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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q112-Q117):

NEW QUESTION # 112
A Spark application developer wants to identify which operations cause shuffling, leading to a new stage in the Spark execution plan.
Which operation results in a shuffle and a new stage?

A. DataFrame.groupBy().agg()
B. DataFrame.withColumn()
C. DataFrame.filter()
D. DataFrame.select()

Answer: A

Explanation:
Operations that trigger data movement across partitions (like groupBy, join, repartition) result in a shuffle and a new stage.
From Spark documentation:
"groupBy and aggregation cause data to be shuffled across partitions to combine rows with the same key." Option A (groupBy + agg) → causes shuffle.
Options B, C, and D (filter, withColumn, select) → transformations that do not require shuffling; they are narrow dependencies.
Final answer: A

NEW QUESTION # 113
A data engineer needs to persist a file-based data source to a specific location. However, by default, Spark writes to the warehouse directory (e.g., /user/hive/warehouse). To override this, the engineer must explicitly define the file path.
Which line of code ensures the data is saved to a specific location?
Options:

A. users.write.saveAsTable("default_table", path="/some/path")
B. users.write.saveAsTable("default_table").option("path", "/some/path")
C. users.write(path="/some/path").saveAsTable("default_table")
D. users.write.option("path", "/some/path").saveAsTable("default_table")

Answer: D

Explanation:
To persist a table and specify the save path, use:
users.write.option("path","/some/path").saveAsTable("default_table")
The .option("path", ...) must be applied before calling saveAsTable.
Option A uses invalid syntax (write(path=...)).
Option B applies.option()after.saveAsTable()-which is too late.
Option D uses incorrect syntax (no path parameter in saveAsTable).
Reference:Spark SQL - Save as Table

NEW QUESTION # 114
32 of 55.
A developer is creating a Spark application that performs multiple DataFrame transformations and actions. The developer wants to maintain optimal performance by properly managing the SparkSession.
How should the developer handle the SparkSession throughout the application?

A. Avoid using a SparkSession and rely on SparkContext only.
B. Create a new SparkSession instance before each transformation.
C. Stop and restart the SparkSession after each action.
D. Use a single SparkSession instance for the entire application.

Answer: D

Explanation:
The SparkSession is the entry point to Spark functionality in modern versions (2.x and later). It unifies the SparkContext, SQLContext, and HiveContext into a single object.
Best Practice:
Use one SparkSession for the entire application.
Create it once at the start using SparkSession.builder.getOrCreate().
Reuse it across all transformations and actions.
Stop it only after all operations are completed.
Example:
from pyspark.sql import SparkSession
spark = SparkSession.builder.appName("MyApp").getOrCreate()
# Perform transformations and actions
spark.stop()
Why the other options are incorrect:
B: SparkSession is the recommended interface; SparkContext alone is deprecated for SQL/DataFrame APIs.
C: Creating multiple sessions increases overhead and wastes resources.
D: Restarting SparkSession breaks lineage and adds unnecessary startup costs.
Reference:
Spark API Reference - SparkSession lifecycle.
Databricks Exam Guide (June 2025): Section "Apache Spark Architecture and Components" - explains SparkSession lifecycle and application management.

NEW QUESTION # 115
38 of 55.
A data engineer is working with Spark SQL and has a large JSON file stored at /data/input.json.
The file contains records with varying schemas, and the engineer wants to create an external table in Spark SQL that:
Reads directly from /data/input.json.
Infers the schema automatically.
Merges differing schemas.
Which code snippet should the engineer use?

A. CREATE TABLE users
USING json
OPTIONS (path '/data/input.json');
B. CREATE EXTERNAL TABLE users
USING json
OPTIONS (path '/data/input.json', inferSchema 'true');
C. CREATE EXTERNAL TABLE users
USING json
OPTIONS (path '/data/input.json', mergeSchema 'true');
D. CREATE EXTERNAL TABLE users
USING json
OPTIONS (path '/data/input.json', mergeAll 'true');

Answer: C

Explanation:
To handle JSON files with evolving or differing schemas, Spark SQL supports the option mergeSchema 'true', which merges all fields across files into a unified schema.
Correct syntax:
CREATE EXTERNAL TABLE users
USING json
OPTIONS (path '/data/input.json', mergeSchema 'true');
This creates an external table directly on the JSON data, inferring schema automatically and merging variations.
Why the other options are incorrect:
B: Missing schema merge configuration - fails with inconsistent files.
C: inferSchema applies to CSV/other file types, not JSON.
D: mergeAll is not a valid Spark SQL option.
Reference:
Spark SQL Data Sources - JSON file options (mergeSchema, path).
Databricks Exam Guide (June 2025): Section "Using Spark SQL" - creating external tables and schema inference for JSON data.

NEW QUESTION # 116
54 of 55.
What is the benefit of Adaptive Query Execution (AQE)?

A. It allows Spark to optimize the query plan before execution but does not adapt during runtime.
B. It automatically distributes tasks across nodes in the clusters and does not perform runtime adjustments to the query plan.
C. It enables the adjustment of the query plan during runtime, handling skewed data, optimizing join strategies, and improving overall query performance.
D. It optimizes query execution by parallelizing tasks and does not adjust strategies based on runtime metrics like data skew.

Answer: C

Explanation:
Adaptive Query Execution (AQE) is a Spark SQL feature introduced to dynamically optimize queries at runtime based on actual data statistics collected during execution.
Key benefits include:
Runtime plan adaptation: Spark adjusts the physical plan after some stages complete.
Skew handling: Automatically splits skewed partitions to balance work distribution.
Join strategy optimization: Dynamically switches between shuffle join and broadcast join depending on partition sizes.
Coalescing shuffle partitions: Reduces the number of small tasks for better performance.
Example configuration:
spark.conf.set("spark.sql.adaptive.enabled", True)
This enables AQE globally in Spark 3.5.
Why the other options are incorrect:
A: AQE adapts during runtime, not only before execution.
B: Task distribution is a base Spark feature, not specific to AQE.
C: AQE specifically addresses runtime skew and join adjustments.
Reference:
Spark SQL Adaptive Query Execution Guide - Runtime optimization, skew handling, and join strategy adjustment.
Databricks Exam Guide (June 2025): Section "Troubleshooting and Tuning Apache Spark DataFrame API Applications" - Adaptive Query Execution benefits and configuration.

NEW QUESTION # 117
......

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