Model the Data

The correct approach is to pre-aggregate transaction records to daily summaries containing fraud counts, chargeback totals, and false positive metrics by merchant category and region.

This solution directly addresses the core problem: the semantic model contains 890 million individual transaction records with millisecond precision, but the fraud investigation team only analyzes data at daily, weekly, and monthly aggregation levels. By pre-aggregating to daily summaries, you dramatically reduce the data volume from hundreds of millions of rows to a manageable dataset while preserving all the analytical capabilities the team requires.

The key insight is matching the data granularity to the actual analytical needs. Since the team analyzes daily fraud incident counts, weekly chargeback ratios, and monthly false positive rates, storing millisecond-level detail provides no analytical value but creates massive performance overhead. Daily pre-aggregation allows all required metrics to be derived through simple DAX aggregations while reducing memory consumption and improving query performance significantly.

The option suggesting incremental refresh partitioning on the detailed transaction table misses the fundamental issue. Incremental refresh helps with data loading efficiency but does not address the root cause of performance problems - the excessive granularity of 890 million detailed records. The team does not need real-time fraud pattern detection at the authorization checkpoint level for their stated analytical requirements.

The suggestion to pre-aggregate to hourly summaries by individual merchant and geographic coordinate clusters is over-engineered. Hourly granularity is finer than needed (daily is sufficient), and aggregating by individual merchant and coordinate clusters would still result in an extremely large dataset that doesn't align with the team's focus on merchant category and regional processor level analysis.

The option proposing calculated columns with query folding optimization fundamentally misunderstands the solution. Calculated columns are computed and stored in memory, which would actually increase rather than decrease memory consumption. Additionally, calculated columns do not fold back to the source, so query folding would not apply here. This approach would worsen the 42GB memory problem.

The correct answer is that the table must be marked as a date table using Model view or through table properties with the Date column specified.

When using DAX time intelligence functions like PREVIOUSYEAR and DATEADD, Power BI requires the date table to be explicitly marked as a date table. This is a critical configuration step that tells Power BI which table and which column should be used for time intelligence calculations.

To mark a table as a date table in Power BI:
1. Select the date table in Model view or Data view
2. Go to Table tools > Mark as date table
3. Specify which column contains the dates

This step is separate from:
- Creating the table with CALENDAR()
- Adding calculated columns for Year, Month, etc.
- Establishing relationships with fact tables
- Having correct data types

Without this explicit marking, Power BI's time intelligence functions cannot properly identify and use the date table, even if all other requirements (contiguous dates, no duplicates, proper relationships) are met.

The other options are incorrect:

• Using HASONEVALUE function for date range validation is not a requirement for time intelligence functions. HASONEVALUE is a filter context function, not a date table configuration mechanism.

• Setting a primary key and indexing the Date column is not required for time intelligence functions in Power BI. While having unique values is important, explicit primary key designation and indexing are not prerequisites for marking a date table.

• Adding fiscal year boundary columns through Auto date/time settings is unrelated to this issue. Auto date/time is actually a separate feature that creates hidden date tables automatically, and fiscal year columns are optional enhancements, not requirements for time intelligence functions to work.

This question addresses a classic semi-additive measure challenge in Power BI, where headcount values cannot simply be summed across time periods but must instead reflect point-in-time snapshots.

The correct solution uses SUMX combined with LASTNONBLANK to iterate through each region and find the most recent non-blank headcount value within the selected time context.

Here's how the correct measure works:

1. SUMX(VALUES(Workforce_Snapshots[RegionID]), ...) - This iterates through each distinct region that exists within the current filter context

2. For each region, CALCULATE(SUM(Workforce_Snapshots[EmployeeCount]), LASTNONBLANK('Date'[Date], CALCULATE(SUM(Workforce_Snapshots[EmployeeCount])))) finds the last date where that specific region has a non-blank employee count value

3. LASTNONBLANK is crucial here because it evaluates each date within the context and returns the last one where the expression (employee count sum) is not blank - this handles the varying reporting frequencies perfectly

4. The results from each region are then summed together to produce the corporate total

This approach correctly handles the scenario where Region A reports on December 31st while Region B's last report was December 15th - both regions' most recent values are captured and aggregated.

The second option using LASTDATE with additional filter context would still only capture regions reporting on the absolute last date, not each region's individual last reporting date.

The third option using ALL(Workforce_Snapshots[RegionID]) removes all region filters, which would include regions not relevant to the current selection context and uses LASTDATE on the fact table directly rather than the Date dimension, breaking the time intelligence functionality.

The fourth option finds only the single maximum report date across all data and filters to that one date, which suffers from the same problem as the original measure - it doesn't account for different regions having different final reporting dates within the period.