Free Databricks Certified Associate Developer for Apache Spark 3.0 Exam Databricks-Certified-Associate-Developer-for-Apache-Spark-3.0 Exam Practice Test

The code block shown below should return the number of columns in the CSV file stored at location filePath. From the CSV file, only lines should be read that do not start with a # character. Choosethe answer that correctly fills the blanks in the code block to accomplish this.Code block:__1__(__2__.__3__.csv(filePath, __4__).__5__)

• 1. size
  2. spark
  3. read()
  4. escape='#'
  5. columns
  
• 1. DataFrame
  2. spark
  3. read()
  4. escape='#'
  5. shape[0]
  
• 1. len
  2. pyspark
  3. DataFrameReader
  4. comment='#'
  5. columns
  
• 1. size
  2. pyspark
  3. DataFrameReader
  4. comment='#'
  5. columns
  
• 1. len
  2. spark
  3. read
  4. comment='#'
  5. columns
  

The code block displayed below contains an error. The code block should return all rows of DataFrame transactionsDf, but including only columns storeId and predError. Find the error.Code block:spark.collect(transactionsDf.select('storeId', 'predError'))

• Instead of select, DataFrame transactionsDf needs to be filtered using the filter operator.
  
• Columns storeId and predError need to be represented as a Python list, so they need to be wrapped in brackets ([]).
  
• The take method should be used instead of the collect method.
  
• Instead of collect, collectAsRows needs to be called.
  
• The collect method is not a method of the SparkSession object.
  

The code block shown below should return only the average prediction error (column predError) of a random subset, without replacement, of approximately 15% of rows in DataFrametransactionsDf. Choose the answer that correctly fills the blanks in the code block to accomplish this.transactionsDf.__1__(__2__, __3__).__4__(avg('predError'))

• 1. sample
  2. True
  3. 0.15
  4. filter
  
• 1. sample
  2. False
  3. 0.15
  4. select
  
• 1. sample
  2. 0.85
  3. False
  4. select
  
• 1. fraction
  2. 0.15
  3. True
  4. where
  
• 1. fraction
  2. False
  3. 0.85
  4. select
  

Which of the following statements about lazy evaluation is incorrect?

• Predicate pushdown is a feature resulting from lazy evaluation.
  
• Execution is triggered by transformations.
  
• Spark will fail a job only during execution, but not during definition.
  
• Accumulators do not change the lazy evaluation model of Spark.
  
• Lineages allow Spark to coalesce transformations into stages
  

Which of the following describes the difference between client and cluster execution modes?

• In cluster mode, the driver runs on the worker nodes, while the client mode runs the driver on the client machine.
  
• In cluster mode, the driver runs on the edge node, while the client mode runs the driver in a worker node.
  
• In cluster mode, each node will launch its own executor, while in client mode, executors will exclusively run on the client machine.
  
• In client mode, the cluster manager runs on the same host as the driver, while in cluster mode, the cluster manager runs on a separate node.
  
• In cluster mode, the driver runs on the master node, while in client mode, the driver runs on a virtual machine in the cloud.
  

The code block shown below should write DataFrame transactionsDf as a parquet file to path storeDir, using brotli compression and replacing any previously existing file. Choose the answer thatcorrectly fills the blanks in the code block to accomplish this.transactionsDf.__1__.format('parquet').__2__(__3__).option(__4__, 'brotli').__5__(storeDir)

• 1. save
  2. mode
  3. 'ignore'
  4. 'compression'
  5. path
  
• 1. store
  2. with
  3. 'replacement'
  4. 'compression'
  5. path
  
• 1. write
  2. mode
  3. 'overwrite'
  4. 'compression'
  5. save
  (Correct)
  
• 1. save
  2. mode
  3. 'replace'
  4. 'compression'
  5. path
  
• 1. write
  2. mode
  3. 'overwrite'
  4. compression
  5. parquet
  

The code block displayed below contains an error. The code block should merge the rows of DataFrames transactionsDfMonday and transactionsDfTuesday into a new DataFrame, matchingcolumn names and inserting null values where column names do not appear in both DataFrames. Find the error.Sample of DataFrame transactionsDfMonday:1. +-------------+---------+-----+-------+---------+----+2. |transactionId|predError|value|storeId|productId| f|3. +-------------+---------+-----+-------+---------+----+4. | 5| null| null| null| 2|null|5. | 6| 3| 2| 25| 2|null|6. +-------------+---------+-----+-------+---------+----+Sample of DataFrame transactionsDfTuesday:1. +-------+-------------+---------+-----+2. |storeId|transactionId|productId|value|3. +-------+-------------+---------+-----+4. | 25| 1| 1| 4|5. | 2| 2| 2| 7|6. | 3| 4| 2| null|7. | null| 5| 2| null|8. +-------+-------------+---------+-----+Code block:sc.union([transactionsDfMonday, transactionsDfTuesday])

• The DataFrames' RDDs need to be passed into the sc.union method instead of the DataFrame variable names.
  
• Instead of union, the concat method should be used, making sure to not use its default arguments.
  
• Instead of the Spark context, transactionDfMonday should be called with the join method instead of the union method, making sure to use its default arguments.
  
• Instead of the Spark context, transactionDfMonday should be called with the union method.
  
• Instead of the Spark context, transactionDfMonday should be called with the unionByName method instead of the union method, making sure to not use its default arguments.
  

The code block shown below should return a single-column DataFrame with a column named consonant_ct that, for each row, shows the number of consonants in column itemName of DataFrameitemsDf. Choose the answer that correctly fills the blanks in the code block to accomplish this.DataFrame itemsDf:1. +------+----------------------------------+-----------------------------+-------------------+2. |itemId|itemName |attributes |supplier |3. +------+----------------------------------+-----------------------------+-------------------+4. |1 |Thick Coat for Walking in the Snow|[blue, winter, cozy] |Sports Company Inc.|5. |2 |Elegant Outdoors Summer Dress |[red, summer, fresh, cooling]|YetiX |6. |3 |Outdoors Backpack |[green, summer, travel] |Sports Company Inc.|7. +------+----------------------------------+-----------------------------+-------------------+Code block:itemsDf.select(__1__(__2__(__3__(__4__), 'a|e|i|o|u|\s', '')).__5__('consonant_ct'))

• 1. length
  2. regexp_extract
  3. upper
  4. col('itemName')
  5. as
  
• 1. size
  2. regexp_replace
  3. lower
  4. 'itemName'
  5. alias
  
• 1. lower
  2. regexp_replace
  3. length
  4. 'itemName'
  5. alias
  
• 1. length
  2. regexp_replace
  3. lower
  4. col('itemName')
  5. alias
  
• 1. size
  2. regexp_extract
  3. lower
  4. col('itemName')
  5. alias
  

Which of the following code blocks creates a new DataFrame with two columns season and wind_speed_ms where column season is of data type string and column wind_speed_ms is of data typedouble?

• spark.DataFrame({'season': ['winter','summer'], 'wind_speed_ms': [4.5, 7.5]})
  
• spark.createDataFrame([('summer', 4.5), ('winter', 7.5)], ['season', 'wind_speed_ms'])
  C.
  1. from pyspark.sql import types as T
  2. spark.createDataFrame((('summer', 4.5), ('winter', 7.5)), T.StructType([T.StructField('season', T.CharType()), T.StructField('season', T.DoubleType())]))
  
• spark.newDataFrame([('summer', 4.5), ('winter', 7.5)], ['season', 'wind_speed_ms'])
  
• spark.createDataFrame({'season': ['winter','summer'], 'wind_speed_ms': [4.5, 7.5]})
  

Which of the following code blocks displays the 10 rows with the smallest values of column value in DataFrame transactionsDf in a nicely formatted way?

• transactionsDf.sort(asc(value)).show(10)
  
• transactionsDf.sort(col('value')).show(10)
  
• transactionsDf.sort(col('value').desc()).head()
  
• transactionsDf.sort(col('value').asc()).print(10)
  
• transactionsDf.orderBy('value').asc().show(10)