Perform Data Deduplication Operations and Aggregate Operations on DataFrames, Such as Count, Approximate Count Distinct, and Mean, Summary. DATA-ENGINEER-ASSOCIATE Practice Questions (Page 1)

Questions 1–5

1. 1application · medium

   A sales DataFrame has columns region, product, and revenue. The team wants to compute the average revenue per product within each region. Which code should they use?

   • Adf.groupBy('region', 'product').agg(mean('revenue'))
   • Bdf.groupBy('product').agg(mean('revenue'))
   • Cdf.select('region', 'product', 'revenue').summary()
   • Ddf.groupBy('region').agg(mean('revenue'))
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2. 2foundation · medium

   What does the second parameter of approx_count_distinct represent?

   • AThe number of partitions to use
   • BThe relative standard deviation (tolerance) for the estimate
   • CThe exact count to return
   • DThe maximum number of distinct values to count
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4. 3application · medium

   A retail company's sales DataFrame has duplicate rows because of a data pipeline retry. Each row has a unique transaction_id, but the same transaction may appear multiple times. The data team wants to keep only the first occurrence of each transaction_id while retaining all other columns. Which code should they use?

   • Adf.distinct()
   • Bdf.groupBy('transaction_id').count()
   • Cdf.dropDuplicates(['transaction_id'])
   • Ddf.drop_duplicates()
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5. 4foundation · medium

   Which aggregate function would you use with agg to count the number of rows in each group?

   • Acount
   • Bmean
   • Csum
   • Dmax
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6. 5foundation · medium

   Which function returns the exact number of distinct values in a column?

   • Aapprox_count_distinct
   • Bcount
   • Cdistinct
   • DcountDistinct
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