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Select the correct supported source encoding and validate representative characters is useful when a file uses a different character encoding and book titles become unreadable, since wrong decoding can corrupt text while leaving row counts apparently correct; however, the actual requirement is a Power Query step references an outdated source column name, which calls for Update the broken column reference in the Applied Steps and verify schema.

The action Deduplicate or upsert using an authoritative edition and ISBN key solves the destination contains repeated books after retrying a failed load because repeated append after partial success creates duplicate catalog entries; it does not by itself solve a Power Query step references an outdated source column name, whereas Update the broken column reference in the Applied Steps and verify schema does.

Applying Replace all unreadable titles with the same placeholder and claim the catalog is repaired when a Power Query step references an outdated source column name would introduce a defect because uniform placeholders erase source meaning and identification; a direct solution is Update the broken column reference in the Applied Steps and verify schema.

The action Inspect the transformation step and mapped field reference for schema drift solves the transformation reports different totals after a source column was renamed because a renamed source field can break computed columns even if the input connection remains active; it does not by itself solve some bills are reversed and must subtract rather than add to revenue, whereas Preserve the adjustment sign and transaction type in the fact model does.

Although Parse amounts using explicit source locale and validation rules fits the separate issue that monetary amounts arrive in strings with two different locale-specific decimal conventions because blind text-to-decimal conversion can shift values or turn invalid strings into nulls, this case concerns some bills are reversed and must subtract rather than add to revenue and should be resolved with Preserve the adjustment sign and transaction type in the fact model.

The option Remove reversing transactions before computing revenue because they have negative values fails to meet some bills are reversed and must subtract rather than add to revenue, given that excluding reversals overstates financial performance; Preserve the adjustment sign and transaction type in the fact model is the correct scope of action.

Although Inspect shortcut-path and producer-target security for the actual reader fits the separate issue that a reader can browse the shortcut but query execution returns access denied because source authorization remains effective even when a consumer can see the shortcut object, this case concerns an external shortcut's stored authentication has expired and should be resolved with Repair its supported connection credential and verify source access.

Option Assess external-table query acceleration and caching within supported limits would focus on the separate issue that an Eventhouse shortcut query slows after the source data grows because external Delta reads may benefit from caching, but it does not repair missing source authorization, not the current requirement that an external shortcut's stored authentication has expired; the appropriate remedy is Repair its supported connection credential and verify source access.

Disable OneLake security on the source for every consumer to bypass one access problem would not satisfy an external shortcut's stored authentication has expired because shared source data should remain governed under least-privilege identity controls; the defensible response is Repair its supported connection credential and verify source access.

Applying Join on the display name of a transformer and ignore its stable identifier when a high-cardinality customer attribute is not required for downstream metrics would introduce a defect because names can be reused or changed and are not reliable keys; a direct solution is Project only necessary columns before an expensive shuffle or join.

Option Validate and evolve the output schema deliberately before merging it into Delta would focus on the separate issue that the ingestion adds an unexpected nullable measurement field because uncontrolled schema evolution can produce inconsistent consumer expectations, not the current requirement that a high-cardinality customer attribute is not required for downstream metrics; the appropriate remedy is Project only necessary columns before an expensive shuffle or join.

Group by transformer ID and the correct hour bucket using an explicit max aggregate is useful when the team needs per-hour maximum load by transformer, since aggregation must use the event timestamp and the intended transformer grain; however, the actual requirement is a high-cardinality customer attribute is not required for downstream metrics, which calls for Project only necessary columns before an expensive shuffle or join.

The option Publish the secured historical Delta source to an anonymous endpoint to increase query speed fails to meet the event query shows unexpected duplicate transactions after replay, given that public sharing does not repair the query plan and violates the data-protection intent; Deduplicate by stable transaction event ID and source revision is the correct scope of action.

Review OneLake shortcut and target identity permissions is useful when a restricted analyst can query native events but not the source Delta shortcut, since native KQL access and external OneLake data authorization are separate paths; however, the actual requirement is the event query shows unexpected duplicate transactions after replay, which calls for Deduplicate by stable transaction event ID and source revision.

The action Evaluate shortcut query acceleration and relevant time predicates solves a KQL query is unexpectedly slow due to scanning external historical Delta data because external tables can require data fetches and lack native indexes without acceleration; it does not by itself solve the event query shows unexpected duplicate transactions after replay, whereas Deduplicate by stable transaction event ID and source revision does.

The option Inspect schema/mapping errors and update the tested destination column mapping applies to the separate issue that an API response schema changes and Copy maps the wrong fields, because connector success does not guarantee that a changed payload still matches the target contract; the current condition is the partner API paginates results and a single request omits most entries, which calls for Use supported pagination or controlled page iteration with completion checks.

The action Apply bounded retry and throughput controls that honor the source rate limit solves the source limits requests per minute and the pipeline receives rate-limit errors because aggressive unbounded requests can worsen throttling and missing data; it does not by itself solve the partner API paginates results and a single request omits most entries, whereas Use supported pagination or controlled page iteration with completion checks does.

Applying Put the partner's production bearer token in plaintext pipeline run descriptions when the partner API paginates results and a single request omits most entries would introduce a defect because operational logs and comments are not approved secret storage; a direct solution is Use supported pagination or controlled page iteration with completion checks.

Correct supported pagination and verify source-versus-sink record counts is useful when the pipeline copies only the first page of a paginated API, since partial pagination produces a business-data gap even though a first request returned successfully; however, the actual requirement is the source login has expired and connection testing fails, which calls for Renew the approved connection credentials and retest the connector.

For retries duplicate destination data after a partial successful copy, Reconcile committed batch keys and implement idempotent retry writes can be justified because a pipeline retry can repeat work already accepted by the destination; for this scenario involving the source login has expired and connection testing fails, the targeted response is Renew the approved connection credentials and retest the connector.

The option Set source and destination schemas to ignore every data type error without audit fails to meet the source login has expired and connection testing fails, given that type errors may represent malformed business data and should be validated or quarantined; Renew the approved connection credentials and retest the connector is the correct scope of action.

Choosing Remove leading zeros from postal codes because SQL comparison is faster on integers would not satisfy the case condition (multiple spellings of the same street need an approved canonical mapping), because postal codes are categorical IDs and may require their original exact text; Apply governed reference-table normalization before aggregate grouping preserves the intended result.

For a query includes blank postal codes and mistakenly assigns them to one district, Preserve missingness and route unmatched addresses for validation can be justified because assigning arbitrary districts to null postal codes produces false location statistics; for this scenario involving multiple spellings of the same street need an approved canonical mapping, the targeted response is Apply governed reference-table normalization before aggregate grouping.

Although Use a district-level GROUP BY with a correctly defined distinct household key fits the separate issue that analysts need an aggregate count of distinct households by district because raw row count is not the same as unique household count after multiple address events, this case concerns multiple spellings of the same street need an approved canonical mapping and should be resolved with Apply governed reference-table normalization before aggregate grouping.

For the dataflow fails due to its expired connection identity, Repair the effective credentials and validate all source connections can be justified because refresh scheduling does not replace connection authorization; for this scenario involving the dataflow outputs only half the planned rows after a canceled run, the targeted response is Reconcile partial sink state and rerun idempotently where necessary.

Option Revisit the Power Query column projection and destination schema mapping would focus on the separate issue that the source adds a mandatory field but existing transformations discard it because a stale select-columns step can drop new required attributes silently, not the current requirement that the dataflow outputs only half the planned rows after a canceled run; the appropriate remedy is Reconcile partial sink state and rerun idempotently where necessary.

For this requirement of the dataflow outputs only half the planned rows after a canceled run, Assume every completed query preview implies the dataflow has written all output rows would be unsafe or ineffective because the actual write/refresh status must be checked separately; apply Reconcile partial sink state and rerun idempotently where necessary instead.

Although Aggregate to the required account grain before joining to one-row dimensions fits the separate issue that multiple source rows per account produce inflated account-level totals because resolving grain mismatch prevents fan-out and double counting, this case concerns historical balances must be tracked separately from transaction movements and should be resolved with Create distinct snapshot and transaction fact grains with documented aggregation rules.

Option Apply a reviewed SQL CASE expression with explicit thresholds and null handling would focus on the separate issue that the analysis requires conditional business classes from numeric risk values because conditional SQL maps validated source values to documented business categories, not the current requirement that historical balances must be tracked separately from transaction movements; the appropriate remedy is Create distinct snapshot and transaction fact grains with documented aggregation rules.

Choosing Replace all missing risk scores with the highest score and present them as measured values would not satisfy the case condition (historical balances must be tracked separately from transaction movements), because an invented measurement is not a validated source fact; Create distinct snapshot and transaction fact grains with documented aggregation rules preserves the intended result.

For this requirement of spark stages run slowly after a DataFrame join introduces a single hot key, Disable all partition-level task retries because one executor failed would be unsafe or ineffective because removing fault recovery does not fix a bad library or incorrect target; apply Inspect the join's skewed partitions and choose an appropriate mitigation instead.

Coordinate conflicting writes and inspect Delta commit conflict diagnostics is useful when an output Delta transaction fails after multiple jobs write the same table, since concurrent writes require valid conflict resolution, not disabled transaction logs; however, the actual requirement is spark stages run slowly after a DataFrame join introduces a single hot key, which calls for Inspect the join's skewed partitions and choose an appropriate mitigation.

The action Verify the notebook's attached lakehouse and fully qualified output path solves a notebook writes data to an unintended development lakehouse because default item context can direct writes away from the intended production target; it does not by itself solve spark stages run slowly after a DataFrame join introduces a single hot key, whereas Inspect the join's skewed partitions and choose an appropriate mitigation does.