Objective 2.3 Merge and Load Data Sets CERTIFIED-DATA-SCIENCE-PRACTITIONER Practice Questions (Page 3)

Questions 11–15

1. 11foundation · easy

   Which method is most appropriate for loading a very large cleaned dataset into a database table efficiently?

   • AUse an ORM to insert each record individually
   • BExport to CSV and manually import via a GUI
   • CSingle-row INSERT statements in a loop
   • DBulk load utility (e.g., COPY, BULK INSERT)
   Reveal Solution Discussion Select an answer first
2. 12expert · hard

   A data scientist is merging a sensor log (key: `timestamp`, string in ISO format) with a weather dataset (key: `time`, string in 'YYYY-MM-DD HH:MM:SS' format). The merge yields zero matches. What is the most likely cause?

   • AThe weather dataset has missing timestamps
   • BThe key columns have different names and formats
   • CThe merge was an inner join instead of a left join
   • DThe sensor log has duplicate timestamps
   Reveal Solution Discussion Select an answer first
4. 13foundation · easy

   Which pandas function is commonly used to load a CSV file into a DataFrame?

   • Apd.import_csv()
   • Bpd.load_csv()
   • Cpd.read_csv()
   • Dpd.open_csv()
   Reveal Solution Discussion Select an answer first
5. 14foundation · easy

   Which join type returns only the rows that have matching key values in both input datasets?

   • ARight outer join
   • BInner join
   • CFull outer join
   • DLeft outer join
   Reveal Solution Discussion Select an answer first
6. 15foundation · easy

   A data scientist has two tables: `customers` and `orders`. The goal is to produce a result that includes every customer, even those who have never placed an order. Which join operation should be used?

   • AFull outer join
   • BInner join
   • CRight outer join
   • DLeft outer join
   Reveal Solution Discussion Select an answer first