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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q125-Q130):NEW QUESTION # 125
8 of 55.
A data scientist at a large e-commerce company needs to process and analyze 2 TB of daily customer transaction data. The company wants to implement real-time fraud detection and personalized product recommendations.
Currently, the company uses a traditional relational database system, which struggles with the increasing data volume and velocity.
Which feature of Apache Spark effectively addresses this challenge?
  • A. Ability to process small datasets efficiently
  • B. Support for SQL queries on structured data
  • C. Built-in machine learning libraries
  • D. In-memory computation and parallel processing capabilities
Answer: D
Explanation:
Apache Spark was designed for big data and high-velocity workloads. Its core strength lies in its in-memory computation and parallel distributed processing model.
These features allow Spark to:
Process large-scale datasets quickly across many nodes.
Support real-time and near-real-time analytics for tasks like fraud detection and recommendations.
Minimize disk I/O through caching and memory persistence.
Thus, the key advantage in this use case is Spark's ability to handle large data volumes efficiently using distributed, in-memory computation.
Why the other options are incorrect:
A: Spark is optimized for large, not small, datasets.
C: SQL support is useful but doesn't solve the scalability issue.
D: MLlib supports machine learning but relies on Spark's parallel computation for speed.
Reference:
Databricks Exam Guide (June 2025): Section "Apache Spark Architecture and Components" - identifies Spark's advantages: in-memory processing, distributed computation, and scalability.
Apache Spark 3.5 Overview - Key design goals and cluster computation model.

NEW QUESTION # 126
A data engineer noticed improved performance after upgrading from Spark 3.0 to Spark 3.5. The engineer found that Adaptive Query Execution (AQE) was enabled.
Which operation is AQE implementing to improve performance?
  • A. Dynamically switching join strategies
  • B. Collecting persistent table statistics and storing them in the metastore for future use
  • C. Optimizing the layout of Delta files on disk
  • D. Improving the performance of single-stage Spark jobs
Answer: A
Explanation:
Comprehensive and Detailed Explanation:
Adaptive Query Execution (AQE) is a Spark 3.x feature that dynamically optimizes query plans at runtime.
One of its core features is:
Dynamically switching join strategies (e.g., from sort-merge to broadcast) based on runtime statistics.
Other AQE capabilities include:
Coalescing shuffle partitions
Skew join handling
Option A is correct.
Option B refers to statistics collection, which is not AQE's primary function.
Option C is too broad and not AQE-specific.
Option D refers to Delta Lake optimizations, unrelated to AQE.
Final Answer: A

NEW QUESTION # 127
31 of 55.
Given a DataFrame df that has 10 partitions, after running the code:
df.repartition(20)
How many partitions will the result DataFrame have?
  • A. 0
  • B. 1
  • C. 2
  • D. Same number as the cluster executors
Answer: A
Explanation:
The repartition(n) transformation reshuffles data into exactly n partitions.
Unlike coalesce(), repartition() always causes a shuffle to evenly redistribute the data.
Correct behavior:
df2 = df.repartition(20)
df2.rdd.getNumPartitions() # returns 20
Thus, the resulting DataFrame will have 20 partitions.
Why the other options are incorrect:
A/D: Doesn't retain old partition count - it's explicitly set to 20.
C: Number of partitions is not automatically tied to executors.
Reference:
PySpark DataFrame API - repartition() vs. coalesce().
Databricks Exam Guide (June 2025): Section "Developing Apache Spark DataFrame/DataSet API Applications" - tuning partitioning and shuffling for performance.

NEW QUESTION # 128
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(raw: str) -> str:
  • B. def shake_256(df: pd.Series) -> str:
  • C. def shake_256(df: pd.Series) -> pd.Series:
  • D. def shake_256(df: Iterator[pd.Series]) -> Iterator[pd.Series]:
Answer: C
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 # 129
7 of 55.
A developer has been asked to debug an issue with a Spark application. The developer identified that the data being loaded from a CSV file is being read incorrectly into a DataFrame.
The CSV file has been read using the following Spark SQL statement:
CREATE TABLE locations
USING csv
OPTIONS (path '/data/locations.csv')
The first lines of the command SELECT * FROM locations look like this:
| city | lat | long |
| ALTI Sydney | -33... | ... |
Which parameter can the developer add to the OPTIONS clause in the CREATE TABLE statement to read the CSV data correctly again?
  • A. 'sep' '|'
  • B. 'header' 'true'
  • C. 'sep' ','
  • D. 'header' 'false'
Answer: B
Explanation:
When reading CSV files using Spark SQL or the DataFrame API, Spark by default assumes that the first line of the file is data, not headers. To interpret the first line as column names, the header option must be set to true.
Correct syntax:
CREATE TABLE locations
USING csv
OPTIONS (
path '/data/locations.csv',
header 'true'
);
This tells Spark to read the first row as column headers and correctly map columns like city, lat, and long.
Why the other options are incorrect:
B (header 'false'): Default behavior; would keep reading header as data.
C / D (sep): Used to specify the delimiter; not relevant unless the file uses a different separator (e.g., |).
Reference (Databricks Apache Spark 3.5 - Python / Study Guide):
PySpark SQL Data Sources - CSV options (header, inferSchema, sep).
Databricks Exam Guide (June 2025): Section "Using Spark SQL" - Reading data from files with different formats using Spark SQL and DataFrame APIs.

NEW QUESTION # 130
......
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