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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q18-Q23):

NEW QUESTION # 18
A Spark application developer wants to identify which operations cause shuffling, leading to a new stage in the Spark execution plan.
Which operation results in a shuffle and a new stage?

A. DataFrame.select()
B. DataFrame.groupBy().agg()
C. DataFrame.withColumn()
D. DataFrame.filter()

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Operations that trigger data movement across partitions (like groupBy, join, repartition) result in a shuffle and a new stage.
From Spark documentation:
"groupBy and aggregation cause data to be shuffled across partitions to combine rows with the same key." Option A (groupBy + agg) # causes shuffle.
Options B, C, and D (filter, withColumn, select) # transformations that do not require shuffling; they are narrow dependencies.
Final Answer: A

NEW QUESTION # 19
What is the behavior for functiondate_sub(start, days)if a negative value is passed into thedaysparameter?

A. The number of days specified will be added to the start date
B. The same start date will be returned
C. An error message of an invalid parameter will be returned
D. The number of days specified will be removed from the start date

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The functiondate_sub(start, days)subtracts the number of days from the start date. If a negative number is passed, the behavior becomes a date addition.
Example:
SELECT date_sub('2024-05-01', -5)
-- Returns: 2024-05-06
So, a negative value effectively adds the absolute number of days to the date.
Reference: Spark SQL Functions # date_sub()

NEW QUESTION # 20
A data engineer needs to write a Streaming DataFrame as Parquet files.
Given the code:

Which code fragment should be inserted to meet the requirement?
A)

Which code fragment should be inserted to meet the requirement?

A. .format("parquet")
.option("path", "path/to/destination/dir")
B. .option("format", "parquet")
.option("location", "path/to/destination/dir")
C. CopyEdit
.option("format", "parquet")
.option("destination", "path/to/destination/dir")
D. .format("parquet")
.option("location", "path/to/destination/dir")

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
To write a structured streaming DataFrame to Parquet files, the correct way to specify the format and output directory is:
writeStream
format("parquet")
option("path", "path/to/destination/dir")
According to Spark documentation:
"When writing to file-based sinks (like Parquet), you must specify the path using the .option("path", ...) method. Unlike batch writes, .save() is not supported." Option A incorrectly uses.option("location", ...)(invalid for Parquet sink).
Option B incorrectly sets the format via.option("format", ...), which is not the correct method.
Option C repeats the same issue.
Option D is correct:.format("parquet")+.option("path", ...)is the required syntax.
Final Answer: D

NEW QUESTION # 21
You have:
DataFrame A: 128 GB of transactions
DataFrame B: 1 GB user lookup table
Which strategy is correct for broadcasting?

A. DataFrame A should be broadcasted because it is smaller and will eliminate the need for shuffling itself
B. DataFrame B should be broadcasted because it is smaller and will eliminate the need for shuffling itself
C. DataFrame A should be broadcasted because it is larger and will eliminate the need for shuffling DataFrame B
D. DataFrame B should be broadcasted because it is smaller and will eliminate the need for shuffling DataFrame A

Answer: D

Explanation:
Comprehensive and Detailed Explanation:
Broadcast joins work by sending the smaller DataFrame to all executors, eliminating the shuffle of the larger DataFrame.
From Spark documentation:
"Broadcast joins are efficient when one DataFrame is small enough to fit in memory. Spark avoids shuffling the larger table." DataFrame B (1 GB) fits within the default threshold and should be broadcasted.
It eliminates the need to shuffle the large DataFrame A.
Final Answer: B

NEW QUESTION # 22
A DataFramedfhas columnsname,age, andsalary. The developer needs to sort the DataFrame byagein ascending order andsalaryin descending order.
Which code snippet meets the requirement of the developer?

A. df.orderBy(col("age").asc(), col("salary").asc()).show()
B. df.sort("age", "salary", ascending=[True, True]).show()
C. df.sort("age", "salary", ascending=[False, True]).show()
D. df.orderBy("age", "salary", ascending=[True, False]).show()

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
To sort a PySpark DataFrame by multiple columns with mixed sort directions, the correct usage is:
python
CopyEdit
df.orderBy("age","salary", ascending=[True,False])
agewill be sorted in ascending order
salarywill be sorted in descending order
TheorderBy()andsort()methods in PySpark accept a list of booleans to specify the sort direction for each column.
Documentation Reference:PySpark API - DataFrame.orderBy

NEW QUESTION # 23
......

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