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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q33-Q38):

NEW QUESTION # 33
What is the benefit of using Pandas on Spark for data transformations?
Options:

A. It runs on a single node only, utilizing the memory with memory-bound DataFrames and hence cost- efficient.
B. It is available only with Python, thereby reducing the learning curve.
C. It executes queries faster using all the available cores in the cluster as well as provides Pandas's rich set of features.
D. It computes results immediately using eager execution, making it simple to use.

Answer: C

Explanation:
Pandas API on Spark (formerly Koalas) offers:
Familiar Pandas-like syntax
Distributed execution using Spark under the hood
Scalability for large datasets across the cluster
It provides the power of Spark while retaining the productivity of Pandas.
Reference:Pandas API on Spark Guide

NEW QUESTION # 34
A developer notices that all the post-shuffle partitions in a dataset are smaller than the value set forspark.sql.
adaptive.maxShuffledHashJoinLocalMapThreshold.
Which type of join will Adaptive Query Execution (AQE) choose in this case?

A. A Cartesian join
B. A broadcast nested loop join
C. A shuffled hash join
D. A sort-merge join

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Adaptive Query Execution (AQE) dynamically selects join strategies based on actual data sizes at runtime. If the size of post-shuffle partitions is below the threshold set by:
spark.sql.adaptive.maxShuffledHashJoinLocalMapThreshold
then Spark prefers to use a shuffled hash join.
From the Spark documentation:
"AQE selects a shuffled hash join when the size of post-shuffle data is small enough to fit within the configured threshold, avoiding more expensive sort-merge joins." Therefore:
A is wrong - Cartesian joins are only used with no join condition.
B is correct - this is the optimized join for small partitioned shuffle data under AQE.
C and D are used under other scenarios but not for this case.
Final Answer: B

NEW QUESTION # 35
An engineer has a large ORC file located at/file/test_data.orcand wants to read only specific columns to reduce memory usage.
Which code fragment will select the columns, i.e.,col1,col2, during the reading process?

A. spark.read.format("orc").select("col1", "col2").load("/file/test_data.orc")
B. spark.read.orc("/file/test_data.orc").selected("col1", "col2")
C. spark.read.format("orc").load("/file/test_data.orc").select("col1", "col2")
D. spark.read.orc("/file/test_data.orc").filter("col1 = 'value' ").select("col2")

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The correct way to load specific columns from an ORC file is to first load the file using.load()and then apply.
select()on the resulting DataFrame. This is valid with.read.format("orc")or the shortcut.read.orc().
df = spark.read.format("orc").load("/file/test_data.orc").select("col1","col2") Why others are incorrect:
Aperforms selection after filtering, but doesn't match the intention to minimize memory at load.
Bincorrectly tries to use.select()before.load(), which is invalid.
Cuses a non-existent.selected()method.
Dcorrectly loads and then selects.
Reference:Apache Spark SQL API - ORC Format

NEW QUESTION # 36
An engineer wants to join two DataFramesdf1anddf2on the respectiveemployee_idandemp_idcolumns:
df1:employee_id INT,name STRING
df2:emp_id INT,department STRING
The engineer uses:
result = df1.join(df2, df1.employee_id == df2.emp_id, how='inner')
What is the behaviour of the code snippet?

A. The code fails to execute because the column names employee_id and emp_id do not match automatically
B. The code works as expected because the join condition explicitly matches employee_id from df1 with emp_id from df2
C. The code fails to execute because it must use on='employee_id' to specify the join column explicitly
D. The code fails to execute because PySpark does not support joining DataFrames with a different structure

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In PySpark, when performing a join between two DataFrames, the columns do not have to share the same name. You can explicitly provide a join condition by comparing specific columns from each DataFrame.
This syntax is correct and fully supported:
df1.join(df2, df1.employee_id == df2.emp_id, how='inner')
This will perform an inner join betweendf1anddf2using theemployee_idfromdf1andemp_idfromdf2.
Reference: Databricks Spark 3.5 Documentation # DataFrame API # join()

NEW QUESTION # 37
Which command overwrites an existing JSON file when writing a DataFrame?

A. df.write.json("path/to/file", overwrite=True)
B. df.write.overwrite.json("path/to/file")
C. df.write.mode("overwrite").json("path/to/file")
D. df.write.format("json").save("path/to/file", mode="overwrite")

Answer: C

Explanation:
The correct way to overwrite an existing file using the DataFrameWriter is:
df.write.mode("overwrite").json("path/to/file")
Option D is also technically valid, but Option A is the most concise and idiomatic PySpark syntax.
Reference:PySpark DataFrameWriter API

NEW QUESTION # 38
......

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Leo Davis Leo Davis

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