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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:

Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:

1. 18 of 55.
An engineer has two DataFrames - df1 (small) and df2 (large). To optimize the join, the engineer uses a broadcast join:
from pyspark.sql.functions import broadcast
df_result = df2.join(broadcast(df1), on="id", how="inner")
What is the purpose of using broadcast() in this scenario?

A) It reduces the number of shuffle operations by replicating the smaller DataFrame to all nodes.
B) It increases the partition size for df1 and df2.
C) It ensures that the join happens only when the id values are identical.
D) It filters the id values before performing the join.

2. 23 of 55.
A data scientist is working with a massive dataset that exceeds the memory capacity of a single machine. The data scientist is considering using Apache Spark™ instead of traditional single-machine languages like standard Python scripts.
Which two advantages does Apache Spark™ offer over a normal single-machine language in this scenario? (Choose 2 answers)

A) It requires specialized hardware to run, making it unsuitable for commodity hardware clusters.
B) It has built-in fault tolerance, allowing it to recover seamlessly from node failures during computation.
C) It can distribute data processing tasks across a cluster of machines, enabling horizontal scalability.
D) It processes data solely on disk storage, reducing the need for memory resources.
E) It eliminates the need to write any code, automatically handling all data processing.

3. Given a CSV file with the content:

And the following code:
from pyspark.sql.types import *
schema = StructType([
StructField("name", StringType()),
StructField("age", IntegerType())
])
spark.read.schema(schema).csv(path).collect()
What is the resulting output?

A) [Row(name='alladin', age=20)]
B) [Row(name='bambi', age=None), Row(name='alladin', age=20)]
C) The code throws an error due to a schema mismatch.
D) [Row(name='bambi'), Row(name='alladin', age=20)]

4. 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', mergeAll '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', inferSchema 'true');

5. 17 of 55.
A data engineer has noticed that upgrading the Spark version in their applications from Spark 3.0 to Spark 3.5 has improved the runtime of some scheduled Spark applications.
Looking further, the data engineer realizes that Adaptive Query Execution (AQE) is now enabled.
Which operation should AQE be implementing to automatically improve the Spark application performance?

A) Dynamically switching join strategies
B) Improving the performance of single-stage Spark jobs
C) Collecting persistent table statistics and storing them in the metastore for future use
D) Optimizing the layout of Delta files on disk

Solutions: