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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q124-Q129):

NEW QUESTION # 124
45 of 55.
Which feature of Spark Connect should be considered when designing an application that plans to enable remote interaction with a Spark cluster?

A. It allows for remote execution of Spark jobs.
B. It provides a way to run Spark applications remotely in any programming language.
C. It is primarily used for data ingestion into Spark from external sources.
D. It can be used to interact with any remote cluster using the REST API.

Answer: A

Explanation:
Spark Connect enables remote execution of Spark jobs by decoupling the client from the driver using the Spark Connect protocol (gRPC).
It allows users to run Spark code from different environments (like notebooks, IDEs, or remote clients) while executing jobs on the cluster.
Key Features:
Enables remote interaction between client and Spark driver.
Supports interactive development and lightweight client sessions.
Improves developer productivity without needing driver resources locally.
Why the other options are incorrect:
A: Spark Connect is not limited to ingestion tasks.
B: It allows multi-language clients (Python, Scala, etc.) but runs via Spark Connect API, not arbitrary remote code.
C: Uses gRPC protocol, not REST.
Reference:
Databricks Exam Guide (June 2025): Section "Using Spark Connect to Deploy Applications" - describes Spark Connect architecture and remote execution model.
Spark 3.5 Documentation - Spark Connect overview and client-server protocol.

NEW QUESTION # 125
What is the difference betweendf.cache()anddf.persist()in Spark DataFrame?

A. Bothcache()andpersist()can be used to set the default storage level (MEMORY_AND_DISK_SER)
B. Both functions perform the same operation. Thepersist()function provides improved performance asits default storage level isDISK_ONLY.
C. 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
D. 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.

Answer: C

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 # 126
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?

A. The job may fail if the executors do not have enough CPU cores to process the broadcasted dataset
B. The job may fail if the memory on each executor is not large enough to accommodate the DataFrame being broadcasted
C. The job will hang indefinitely as Spark will struggle to distribute and serialize such a large broadcast variable to all executors
D. The job may fail because the driver does not have enough CPU cores to serialize the large DataFrame

Answer: B

Explanation:
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.

NEW QUESTION # 127
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("age", "salary", ascending=[True, False]).show()
B. df.orderBy(col("age").asc(), col("salary").asc()).show()
C. df.sort("age", "salary", ascending=[True, True]).show()
D. df.sort("age", "salary", ascending=[False, True]).show()

Answer: A

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 # 128
19 of 55.
A Spark developer wants to improve the performance of an existing PySpark UDF that runs a hash function not available in the standard Spark functions library.
The existing UDF code is:
import hashlib
from pyspark.sql.types import StringType
def shake_256(raw):
return hashlib.shake_256(raw.encode()).hexdigest(20)
shake_256_udf = udf(shake_256, StringType())
The developer replaces this UDF with a Pandas UDF for better performance:
@pandas_udf(StringType())
def shake_256(raw: str) -> str:
return hashlib.shake_256(raw.encode()).hexdigest(20)
However, the developer receives this error:
TypeError: Unsupported signature: (raw: str) -> str
What should the signature of the shake_256() function be changed to in order to fix this error?

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

Answer: C

Explanation:
Pandas UDFs (vectorized UDFs) process entire Pandas Series objects, not scalar values. Each invocation operates on a column (Series) rather than a single value.
Correct syntax:
@pandas_udf(StringType())
def shake_256(raw: pd.Series) -> pd.Series:
return raw.apply(lambda x: hashlib.shake_256(x.encode()).hexdigest(20)) This allows Spark to apply the function in a vectorized way, improving performance significantly over traditional Python UDFs.
Why the other options are incorrect:
A/D: These define scalar functions - not compatible with Pandas UDFs.
B: Uses an invalid type hint [pd.Series] (not a valid Python type annotation).
Reference:
PySpark Pandas API - @pandas_udf decorator and function signatures.
Databricks Exam Guide (June 2025): Section "Using Pandas API on Apache Spark" - creating and invoking Pandas UDFs.

NEW QUESTION # 129
......

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