cached descriptive columns need change-propagation rules. However, the question instead requires Consider selectively denormalizing stable lookup attributes into the serving table. because materializing a small repeated lookup may reduce read-time joins where justified.

This action is appropriate when a low-latency real-time report cannot afford repeated joins against a slow remote source. Prepared read models can trade transformation work for predictable query latency. However, the question instead requires Consider selectively denormalizing stable lookup attributes into the serving table. because materializing a small repeated lookup may reduce read-time joins where justified.

This action is appropriate when a report groups order-line amounts by customer and a join duplicates every line twice. Aggregating incorrect duplicated rows does not recover the true financial total. However, the question instead requires Build a daily store-level aggregate with documented grain and refresh semantics. because preaggregation reduces repeated scans for approved summary questions.

This action is appropriate when a KPI definition sums cumulative daily balances across dates and overstates month-end cash. Snapshot balances cannot generally be summed over time like transactional flows. However, the question instead requires Build a daily store-level aggregate with documented grain and refresh semantics. because preaggregation reduces repeated scans for approved summary questions.

This action is appropriate when a query computes AVG of per-store averages where stores have very different sales counts. Averaging averages without weighting yields incorrect aggregate statistics. However, the question instead requires Build a daily store-level aggregate with documented grain and refresh semantics. because preaggregation reduces repeated scans for approved summary questions.

This action is appropriate when customer records arrive with repeated identical email addresses but distinct legitimate customer IDs. Matching a nonunique attribute can incorrectly delete separate customers. However, the question instead requires Deduplicate or MERGE on stable order identifiers and source versions. because idempotent writes prevent retries from multiplying business events.

This action is appropriate when a table contains multiple revisions of an invoice and the latest revision must be retained. Version-aware deduplication distinguishes corrections from accidental duplicates. However, the question instead requires Deduplicate or MERGE on stable order identifiers and source versions. because idempotent writes prevent retries from multiplying business events.

This action is appropriate when a join expands ten thousand transactions to a hundred thousand rows unexpectedly. Cardinality diagnostics identify accidental many-to-many joins. However, the question instead requires Deduplicate or MERGE on stable order identifiers and source versions. because idempotent writes prevent retries from multiplying business events.

This action is appropriate when an INNER JOIN unexpectedly removes orders without a matching optional promotion row. Optional relationships should not discard unrelated valid facts. However, the question instead requires Use a source-specific COALESCE rule after validating that NULL means no discount. because missing values require business semantics before imputing zero.

This action is appropriate when a SQL predicate `Status <> 'Closed'` omits rows where Status is NULL. SQL three-valued logic excludes NULL comparisons unless handled explicitly. However, the question instead requires Use a source-specific COALESCE rule after validating that NULL means no discount. because missing values require business semantics before imputing zero.

This action is appropriate when a source lacks birth dates and an analyst replaces all NULL values with today's date. Inventing dates converts missing data into incorrect facts. However, the question instead requires Use a source-specific COALESCE rule after validating that NULL means no discount. because missing values require business semantics before imputing zero.

This action is appropriate when a pipeline stores order timestamps as untyped strings and later time filters sort incorrectly. Typed timestamps support correct ordering and time arithmetic. However, the question instead requires Parse the numeric format using explicit locale or conversion rules and flag rejects. because type conversion must be controlled for the actual source representation.

This action is appropriate when a financial computation uses binary floating point and rounding differences violate reconciliation tolerances. Decimal precision and scale matter for currency calculations. However, the question instead requires Parse the numeric format using explicit locale or conversion rules and flag rejects. because type conversion must be controlled for the actual source representation.

This action is appropriate when a customer identifier has leading zeros but an engineer converts it to INTEGER. Business keys are labels, not always numeric quantities. However, the question instead requires Parse the numeric format using explicit locale or conversion rules and flag rejects. because type conversion must be controlled for the actual source representation.

This action is appropriate when a Spark transformation filters by local server date instead of UTC event date and drops midnight transactions. Date filters need consistent event-time interpretation. However, the question instead requires Push an appropriate date filter before expensive joins and aggregation. because early predicate filtering reduces unnecessary input processing.

This action is appropriate when a dataflow uses a case-sensitive comparison but source statuses differ only by capitalization. String matching must reflect the source's expected case rules. However, the question instead requires Push an appropriate date filter before expensive joins and aggregation. because early predicate filtering reduces unnecessary input processing.

This action is appropriate when a report exposes restricted PII columns when analysts need only revenue totals. Column minimization reduces data movement but does not replace authorization. However, the question instead requires Push an appropriate date filter before expensive joins and aggregation. because early predicate filtering reduces unnecessary input processing.