Data Wrangling DY0-001 Practice Questions (Page 4)

Questions 16–20

1. 16foundation · easy

   What is the primary purpose of ground truth labeling in supervised machine learning?

   • ATo reduce the dimensionality of the dataset
   • BTo remove duplicate records from the dataset
   • CTo fill in missing values in the dataset
   • DTo assign accurate labels to data for training and validating models
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2. 17foundation · easy

   A data analyst notices that a customer dataset contains duplicate records for the same customer ID, with slightly different values in the address field. Which data cleaning technique is most appropriate to address this issue?

   • AOutlier detection
   • BParsing
   • CNormalization
   • DDeduplication
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4. 18expert · hard

   A data engineer is merging two large datasets: 'transactions' (transaction_id, user_id, amount) and 'users' (user_id, name, email). The 'transactions' table has 10 million rows, and the 'users' table has 1 million rows. The engineer notices that after a left join, the resulting dataset has more rows than the 'transactions' table. What is the most likely cause and the best solution?

   • AThe merge key is case-sensitive; convert both columns to lowercase before merging.
   • BThe 'transactions' table has duplicate transaction_id values; deduplicate the 'transactions' table on transaction_id before merging.
   • CThe merge is using an inner join; switch to a left join to avoid the extra rows.
   • DThe 'users' table has duplicate user_id values; deduplicate the 'users' table on user_id before merging.
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5. 19foundation · easy

   Which data cleaning technique is used to correct inconsistencies in data formats, such as converting all date values to a single format (e.g., YYYY-MM-DD)?

   • ABinning
   • BImputation
   • CStandardization
   • DDeduplication
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6. 20application · medium

   A data analyst is merging two datasets on a 'date' column. One dataset has dates as strings in 'YYYY-MM-DD' format, the other has dates as datetime objects. After merging, the analyst finds that no rows matched. What is the most likely cause and the best solution?

   • AThe datasets have different time zones; convert both to UTC before merging.
   • BThe date formats are incompatible; convert both columns to the same datetime type before merging.
   • CThe merge is using an inner join; switch to a left join to see matches.
   • DThe column names are different; rename the columns to match before merging.
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