Apply Data Transformation DA0-001 Practice Questions (Page 5)

Questions 21–25

1. 21expert · hard

   A retail chain has a 'stores' table with store_id and store_name, and a 'sales' table with sale_id, store_id, and revenue. The analyst needs a dataset showing store names and total revenue per store. The sales table has some store_id values that do not exist in the stores table. What should the analyst do?

   • AClean the sales table by removing rows with store_ids not in the stores table, then join.
   • BPerform a left join from stores to sales, then aggregate revenue by store.
   • CPerform a full outer join on store_id, then aggregate revenue by store, treating missing store names as 'Unknown'.
   • DPerform an inner join on store_id, then aggregate revenue by store.
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2. 22foundation · easy

   Which of the following activities is an example of data cleansing?

   • AExtracting product names from a free-text description field
   • BConverting all date fields from MM/DD/YYYY to YYYY-MM-DD
   • CRemoving records with missing or invalid postal codes
   • DCombining customer data from a CRM and a billing system into one table
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4. 23application · medium

   A financial analyst receives a JSON file with transaction data. The file contains nested objects for customer information. The analyst needs to load this into a relational table with columns for transaction_id, customer_name, and amount. Which transformation is required?

   • AConvert the JSON file to a CSV file without changing the structure.
   • BRemove the customer information from the JSON file.
   • CFlatten the nested JSON structure into a tabular format.
   • DLoad the JSON file directly into the relational table.
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5. 24foundation · easy

   A data analyst receives a log file where each line contains a timestamp, an IP address, and a status code separated by spaces. The analyst needs to extract the IP address from each line for further analysis. Which data transformation process is most directly used?

   • AData formatting
   • BData merging
   • CData parsing
   • DData cleansing
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6. 25application · medium

   A data analyst notices that a 'Country' column contains 'USA', 'U.S.A.', and 'United States' for the same country. Before aggregating sales by country, what should the analyst do?

   • AConvert the column to uppercase.
   • BRemove all rows with 'U.S.A.' or 'United States'.
   • CCreate a mapping table to standardize all variants to a single value.
   • DUse the first value encountered for each group.
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