Databricks Databricks-Certified-Associate-Developer-for-Apache-Spark-3.0 Free Sample Exam Questions 2026

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Page: 1 / 36
Total Questions: 180

Questions & Answers PDF

• Question 1

  

  Which of the following is a problem with using accumulators?

  • Only unnamed accumulators can be inspected in the Spark UI.
    
  • Only numeric values can be used in accumulators.
    
  • Accumulator values can only be read by the driver, but not by executors.
    
  • Accumulators do not obey lazy evaluation.
    
  • Accumulators are difficult to use for debugging because they will only be updated once, independent if a task has to be re-run due to hardware failure.
    

  Reveal Answer Answer: 3 Next Question

• Question 2

  

  The code block displayed below contains an error. The code block is intended to join DataFrame itemsDf with the larger DataFrame transactionsDf on column itemId. Find the error.Code block:transactionsDf.join(itemsDf, 'itemId', how='broadcast')

  • The syntax is wrong, how= should be removed from the code block.
    
  • The join method should be replaced by the broadcast method.
    
  • Spark will only perform the broadcast operation if this behavior has been enabled on the Spark cluster.
    
  • The larger DataFrame transactionsDf is being broadcasted, rather than the smaller DataFrame itemsDf.
    
  • broadcast is not a valid join type.
    

  Reveal Answer Answer: 5 Next Question

• Question 3

  

  Which of the following code blocks returns all unique values of column storeId in DataFrame transactionsDf?

  • transactionsDf['storeId'].distinct()
    
  • transactionsDf.select('storeId').distinct()
    (Correct)
    
  • transactionsDf.filter('storeId').distinct()
    
  • transactionsDf.select(col('storeId').distinct())
    
  • transactionsDf.distinct('storeId')
    

  Reveal Answer Answer: 2 Next Question

• Question 4

  

  Which of the following code blocks returns a new DataFrame with only columns predError and values of every second row of DataFrame transactionsDf?Entire DataFrame transactionsDf:1. +-------------+---------+-----+-------+---------+----+2. |transactionId|predError|value|storeId|productId| f|3. +-------------+---------+-----+-------+---------+----+4. | 1| 3| 4| 25| 1|null|5. | 2| 6| 7| 2| 2|null|6. | 3| 3| null| 25| 3|null|7. | 4| null| null| 3| 2|null|8. | 5| null| null| null| 2|null|9. | 6| 3| 2| 25| 2|null|10. +-------------+---------+-----+-------+---------+----+

  • transactionsDf.filter(col('transactionId').isin([3,4,6])).select([predError, value])
    
  • transactionsDf.select(col('transactionId').isin([3,4,6]), 'predError', 'value')
    
  • transactionsDf.filter('transactionId' % 2 == 0).select('predError', 'value')
    
  • transactionsDf.filter(col('transactionId') % 2 == 0).select('predError', 'value')
    (Correct)
    E.
    1. transactionsDf.createOrReplaceTempView('transactionsDf')
    2. spark.sql('FROM transactionsDf SELECT predError, value WHERE transactionId % 2 = 2')
    
  • transactionsDf.filter(col(transactionId).isin([3,4,6]))
    

  Reveal Answer Answer: 4 Next Question

• Question 5

  

  Which of the following is the deepest level in Spark's execution hierarchy?

  • Job
    
  • Task
    
  • Executor
    
  • Slot
    
  • Stage
    

  Reveal Answer Answer: 2 Next Question

• Question 6

  

  Which of the following code blocks displays various aggregated statistics of all columns in DataFrame transactionsDf, including the standard deviation and minimum of values in each column?

  • transactionsDf.summary()
    
  • transactionsDf.agg('count', 'mean', 'stddev', '25%', '50%', '75%', 'min')
    
  • transactionsDf.summary('count', 'mean', 'stddev', '25%', '50%', '75%', 'max').show()
    
  • transactionsDf.agg('count', 'mean', 'stddev', '25%', '50%', '75%', 'min').show()
    
  • transactionsDf.summary().show()
    

  Reveal Answer Answer: 5 Next Question

• Question 7

  

  Which of the following code blocks returns a 2-column DataFrame that shows the distinct values in column productId and the number of rows with that productId in DataFrame transactionsDf?

  • transactionsDf.count('productId').distinct()
    
  • transactionsDf.groupBy('productId').agg(col('value').count())
    
  • transactionsDf.count('productId')
    
  • transactionsDf.groupBy('productId').count()
    
  • transactionsDf.groupBy('productId').select(count('value'))
    

  Reveal Answer Answer: 4 Next Question

• Question 8

  

  The code block displayed below contains an error. The code block should return a DataFrame where all entries in column supplier contain the letter combination et in this order. Find the error.Code block:itemsDf.filter(Column('supplier').isin('et'))

  • The Column operator should be replaced by the col operator and instead of isin, contains should be used.
    
  • The expression inside the filter parenthesis is malformed and should be replaced by isin('et', 'supplier').
    
  • Instead of isin, it should be checked whether column supplier contains the letters et, so isin should be replaced with contains. In addition, the column should be accessed using col['supplier'].
    
  • The expression only returns a single column and filter should be replaced by select.
    

  Reveal Answer Answer: 2 Next Question

• Question 9

  

  Which of the following code blocks writes DataFrame itemsDf to disk at storage location filePath, making sure to substitute any existing data at that location?

  • itemsDf.write.mode('overwrite').parquet(filePath)
    
  • itemsDf.write.option('parquet').mode('overwrite').path(filePath)
    
  • itemsDf.write(filePath, mode='overwrite')
    
  • itemsDf.write.mode('overwrite').path(filePath)
    
  • itemsDf.write().parquet(filePath, mode='overwrite')
    

  Reveal Answer Answer: 1 Next Question

• Question 10

  

  Which of the following describes Spark's way of managing memory?

  • Spark uses a subset of the reserved system memory.
    
  • Storage memory is used for caching partitions derived from DataFrames.
    
  • As a general rule for garbage collection, Spark performs better on many small objects than few big objects.
    
  • Disabling serialization potentially greatly reduces the memory footprint of a Spark application.
    
  • Spark's memory usage can be divided into three categories: Execution, transaction, and storage.
    

  Reveal Answer Answer: 2 Next Question

Page: 1 / 36
Total Questions: 180

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