DP-700 Monitor and Optimize an Analytics Solution Practice Question
Exhibit
{
"operation": "REFRESH",
"startTime": "2023-11-01T08:00:00",
"endTime": "2023-11-01T08:45:00",
"duration": "2700s",
"rowsProcessed": 5000000,
"status": "SUCCESS"
}Refer to the exhibit. The refresh operation for a Power BI semantic model took 45 minutes to complete. What is the most efficient way to reduce the refresh time for this specific model?
⚠ Common exam trap
Candidates often choose 'optimize DAX' or 'increase capacity SKU' as the first step. While these help, they do not address the fundamental inefficiency of reloading static historical data during every single refresh cycle.
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
✓
Configure incremental refresh.
Incremental refresh is the most effective strategy for large datasets. Instead of reloading all 5 million rows every time, you configure the model to load only new or updated data based on a date/time column. This dramatically reduces the amount of data processed per refresh, lowering execution time and resource utilization on the Fabric capacity while keeping reports up to date.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Upgrade to a higher capacity SKU.
Why it's wrong here
Upgrading the SKU provides more raw power, but it is an expensive way to solve a problem that is better addressed through architectural optimization. Incremental refresh is a design pattern that inherently optimizes the workload itself rather than just throwing more hardware at a inefficient process.
- ✓
Configure incremental refresh.
Why this is correct
Incremental refresh allows Power BI to refresh only the partitions that have changed, rather than reloading the entire dataset. This is the industry-standard method for handling large models, as it minimizes the processing time and network traffic required for daily model updates.
- ✗
Use Power BI Desktop to manually refresh.
Why it's wrong here
Manual refreshing in Power BI Desktop does not solve the performance issue; it only shifts the burden to a local machine. It does not automate the process or reduce the amount of data processed during the refresh, and it does not help with the service-level performance bottlenecks.
- ✗
Delete all historical data.
Why it's wrong here
Deleting historical data is usually not a valid business option as users typically require access to historical trends for analysis. Incremental refresh provides the benefits of keeping that history while only requiring the processing of fresh data, which is the preferred technical solution.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Microsoft exam blueprint
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