DP-900 Describe an analytics workload on Azure Practice Question
Your company runs a sales analytics dashboard on Power BI that refreshes every hour from Azure Synapse Analytics. During peak hours, the dashboard refresh fails with a 'timeout' error. Which action should you take FIRST to resolve the issue?
⚠ Common exam trap
Test-takers frequently assume scaling the source (Option A) or changing the connection mode (Option B) is the immediate fix, but the DP-900 exam emphasizes that incremental refresh is the primary technique to optimize refresh performance for large datasets without altering the underlying infrastructure.
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
✓
Implement incremental refresh in Power BI to refresh only changed data.
Implementing incremental refresh in Power BI reduces the amount of data loaded during each refresh cycle, which directly addresses timeout errors by limiting the refresh to only changed or new data rather than the entire dataset. This is the most efficient first step to reduce refresh duration without changing the underlying architecture or data source connection mode.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Scale up the Azure Synapse dedicated SQL pool to handle more concurrent queries.
Why it's wrong here
Scaling up the Azure Synapse dedicated SQL pool increases DWUs to handle higher concurrency and throughput, but the refresh timeout is typically caused by a single long-running query that scans the entire table, not by too many concurrent queries. Even if scaling reduces query execution time slightly, it incurs significant extra cost and does not address the inefficiency of re-importing unchanged historical data. Incremental refresh specifically targets changed partitions, making it a more targeted and cost-effective fix.
- ✗
Configure the dashboard to use DirectQuery instead of Import mode.
Why it's wrong here
DirectQuery shifts the load from scheduled refresh to interactive queries sent to Synapse every time a user interacts with a dashboard visual, so those queries run during peak business hours and remain subject to source timeout limits. Because the volume of data queried is still large and no caching is used, direct queries can easily exceed the timeout thresholds in Power BI or Synapse. Import mode with incremental refresh instead pre-loads a smaller, up-to-date dataset into the Power BI engine, eliminating the per-visual query latency and timeouts.
- ✓
Implement incremental refresh in Power BI to refresh only changed data.
Why this is correct
Incremental refresh partitions the fact table by date using RangeStart and RangeEnd parameters, so only partitions that contain new or modified rows (typically the last few days) are pulled from the Synapse SQL pool during each scheduled refresh. This dramatically reduces the amount of data scanned and transferred per refresh, ensuring the query finishes well within the timeout window and lowering the load on Synapse. The historic partitions remain unchanged in the Power BI model, so the dashboard continues to deliver full historical analysis without sacrificing performance.
- ✗
Export the data to CSV files and load into Power BI from Azure Blob Storage.
Why it's wrong here
Exporting from Synapse to CSV files in Blob Storage interrupts the automated refresh path and still requires a full export of the entire dataset, which can itself hit the same timeout when the export query is long-running. The subsequent load from Blob Storage introduces an extra dependency on file staging, complicates data freshness and security, and does not reduce the volume of data handled. Unlike incremental refresh, this approach does not address the root cause of the timeout and adds operational overhead.
Visual reference
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Data Roles and Core Concepts
Key term
Data
Data is raw, unprocessed information, like numbers, words, or measurements, that can be stored, processed, and analyzed by computers.
Key term
Power BI
Power BI is a Microsoft business analytics tool that transforms raw data into interactive visual reports and dashboards for informed decision-making.
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