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 displayed below contains an error. When the code block below has executed, it should have divided DataFrame transactionsDf into 14 parts, based on columns storeId andtransactionDate (in this order). Find the error.Code block:transactionsDf.coalesce(14, ('storeId', 'transactionDate'))

• The parentheses around the column names need to be removed and .select() needs to be appended to the code block.
  
• Operator coalesce needs to be replaced by repartition, the parentheses around the column names need to be removed, and .count() needs to be appended to the code block.
  (Correct)
  
• Operator coalesce needs to be replaced by repartition, the parentheses around the column names need to be removed, and .select() needs to be appended to the code block.
  
• Operator coalesce needs to be replaced by repartition and the parentheses around the column names need to be replaced by square brackets.
  
• Operator coalesce needs to be replaced by repartition.
  

Which of the following code blocks performs an inner join between DataFrame itemsDf and DataFrame transactionsDf, using columns itemId and transactionId as join keys, respectively?

• itemsDf.join(transactionsDf, 'inner', itemsDf.itemId == transactionsDf.transactionId)
  
• itemsDf.join(transactionsDf, itemId == transactionId)
  
• itemsDf.join(transactionsDf, itemsDf.itemId == transactionsDf.transactionId, 'inner')
  
• itemsDf.join(transactionsDf, 'itemsDf.itemId == transactionsDf.transactionId', 'inner')
  
• itemsDf.join(transactionsDf, col(itemsDf.itemId) == col(transactionsDf.transactionId))
  

Which of the following code blocks reads in the parquet file stored at location filePath, given that all columns in the parquet file contain only whole numbers and are stored in the most appropriateformat for this kind of data?

• 1. spark.read.schema(
  2. StructType(
  3. StructField('transactionId', IntegerType(), True),
  4. StructField('predError', IntegerType(), True)
  5. )).load(filePath)
  
• 1. spark.read.schema([
  2. StructField('transactionId', NumberType(), True),
  3. StructField('predError', IntegerType(), True)
  4. ]).load(filePath)
  
• 1. spark.read.schema(
  2. StructType([
  3. StructField('transactionId', StringType(), True),
  4. StructField('predError', IntegerType(), True)]
  5. )).parquet(filePath)
  
• 1. spark.read.schema(
  2. StructType([
  3. StructField('transactionId', IntegerType(), True),
  4. StructField('predError', IntegerType(), True)]
  5. )).format('parquet').load(filePath)
  
• 1. spark.read.schema([
  2. StructField('transactionId', IntegerType(), True),
  3. StructField('predError', IntegerType(), True)
  4. ]).load(filePath, format='parquet')
  

Which of the following code blocks produces the following output, given DataFrame transactionsDf?Output:1. root2. |-- transactionId: integer (nullable = true)3. |-- predError: integer (nullable = true)4. |-- value: integer (nullable = true)5. |-- storeId: integer (nullable = true)6. |-- productId: integer (nullable = true)7. |-- f: integer (nullable = true)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. +-------------+---------+-----+-------+---------+----+

• transactionsDf.schema.print()
  
• transactionsDf.rdd.printSchema()
  
• transactionsDf.rdd.formatSchema()
  
• transactionsDf.printSchema()
  
• print(transactionsDf.schema)
  

Which of the following statements about executors is correct?

• Executors are launched by the driver.
  
• Executors stop upon application completion by default.
  
• Each node hosts a single executor.
  
• Executors store data in memory only.
  
• An executor can serve multiple applications.
  

Which of the following statements about the differences between actions and transformations is correct?

• Actions are evaluated lazily, while transformations are not evaluated lazily.
  
• Actions generate RDDs, while transformations do not.
  
• Actions do not send results to the driver, while transformations do.
  
• Actions can be queued for delayed execution, while transformations can only be processed immediately.
  
• Actions can trigger Adaptive Query Execution, while transformation cannot.
  

Which of the following code blocks removes all rows in the 6-column DataFrame transactionsDf that have missing data in at least 3 columns?

• transactionsDf.dropna('any')
  
• transactionsDf.dropna(thresh=4)
  
• transactionsDf.drop.na('',2)
  
• transactionsDf.dropna(thresh=2)
  
• transactionsDf.dropna('',4)
  

The code block displayed below contains an error. The code block should count the number of rows that have a predError of either 3 or 6. Find the error.Code block:transactionsDf.filter(col('predError').in([3, 6])).count()

• The number of rows cannot be determined with the count() operator.
  
• Instead of filter, the select method should be used.
  
• The method used on column predError is incorrect.
  
• Instead of a list, the values need to be passed as single arguments to the in operator.
  
• Numbers 3 and 6 need to be passed as string variables.
  

The code block shown below should set the number of partitions that Spark uses when shuffling data for joins or aggregations to 100. Choose the answer that correctly fills the blanks in the codeblock to accomplish this.spark.sql.shuffle.partitions__1__.__2__.__3__(__4__, 100)

• 1. spark
  2. conf
  3. set
  4. 'spark.sql.shuffle.partitions'
  
• 1. pyspark
  2. config
  3. set
  4. spark.shuffle.partitions
  
• 1. spark
  2. conf
  3. get
  4. 'spark.sql.shuffle.partitions'
  
• 1. pyspark
  2. config
  3. set
  4. 'spark.sql.shuffle.partitions'
  
• 1. spark
  2. conf
  3. set
  4. 'spark.sql.aggregate.partitions'
  

Which of the following statements about broadcast variables is correct?

• Broadcast variables are serialized with every single task.
  
• Broadcast variables are commonly used for tables that do not fit into memory.
  
• Broadcast variables are immutable.
  
• Broadcast variables are occasionally dynamically updated on a per-task basis.
  
• Broadcast variables are local to the worker node and not shared across the cluster.