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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q86-Q91):

NEW QUESTION # 86
An engineer notices a significant increase in the job execution time during the execution of a Spark job. After some investigation, the engineer decides to check the logs produced by the Executors.
How should the engineer retrieve the Executor logs to diagnose performance issues in the Spark application?

A. Use the Spark UI to select the stage and view the executor logs directly from the stages tab.
B. Use the command spark-submit with the -verbose flag to print the logs to the console.
C. Fetch the logs by running a Spark job with the spark-sql CLI tool.
D. Locate the executor logs on the Spark master node, typically under the /tmp directory.

Answer: A

Explanation:
The Spark UI is the standard and most effective way to inspect executor logs, task time, input size, and shuffles.
From the Databricks documentation:
"You can monitor job execution via the Spark Web UI. It includes detailed logs and metrics, including task-level execution time, shuffle reads/writes, and executor memory usage." (Source: Databricks Spark Monitoring Guide) Option A is incorrect: logs are not guaranteed to be in /tmp, especially in cloud environments.
B . -verbose helps during job submission but doesn't give detailed executor logs.
D . spark-sql is a CLI tool for running queries, not for inspecting logs.
Hence, the correct method is using the Spark UI → Stages tab → Executor logs.

NEW QUESTION # 87
14 of 55.
A developer created a DataFrame with columns color, fruit, and taste, and wrote the data to a Parquet directory using:
df.write.partitionBy("color", "taste").parquet("/path/to/output")
What is the result of this code?

A. It throws an error if there are null values in either partition column.
B. It appends new partitions to an existing Parquet file.
C. It creates separate directories for each unique combination of color and taste.
D. It stores all data in a single Parquet file.

Answer: C

Explanation:
When writing a DataFrame using .partitionBy() in Spark, the data is physically organized into directory structures corresponding to unique combinations of the partition columns.
Example:
/path/to/output/color=Red/taste=Sweet/part-0001.parquet
/path/to/output/color=Green/taste=Sour/part-0002.parquet
This structure improves query performance by pruning partitions when filtering on these columns.
Why the other options are incorrect:
A: Appending requires .mode("append"), which isn't used here.
B: Null values in partition columns are handled; they don't raise errors.
D: Partitioning prevents storing all data in a single file.
Reference:
PySpark DataFrameWriter API - partitionBy() and .parquet() methods.
Databricks Exam Guide (June 2025): Section "Using Spark SQL" - partitioning and writing optimized output files.

NEW QUESTION # 88
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: pd.Series) -> pd.Series:
B. def shake_256(raw: [pd.Series]) -> pd.Series:
C. def shake_256(raw: str) -> str:
D. def shake_256(raw: [str]) -> [str]:

Answer: A

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 # 89
A Spark engineer must select an appropriate deployment mode for the Spark jobs.
What is the benefit of using cluster mode in Apache Spark™?

A. In cluster mode, the driver is responsible for executing all tasks locally without distributing them across the worker nodes.
B. In cluster mode, the driver program runs on one of the worker nodes, allowing the application to fully utilize the distributed resources of the cluster.
C. In cluster mode, the driver runs on the client machine, which can limit the application's ability to handle large datasets efficiently.
D. In cluster mode, resources are allocated from a resource manager on the cluster, enabling better performance and scalability for large jobs

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In Apache Spark's cluster mode:
"The driver program runs on the cluster's worker node instead of the client's local machine. This allows the driver to be close to the data and other executors, reducing network overhead and improving fault tolerance for production jobs." (Source: Apache Spark documentation -Cluster Mode Overview) This deployment is ideal for production environments where the job is submitted from a gateway node, and Spark manages the driver lifecycle on the cluster itself.
Option A is partially true but less specific than D.
Option B is incorrect: the driver never executes all tasks; executors handle distributed tasks.
Option C describes client mode, not cluster mode.

NEW QUESTION # 90
A Spark application suffers from too many small tasks due to excessive partitioning. How can this be fixed without a full shuffle?
Options:

A. Use the coalesce() transformation with a lower number of partitions
B. Use the repartition() transformation with a lower number of partitions
C. Use the sortBy() transformation to reorganize the data
D. Use the distinct() transformation to combine similar partitions

Answer: A

Explanation:
coalesce(n) reduces the number of partitions without triggering a full shuffle, unlike repartition().
This is ideal when reducing partition count, especially during write operations.

NEW QUESTION # 91
......

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