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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q26-Q31):

NEW QUESTION # 26
A data engineer is reviewing a Spark application that applies several transformations to a DataFrame but notices that the job does not start executing immediately.
Which two characteristics of Apache Spark's execution model explain this behavior?
Choose 2 answers:

A. Transformations are evaluated lazily.
B. Only actions trigger the execution of the transformation pipeline.
C. The Spark engine requires manual intervention to start executing transformations.
D. The Spark engine optimizes the execution plan during the transformations, causing delays.
E. Transformations are executed immediately to build the lineage graph.

Answer: A,B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Apache Spark employs a lazy evaluation model for transformations. This means that when transformations (e.
g.,map(),filter()) are applied to a DataFrame, Spark does not execute them immediately. Instead, it builds a logical plan (lineage) of transformations to be applied.
Execution is deferred until an action (e.g.,collect(),count(),save()) is called. At that point, Spark's Catalyst optimizer analyzes the logical plan, optimizes it, and then executes the physical plan to produce the result.
This lazy evaluation strategy allows Spark to optimize the execution plan, minimize data shuffling, and improve overall performance by reducing unnecessary computations.

NEW QUESTION # 27
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.sort("age", "salary", ascending=[True, True]).show()
B. df.orderBy("age", "salary", ascending=[True, False]).show()
C. df.orderBy(col("age").asc(), col("salary").asc()).show()
D. df.sort("age", "salary", ascending=[False, True]).show()

Answer: B

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 # 28
A data engineer is building a Structured Streaming pipeline and wants the pipeline to recover from failures or intentional shutdowns by continuing where the pipeline left off.
How can this be achieved?

A. By configuring the optioncheckpointLocationduringreadStream
B. By configuring the optionrecoveryLocationduring the SparkSession initialization
C. By configuring the optioncheckpointLocationduringwriteStream
D. By configuring the optionrecoveryLocationduringwriteStream

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
To enable a Structured Streaming query to recover from failures or intentional shutdowns, it is essential to specify thecheckpointLocationoption during thewriteStreamoperation. This checkpoint location stores the progress information of the streaming query, allowing it to resume from where it left off.
According to the Databricks documentation:
"You must specify thecheckpointLocationoption before you run a streaming query, as in the following example:
option("checkpointLocation", "/path/to/checkpoint/dir")
toTable("catalog.schema.table")
- Databricks Documentation: Structured Streaming checkpoints
By setting thecheckpointLocationduringwriteStream, Spark can maintain state information and ensure exactly- once processing semantics, which are crucial for reliable streaming applications.

NEW QUESTION # 29
What is the difference betweendf.cache()anddf.persist()in Spark DataFrame?

A. cache()- Persists the DataFrame with the default storage level (MEMORY_AND_DISK) andpersist()- Can be used to set different storage levels to persist the contents of the DataFrame
B. Bothcache()andpersist()can be used to set the default storage level (MEMORY_AND_DISK_SER)
C. persist()- Persists the DataFrame with the default storage level (MEMORY_AND_DISK_SER) andcache()- Can be used to set different storage levels to persist the contents of the DataFrame.
D. Both functions perform the same operation. Thepersist()function provides improved performance asits default storage level isDISK_ONLY.

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
df.cache()is shorthand fordf.persist(StorageLevel.MEMORY_AND_DISK)
df.persist()allows specifying any storage level such asMEMORY_ONLY,DISK_ONLY, MEMORY_AND_DISK_SER, etc.
By default,persist()usesMEMORY_AND_DISK, unless specified otherwise.
Reference:Spark Programming Guide - Caching and Persistence

NEW QUESTION # 30
A Spark developer wants to improve the performance of an existing PySpark UDF that runs a hash function that is not available in the standard Spark functions library. The existing UDF code is:

import hashlib
import pyspark.sql.functions as sf
from pyspark.sql.types import StringType
def shake_256(raw):
return hashlib.shake_256(raw.encode()).hexdigest(20)
shake_256_udf = sf.udf(shake_256, StringType())
The developer wants to replace this existing UDF with a Pandas UDF to improve performance. The developer changes the definition ofshake_256_udfto this:CopyEdit shake_256_udf = sf.pandas_udf(shake_256, StringType()) However, the developer receives the error:
What should the signature of theshake_256()function be changed to in order to fix this error?

A. def shake_256(df: pd.Series) -> str:
B. def shake_256(df: pd.Series) -> pd.Series:
C. def shake_256(raw: str) -> str:
D. def shake_256(df: Iterator[pd.Series]) -> Iterator[pd.Series]:

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
When converting a standard PySpark UDF to a Pandas UDF for performance optimization, the function must operate on a Pandas Series as input and return a Pandas Series as output.
In this case, the original function signature:
def shake_256(raw: str) -> str
is scalar - not compatible with Pandas UDFs.
According to the official Spark documentation:
"Pandas UDFs operate onpandas.Seriesand returnpandas.Series. The function definition should be:
def my_udf(s: pd.Series) -> pd.Series:
and it must be registered usingpandas_udf(...)."
Therefore, to fix the error:
The function should be updated to:
def shake_256(df: pd.Series) -> pd.Series:
return df.apply(lambda x: hashlib.shake_256(x.encode()).hexdigest(20))
This will allow Spark to efficiently execute the Pandas UDF in vectorized form, improving performance compared to standard UDFs.
Reference: Apache Spark 3.5 Documentation # User-Defined Functions # Pandas UDFs

NEW QUESTION # 31
......

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