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NEW QUESTION # 63
What is the relationship between jobs, stages, and tasks during execution in Apache Spark?
Options:
Answer: A
Explanation:
A Sparkjobis triggered by an action (e.g., count, show).
The job is broken intostages, typically one per shuffle boundary.
Eachstageis divided into multipletasks, which are distributed across worker nodes.
Reference:Spark Execution Model
NEW QUESTION # 64
The following code fragment results in an error:
@F.udf(T.IntegerType())
def simple_udf(t: str) -> str:
return answer * 3.14159
Which code fragment should be used instead?
Answer: B
Explanation:
Comprehensive and Detailed Explanation:
The original code has several issues:
It references a variable answer that is undefined.
The function is annotated to return a str, but the logic attempts numeric multiplication.
The UDF return type is declared as T.IntegerType() but the function performs a floating-point operation, which is incompatible.
Option B correctly:
Uses DoubleType to reflect the fact that the multiplication involves a float (3.14159).
Declares the input as float, which aligns with the multiplication.
Returns a float, which matches both the logic and the schema type annotation.
This structure aligns with how PySpark expects User Defined Functions (UDFs) to be declared:
"To define a UDF you must specify a Python function and provide the return type using the relevant Spark SQL type (e.g., DoubleType for float results)." Example from official documentation:
from pyspark.sql.functions import udf
from pyspark.sql.types import DoubleType
@udf(returnType=DoubleType())
def multiply_by_pi(x: float) -> float:
return x * 3.14159
This makes Option B the syntactically and semantically correct choice.
NEW QUESTION # 65
A Data Analyst is working on the DataFramesensor_df, which contains two columns:
Which code fragment returns a DataFrame that splits therecordcolumn into separate columns and has one array item per row?
A)
B)
C)
D)
Answer: A
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
To flatten an array of structs into individual rows and access fields within each struct, you must:
Useexplode()to expand the array so each struct becomes its own row.
Access the struct fields via dot notation (e.g.,record_exploded.sensor_id).
Option C does exactly that:
First, explode therecordarray column into a new columnrecord_exploded.
Then, access fields of the struct using the dot syntax inselect.
This is standard practice in PySpark for nested data transformation.
Final Answer: C
NEW QUESTION # 66
A data engineer uses a broadcast variable to share a DataFrame containing millions of rows across executors for lookup purposes. What will be the outcome?
Answer: C
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In Apache Spark, broadcast variables are used to efficiently distribute large, read-only data to all worker nodes. However, broadcasting very large datasets can lead to memory issues on executors if the data does not fit into the available memory.
According to the Spark documentation:
"Broadcast variables allow the programmer to keep a read-only variable cached on each machine rather than shipping a copy of it with tasks. This can greatly reduce the amount of data sent over the network." However, it also notes:
"Using the broadcast functionality available in SparkContext can greatly reduce the size of each serialized task, and the cost of launching a job over a cluster. If your tasks use any large object from the driver program inside of them (e.g., a static lookup table), consider turning it into a broadcast variable." But caution is advised when broadcasting large datasets:
"Broadcasting large variables can cause out-of-memory errors if the data does not fit in the memory of each executor." Therefore, if the broadcasted DataFrame containing millions of rows exceeds the memory capacity of the executors, the job may fail due to memory constraints.
Reference:Spark 3.5.5 Documentation - Tuning
NEW QUESTION # 67
A Data Analyst needs to retrieve employees with 5 or more years of tenure.
Which code snippet filters and shows the list?
Answer: D
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
To filter rows based on a condition and display them in Spark, usefilter(...).show():
employees_df.filter(employees_df.tenure >= 5).show()
Option A is correct and shows the results.
Option B filters but doesn't display them.
Option C uses Python's built-infilter, not Spark.
Option D collects the results to the driver, which is unnecessary if.show()is sufficient.
Final Answer: A
NEW QUESTION # 68
......
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