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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q96-Q101):

NEW QUESTION # 96
A developer initializes a SparkSession:

spark = SparkSession.builder
.appName("Analytics Application")
.getOrCreate()
Which statement describes thesparkSparkSession?

A. If a SparkSession already exists, this code will return the existing session instead of creating a new one.
B. ThegetOrCreate()method explicitly destroys any existing SparkSession and creates a new one.
C. A SparkSession is unique for eachappName, and callinggetOrCreate()with the same name will return an existing SparkSession once it has been created.
D. A new SparkSession is created every time thegetOrCreate()method is invoked.

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
According to the PySpark API documentation:
"getOrCreate(): Gets an existing SparkSession or, if there is no existing one, creates a new one based on the options set in this builder." This means Spark maintains a global singleton session within a JVM process. Repeated calls togetOrCreate() return the same session, unless explicitly stopped.
Option A is incorrect: the method does not destroy any session.
Option B incorrectly ties uniqueness toappName, which does not influence session reusability.
Option D is incorrect: it contradicts the fundamental behavior ofgetOrCreate().
(Source:PySpark SparkSession API Docs)

NEW QUESTION # 97
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' 'false'
C. 'sep' ','
D. 'header' 'true'

Answer: D

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 # 98
41 of 55.
A data engineer is working on the DataFrame df1 and wants the Name with the highest count to appear first (descending order by count), followed by the next highest, and so on.
The DataFrame has columns:
id | Name | count | timestamp
---------------------------------
1 | USA | 10
2 | India | 20
3 | England | 50
4 | India | 50
5 | France | 20
6 | India | 10
7 | USA | 30
8 | USA | 40
Which code fragment should the engineer use to sort the data in the Name and count columns?

A. df1.orderBy(col("count").desc(), col("Name").asc())
B. df1.sort("Name", "count")
C. df1.orderBy(col("Name").desc(), col("count").asc())
D. df1.orderBy("Name", "count")

Answer: A

Explanation:
To sort a Spark DataFrame by multiple columns, use .orderBy() (or .sort()) with column expressions.
Correct syntax for descending and ascending mix:
from pyspark.sql.functions import col
df1.orderBy(col("count").desc(), col("Name").asc())
This sorts primarily by count in descending order and secondarily by Name in ascending order (alphabetically).
Why the other options are incorrect:
B/C: Default sort order is ascending; won't place highest counts first.
D: Reverses sorting logic - sorts Name descending, not required.
Reference:
PySpark DataFrame API - orderBy() and col() for sorting with direction.
Databricks Exam Guide (June 2025): Section "Using Spark DataFrame APIs" - sorting, ordering, and column expressions.

NEW QUESTION # 99
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 distinct() transformation to combine similar partitions
B. Use the repartition() transformation with a lower number of partitions
C. Use the sortBy() transformation to reorganize the data
D. Use the coalesce() transformation with a lower number of partitions

Answer: D

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.
Reference:Spark API - coalesce

NEW QUESTION # 100
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 option checkpointLocation during readStream
B. By configuring the option checkpointLocation during writeStream
C. By configuring the option recoveryLocation during writeStream
D. By configuring the option recoveryLocation during the SparkSession initialization

Answer: B

Explanation:
To enable a Structured Streaming query to recover from failures or intentional shutdowns, it is essential to specify the checkpointLocation option during the writeStream operation. 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 the checkpointLocation option 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 the checkpointLocation during writeStream, Spark can maintain state information and ensure exactly-once processing semantics, which are crucial for reliable streaming applications.

NEW QUESTION # 101
......

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