Reducing loaded data in Power Query with grouping and column removal
Short answer
AC Grouping to the needed grain and removing unused columns and rows are the documented data-reduction techniques.
Question
Key Takeaways
Microsoft's data-reduction guidance names exactly these levers: remove unnecessary columns and rows before load, and group or summarize detail to the grain the report actually needs.
A report on a two-query semantic model underperforms, and Power Query must be used to cut the data volume loaded into the model, with two complementary actions to pick.
Common mistake: Treating query consolidation as data reduction — Append and Merge change how tables come together (and often grow row or column counts), while query groups are pure authoring hygiene with zero effect on model size.
Expert Analysis
Why the Answer Is Correct
Applying group by and summarize techniques (A) collapses detail rows to a higher grain before load, directly shrinking fact tables — the official data-reduction guidance for import models lists grouping and aggregating as a first-line technique. Removing unnecessary columns and rows (C) eliminates fields and records no visual consumes, which reduces both the dataset size and refresh load. Microsoft's import modeling guidance names column and row removal as the most effective data reduction step.
Why the Other Options Are Wrong
Appending queries (B) stacks tables into more rows, growing rather than shrinking the model. Merging queries (D) adds lookup columns to a table, increasing column count unless other reductions follow — on its own it reduces nothing. Creating a new query group (E) merely organizes queries in the editor's folder tree; the data loaded is byte-identical.
Community Comment Notes
Several commenters anchor A and C to the official import-modeling-data-reduction guidance page. One adds the useful observation that Append can actually increase dataset size, and that a query group only affects Power Query editor organization.
Official Reference
https://learn.microsoft.com/en-us/power-bi/guidance/import-modeling-data-reduction
This link opens the official vendor documentation in a new tab.
Community Votes
100% of anonymous learners picked answer AC. Votes are pick records left by other test-takers — they are not the verified answer.
Discussion 5 contributions
Compiled from public community threads and reader submissions, lightly edited for clarity. Handles and vote tallies are shown as recorded, and comments reflect the views of individual contributors.
From public community threads 5
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VladStan 👍 4Correct answers https://learn.microsoft.com/en-us/power-bi/guidance/import-modeling-data-reduction
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amiruladliroslibin 👍 3B. Combine the queries by using Append While combining queries via append can help merge multiple datasets, it typically does not reduce the data size. In fact, it may increase the dataset size unless managed carefully. D. Combine the queries by using Merge Merging queries is useful for joining data from different sources, but it is not inherently a data reduction technique. It may help in certain cases, but it is not directly aimed at reducing the amount of data loaded to the model. E. Create a new query group A query group is simply an organizational tool in Power Query to manage your queries better. It has no impact on performance or data reduction.
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jaume 👍 2Ony options A and C will reduce data size and potentially improving performance. Combining queries (by using Append or Merge) could improve refresh but not report performance as is and a new query group would not impact at all
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desibaby09 👍 2https://learn.microsoft.com/en-us/power-bi/guidance/import-modeling-data-reduction
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LuluSkyy 👍 2A and C are the correct answers
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