Data Issues DY0-001 Practice Questions (Page 2)

Questions 6–10

1. 6application · medium

   A data analyst is modeling the relationship between advertising spend and sales revenue. A scatter plot shows a clear curve: sales rise steeply at first, then plateau. A linear regression model has poor fit. What is the most appropriate next step?

   • AApply a logarithmic transformation to the advertising spend variable.
   • BAggregate the data to a higher level, such as quarterly totals.
   • CUse a correlation matrix to confirm the relationship.
   • DRemove outliers from the sales data.
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2. 7application · medium

   A logistics company tracks package delivery times. The data is recorded at the minute level, but the business needs to analyze delivery performance by hour of the day. What is the most appropriate data preparation step?

   • ARound each delivery time to the nearest hour.
   • BKeep the minute-level data and use it directly in the model.
   • CConvert the time to a categorical variable with 1,440 categories.
   • DDrop the time variable and use only the date.
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4. 8foundation · easy

   In a dataset of employee salaries, one entry is accidentally recorded as $1,000,000 instead of $100,000. What is this data point called?

   • AOutlier
   • BMode
   • CMissing value
   • DMedian
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5. 9application · medium

   A data analyst is analyzing customer transaction amounts. The dataset contains a few transactions of $10,000 or more, while the majority are under $500. These large transactions are legitimate but rare. What is the best way to handle these values in a regression model predicting transaction amount?

   • ACap them at $500 to match the majority.
   • BKeep them but apply a transformation like log to reduce their influence.
   • CReplace them with the median transaction amount.
   • DRemove them because they are outliers.
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6. 10expert · hard

   A data analyst is analyzing daily sales data for a retail chain. The business wants to forecast weekly sales and also understand the impact of promotions that run for specific days. The promotion data is only available at the daily level. What is the most appropriate data preparation step?

   • AAggregate the daily sales and promotion data to weekly totals.
   • BDisaggregate the weekly forecast to daily using a simple average.
   • CRemove the promotion data because it is at a different granularity.
   • DKeep the daily sales data and use it directly for weekly forecasts.
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