Optimize Model Performance PL-300 Practice Questions (Page 1)

Questions 1–5

1. 1application · medium

   A financial services company imports a stock price table that records prices every minute for each stock. The report only needs to show daily closing price per stock. The table also contains a 'Ticker' column that is not used in any report. The model is large and slow to refresh. What should you do to reduce the model size while still supporting the report?

   • AUse a row-level security (RLS) rule to limit the stock price data to the current day for all users.
   • BImport the raw stock price data and use a calculated table to create daily closing prices.
   • CAggregate the stock price data to daily closing prices per stock before importing, and remove the 'Ticker' column.
   • DKeep the raw stock price data but remove the 'Ticker' column and create a measure for daily closing price.
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2. 2application · medium

   A logistics company imports shipment tracking data with a row for every GPS ping (every 5 minutes) for each vehicle. The report shows daily shipment counts and average delivery time. The model is large and refresh is slow. What should you do to reduce data granularity while still supporting the report?

   • AAggregate the GPS pings to one row per shipment per day before importing.
   • BRemove the GPS ping data entirely and keep only the shipment header table.
   • CIncrease the frequency of GPS pings to every minute to get more accurate data.
   • DKeep all GPS pings but remove the timestamp column.
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4. 3application · medium

   A financial company imports a transaction table that includes both posted and pending transactions. The report only needs to show posted transactions. The table also includes a 'SourceFile' column that is not used in any report. The model is slow to refresh. What should you do to improve performance?

   • AImport all transactions and use a calculated column to flag posted transactions.
   • BUse a row-level security (RLS) rule to show only posted transactions to all users.
   • CFilter the transaction table to only posted transactions during import, and remove the 'SourceFile' column.
   • DKeep all transactions but remove the 'SourceFile' column and create a measure that filters to posted transactions.
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5. 4foundation · easy

   Which statement best describes the impact of removing unnecessary columns from a Power BI table?

   • AIt prevents users from creating new reports.
   • BIt reduces the amount of data loaded into the model, which can improve refresh and query performance.
   • CIt automatically optimizes all DAX measures in the model.
   • DIt increases the number of relationships that can be created.
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6. 5expert · hard

   A Power BI developer is optimizing a model with a large fact table (100 million rows) and a measure that calculates the average order value. Performance Analyzer shows that the measure's DAX query takes 12 seconds. The developer suspects the bottleneck is due to the high cardinality of the 'OrderID' column used in the measure. The report only needs to show average order value by month. What should the developer do to improve query performance while maintaining the ability to analyze by month?

   • ARewrite the measure to use a different DAX function, such as AVERAGEX, to improve performance.
   • BKeep the raw fact table but create an index on the 'OrderID' column in the source database.
   • CUse a calculated column to store the month and then use that column in the measure.
   • DPre-aggregate the fact table to monthly averages per customer and use that aggregated table in the report.
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