How to Improve DirectQuery Performance Without Changing Data Model
A Power BI report uses a DirectQuery dataset connected to an Azure SQL Database. Users report that the report takes over 30 seconds to load. You need to improve performance without changing the data model. What should you recommend?
Quick Answer
The answer is to enable 'Reduce cardinality by using aggregation' in the dataset settings. This improves DirectQuery performance by instructing Power BI to pre-aggregate data at the source, reducing the volume of rows sent across the network for each query without altering the underlying data model structure. On the PL-300 exam, this scenario tests your understanding of performance optimization techniques that work within DirectQuery’s constraints—specifically, that aggregation can lower latency while preserving the live connection to Azure SQL Database. A common trap is mistaking this for switching to Import mode, which changes the data model approach entirely, or for adjusting connection limits, which only helps concurrency. Remember the mnemonic: “Aggregate, don’t migrate” to recall that aggregation settings improve speed without changing the model’s storage mode.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Enable 'Reduce cardinality by using aggregation' in the dataset settings.
Enabling 'Reduce cardinality by using aggregation' can improve DirectQuery performance by pre-aggregating data. Option A is wrong because increasing the maximum connections per user might help concurrency but not single query latency. Option C is wrong because converting to Import mode changes the data model approach and is not allowed if the requirement is to not change the data model. Option D is wrong because disabling 'Cross-report data binding' affects report interactivity but not query performance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the 'Maximum connections per user' setting in the Premium capacity.
Why it's wrong here
Increasing the maximum connections per user might help concurrency but not single query latency, so it does not address the 30-second load time.
- ✓
Enable 'Reduce cardinality by using aggregation' in the dataset settings.
Why this is correct
Enabling 'Reduce cardinality by using aggregation' pre-aggregates data at the source, reducing the amount of data transferred and improving DirectQuery performance.
- ✗
Convert the dataset to Import mode.
Why it's wrong here
Converting the dataset to Import mode changes the data model approach (from DirectQuery to Import), which violates the requirement of not changing the data model.
- ✗
Disable 'Cross-report data binding' in the report settings.
Why it's wrong here
Disabling 'Cross-report data binding' affects report interactivity but does not impact DirectQuery query performance.
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1 more way this is tested on PL-300
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Variation 1. You have a Power BI dataset that uses a DirectQuery connection to Azure Synapse Analytics. Users report that the report is slow. You need to improve query performance without changing the data source. What should you do?
hard- A.Reduce the cardinality of calculated measures.
- B.Disable row-level security (RLS) on the dataset.
- C.Increase the scheduled refresh frequency.
- ✓ D.Reduce the number of visuals on each report page.
Why D: Reducing the number of visuals on each report page reduces the number of queries sent to the data source, improving performance for DirectQuery datasets. Option A is incorrect because reducing cardinality of measures may reduce data size but does not directly reduce the number of queries. Option B is incorrect because disabling RLS could change data access but does not significantly improve query performance. Option C is incorrect because increasing scheduled refresh frequency applies to import mode, not DirectQuery.
JA
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Senior Network & Security Engineer · founder of Courseiva
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