Prepare and Clean Datasets SNOWPRO-ADVANCED-DATA-ANALYST Practice Questions (Page 2)

Questions 6–10

1. 6foundation · easy

   What is an example of inconsistent data?

   • AA column with all values in the correct format
   • BA column with unique values
   • CThe same customer name appearing as 'John Smith' in one table and 'J. Smith' in another
   • DA table with no missing values
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2. 7application · medium

   A data analyst is given a new dataset with columns 'customer_id', 'signup_date', and 'total_spend'. Before performing any analysis, the analyst wants to understand the data's structure and quality. Which action is most appropriate for initial data profiling?

   • AUse the SUM, AVG, and COUNT functions on all columns.
   • BRun a SELECT * query to view all the rows in the table.
   • CUse the MIN, MAX, COUNT, COUNT(DISTINCT), and check for NULLs on each column.
   • DCreate a scatter plot of total_spend vs signup_date.
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4. 8expert · hard

   A data analyst is preparing a dataset for a model that predicts employee attrition. The 'salary' column has 20% missing values, and the 'department' column has 10% missing values. The analyst wants to use a decision tree model, which can handle missing values natively. However, the business also wants to use the data for a separate linear regression analysis. What is the best strategy?

   • AKeep the missing values for the decision tree, and create a separate imputed dataset for the linear regression.
   • BRemove all rows with missing values in either column.
   • CImpute salary with the median and department with the mode, then use the same dataset for both models.
   • DImpute salary using a regression model and department using a classification model, and use the same dataset for both.
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5. 9application · medium

   A logistics company consolidates address data from multiple regional offices. The 'state' column contains values like 'CA', 'california', 'Calif.', and 'CA '. The 'zip_code' column contains some 5-digit and some 9-digit values (e.g., '90210' and '90210-1234'). The analyst needs to standardize these fields for a national shipping analysis. What is the best approach?

   • AReplace all state values with the first two letters of the string.
   • BConvert the state column to a numeric code and keep the zip as a string.
   • CCreate a lookup table mapping all state variations to the two-letter abbreviation, and trim zip codes to 5 digits.
   • DUse UPPER() on the state column and leave zip codes as-is.
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6. 10foundation · easy

   Which strategy is commonly used to handle missing values in a dataset?

   • AImputation with the column mean
   • BDuplicating the first non-null value to fill all gaps
   • CIgnoring the missing values entirely without any action
   • DReplacing all missing values with the string 'N/A'
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