PL-300 Prepare the data Practice Question
You have a Power BI semantic model that uses Import mode with a SQL Server data source. The refresh takes over two hours. You need to reduce the refresh time while keeping data up-to-date. What is the best strategy?
⚠ Common exam trap
Candidates often confuse reducing refresh frequency (Option D) with reducing refresh time, or they think DirectQuery (Option A) is a universal performance fix, when in fact it shifts the performance burden to query time and sacrifices Import mode capabilities.
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 policy on the fact table.
Incremental refresh policy allows you to refresh only the most recent data (e.g., last 5 days) while keeping historical partitions unchanged, drastically reducing the amount of data loaded during each refresh. This is the most effective way to reduce refresh time in Import mode while maintaining data freshness, as it avoids re-querying the entire fact table from SQL Server.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Switch the data source to DirectQuery mode.
Why it's wrong here
Switching to DirectQuery mode eliminates the need for scheduled refresh because data is queried live from the source, but this substantially changes analytical performance: every interaction in Power BI translates to a query against the source system, potentially causing slower report loads and heavy load on the source database. Furthermore, DirectQuery restricts time intelligence and often requires the underlying data source to support advanced features, so it is not an equivalent way to reduce refresh time; it is a trade-off, not a refresh-time solution.
- ✗
Remove unnecessary columns and rows from the query.
Why it's wrong here
While trimming unnecessary columns and rows reduces the overall model size and can modestly shorten refresh duration, it does not address the core bottleneck of reading and compressing a large fact table; the refresh engine still scans every remaining row in the data source and transformations still need to run. For a fact table with millions of rows, this approach yields limited refresh time improvement and may compromise analytical capability if useful attributes are removed.
- ✓
Configure incremental refresh policy on the fact table.
Why this is correct
Configuring incremental refresh on the fact table is the correct approach because it partitions data by date using RangeStart and RangeEnd parameters, so only new and updated rows are loaded during each scheduled refresh. This dramatically cuts the data processed per refresh cycle, especially for a fact table accumulating transactional data over time. It requires at least one date column and enables the 'refresh policy' capability in Power BI to manage historical and current partitions separately.
- ✗
Reduce the scheduled refresh frequency to once a day.
Why it's wrong here
Reducing the refresh frequency to once a day does not make a single refresh operation faster; it merely stretches the time between refreshes, so the latest data may be stale more often. The question specifically asks about reducing refresh time, not decreasing how often the job runs, so this option fails to solve the stated problem and could even lead to higher refresh loads if more data accumulates overnight.
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