Keep the curated tables in OneLake and use compatible semantic-model access modes. is an approach for a different requirement: OneLake is the common physical analytics storage used by Fabric workloads. In the present case, store selection should follow workload latency and query characteristics rather than one-size-fits-all storage.

Evaluate Direct Lake over supported OneLake Delta tables, with capacity and security requirements checked. is an approach for a different requirement: Direct Lake loads selected Delta columns into a VertiPaq cache without a full Import processing cycle. In the present case, store selection should follow workload latency and query characteristics rather than one-size-fits-all storage.

Choose a Fabric warehouse as the SQL-oriented analytical store. is an approach for a different requirement: A warehouse provides relational SQL capabilities and structured analytical tables in Fabric. In the present case, store selection should follow workload latency and query characteristics rather than one-size-fits-all storage.

Use KQL summarize count() by DeviceId followed by top ordering. is an approach for a different requirement: KQL summarize groups event records and top limits ordered results. In the present case, kQL supports time-aware event exploration and summarize aggregations on KQL data.

Write a T-SQL query using SUM and GROUP BY Region with the appropriate filters. is an approach for a different requirement: SQL grouping computes measures over the chosen set of grouping keys. In the present case, kQL supports time-aware event exploration and summarize aggregations on KQL data.

Use Fabric's Visual query editor for the supported select, filter and aggregate operations. is an approach for a different requirement: The visual editor represents common relational operations without requiring typed SQL. In the present case, kQL supports time-aware event exploration and summarize aggregations on KQL data.

Keep the curated tables in OneLake and use compatible semantic-model access modes. is an approach for a different requirement: OneLake is the common physical analytics storage used by Fabric workloads. In the present case, store selection should follow workload latency and query characteristics rather than one-size-fits-all storage.

Evaluate Direct Lake over supported OneLake Delta tables, with capacity and security requirements checked. is an approach for a different requirement: Direct Lake loads selected Delta columns into a VertiPaq cache without a full Import processing cycle. In the present case, store selection should follow workload latency and query characteristics rather than one-size-fits-all storage.

Choose a Fabric warehouse as the SQL-oriented analytical store. is an approach for a different requirement: A warehouse provides relational SQL capabilities and structured analytical tables in Fabric. In the present case, store selection should follow workload latency and query characteristics rather than one-size-fits-all storage.

That observation would verify a different goal: The model reports the correct current data and telemetry shows the expected Direct Lake execution path. It does not demonstrate the specific required result: The same event batch can be queried in Eventhouse and accessed by an approved downstream lakehouse path.

That observation would verify a different goal: Historical queries and minute-by-minute event queries each meet their respective latency needs. It does not demonstrate the specific required result: The same event batch can be queried in Eventhouse and accessed by an approved downstream lakehouse path.

That observation would verify a different goal: A new committed source value appears in a model query without an Import refresh. It does not demonstrate the specific required result: The same event batch can be queried in Eventhouse and accessed by an approved downstream lakehouse path.

Set up an authorized connection with approved credentials or identity before attempting transforms. is an approach for a different requirement: Transformation logic cannot make up for a missing data-source permission boundary. In the present case, connections represent managed access to a source rather than putting secrets in every query.

Inspect and repair the underlying Fabric connection and its source access before changing report visuals. is an approach for a different requirement: Connection identity and endpoint validity control whether fresh source data can be retrieved. In the present case, connections represent managed access to a source rather than putting secrets in every query.

Use OneLake catalog discovery and inspect item endorsements and permissions. is an approach for a different requirement: Catalog discovery exposes accessible organizational data assets without first moving their contents. In the present case, connections represent managed access to a source rather than putting secrets in every query.

That observation would verify a different goal: An authorized T-SQL procedure updates staged data and a reporting view returns intended rows. It does not demonstrate the specific required result: Two authorized semantic models query the curated Delta rows without manual file copies.

That observation would verify a different goal: A new committed source value appears in a model query without an Import refresh. It does not demonstrate the specific required result: Two authorized semantic models query the curated Delta rows without manual file copies.

That observation would verify a different goal: The analyst's SELECT statement returns the correct curated rows without editing Delta files. It does not demonstrate the specific required result: Two authorized semantic models query the curated Delta rows without manual file copies.

That observation would verify a different goal: A new ingestion run succeeds under the correct identity and shows recently added source records. It does not demonstrate the specific required result: The correct endorsed dataset is found and can be opened under the analyst's permissions.

That observation would verify a different goal: The desired active stream appears in the hub and can be selected for an authorized operation. It does not demonstrate the specific required result: The correct endorsed dataset is found and can be opened under the analyst's permissions.

That observation would verify a different goal: An analyst locates both the governed lakehouse item and the required available stream. It does not demonstrate the specific required result: The correct endorsed dataset is found and can be opened under the analyst's permissions.

That observation would verify a different goal: The corrected pipeline accepts both expected old and new rows without silent column loss. It does not demonstrate the specific required result: An analyst locates both the governed lakehouse item and the required available stream.

That observation would verify a different goal: The correct endorsed dataset is found and can be opened under the analyst's permissions. It does not demonstrate the specific required result: An analyst locates both the governed lakehouse item and the required available stream.

That observation would verify a different goal: The desired active stream appears in the hub and can be selected for an authorized operation. It does not demonstrate the specific required result: An analyst locates both the governed lakehouse item and the required available stream.

Deduplicate using the composite business key and a deterministic source-priority rule. is an approach for a different requirement: Business-key deduplication addresses logical duplicates that may differ in nonkey attributes. In the present case, a one-to-many relationship needs unique key values in its one-side dimension.

Use a conformed date dimension related to the relevant fact date keys. is an approach for a different requirement: Shared dimensions provide consistent slicing across multiple facts when relationships are correctly modeled. In the present case, a one-to-many relationship needs unique key values in its one-side dimension.

Define one fact row per order line and document its business keys and measures. is an approach for a different requirement: Explicit fact grain prevents ambiguous aggregations and duplicate relationships. In the present case, a one-to-many relationship needs unique key values in its one-side dimension.

Design a fact table with foreign keys to conformed dimensions in a star schema. is an approach for a different requirement: A star schema separates measurable facts from descriptive dimension attributes to clarify grain and filtering. In the present case, bridge tables represent many-to-many associations while preserving a defined relationship model.

Join history using business key and applicable effective-date interval, respecting the desired grain. is an approach for a different requirement: Temporal joins require valid time predicates to prevent duplicate version matches. In the present case, bridge tables represent many-to-many associations while preserving a defined relationship model.

Use an appropriate unknown member or left join strategy while separately fixing invalid keys. is an approach for a different requirement: Missing dimension relationships must not silently erase facts from financial totals. In the present case, bridge tables represent many-to-many associations while preserving a defined relationship model.

That observation would verify a different goal: All returned records satisfy the date requirement and excluded rows are absent. It does not demonstrate the specific required result: All returned customer groups exceed the aggregate threshold.

That observation would verify a different goal: The returned product set matches independently evaluated totals at the chosen threshold. It does not demonstrate the specific required result: All returned customer groups exceed the aggregate threshold.

That observation would verify a different goal: A customer with three orders contributes one to the distinct customer measure. It does not demonstrate the specific required result: All returned customer groups exceed the aggregate threshold.