30 Questions
What is the correct object to fill in gap 2 in the code block?
spark
Which option correctly fills gap 3 in the code block?
DataFrameReader
What is the correct parameter to fill in gap 4 in the code block?
comment='#'
Which object should be used to evaluate the number of columns?
len
Why can option B be eliminated as a correct answer?
DataFrame and shape are not compatible for evaluation.
Which option provides an incorrect parameter value for reading a CSV file?
'shape'
What is the role of the cluster manager in client mode?
Allocating resources to Spark applications and maintaining executor processes
Where is the cluster manager located when operating in cluster mode?
Cluster nodes
What action does the cluster manager take in remote mode?
Maintaining executor processes on the cluster nodes
Which of the following is NOT a role of the cluster manager?
Managing the DataFrame operations
In which mode does the cluster manager start and end executor processes?
Cluster mode
What is the primary function of the cluster manager in Spark applications?
Allocating and managing cluster resources
To perform an inner join between DataFrames transactionsDf and itemsDf on columns productId and itemId, which code block should be used?
transactionsDf.drop('value', 'storeId').join(itemsDf.select('attributes'), transactionsDf.productId==itemsDf.itemId)
Which option correctly excludes columns 'value' and 'storeId' from DataFrame transactionsDf?
transactionsDf.drop('value', 'storeId')
What is the purpose of using createOrReplaceTempView() in the context of DataFrames?
It creates a temporary view that can be used in SQL queries
Which scenario would result in an incorrect inner join between two DataFrames?
Removing all columns from one of the DataFrames
In the context of DataFrame joins, what does the 'ON' clause specify?
The condition for matching rows between DataFrames
Which operation is NOT performed in the provided code block for joining DataFrames?
.drop('attributes')
What method can be used to display the column names and types of a DataFrame in a tree-like structure?
itemsDf.rdd.printSchema()
Which method can be used to change the data type of a column from integer to string in a DataFrame?
itemsDf.withColumn('itemId', convert('itemId', 'string'))
Which method can be used to select all columns in a DataFrame with their corresponding data types?
print(itemsDf.columns)
Which action is incorrect regarding the DataFrame's underlying RDD?
itemsDf.print.schema()
What does the 'element: string (containsNull = true)' represent in the DataFrame's structure?
It signifies that the column 'element' contains nullable strings.
What is the correct method to convert a column's data type in Spark from integer to string?
itemsDf.withColumn('itemId', col('itemId').cast('string'))
What is the main requirement regarding the number of slots and tasks in Spark?
There is no specific requirement on the number of slots compared to tasks
Why is having just a single slot for multiple tasks not recommended in Spark?
It prevents distributed data processing over multiple cores and machines
Which of the following statements accurately represents the relationship between executors and tasks in Spark?
There is no specific requirement on the number of executors compared to tasks
What does the code 'transactionsDf.groupBy('productId').agg(col('value').count())' achieve?
Groups by 'productId' but does not provide counts
Why is calling 'transactionsDf.count('productId').distinct()' incorrect?
'count()' function does not take arguments in Spark
Which DataFrame operation is necessary to get a 2-column DataFrame showing distinct 'productId' values and the number of rows with each 'productId'?
transactionsDf.groupBy('productId').count()
Test your knowledge on filling in the correct method calls in a Spark DataFrame code block. Choose the option that correctly populates the blanks to achieve the desired outcome based on the given context.
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