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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. 11 of 55.
Which Spark configuration controls the number of tasks that can run in parallel on an executor?

A) spark.sql.shuffle.partitions
B) spark.task.maxFailures
C) spark.executor.cores
D) spark.executor.memory

2. A Data Analyst is working on the DataFrame sensor_df, which contains two columns:
Which code fragment returns a DataFrame that splits the record column into separate columns and has one array item per row?
A)

B)

C)

D)

A) exploded_df = exploded_df.select("record_datetime", "record_exploded")
B) exploded_df = exploded_df.select(
"record_datetime",
"record_exploded.sensor_id",
"record_exploded.status",
"record_exploded.health"
)
exploded_df = sensor_df.withColumn("record_exploded", explode("record"))
C) exploded_df = sensor_df.withColumn("record_exploded", explode("record")) exploded_df = exploded_df.select("record_datetime", "sensor_id", "status", "health")
D) exploded_df = exploded_df.select(
"record_datetime",
"record_exploded.sensor_id",
"record_exploded.status",
"record_exploded.health"
)
exploded_df = sensor_df.withColumn("record_exploded", explode("record"))

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

4. 10 of 55.
What is the benefit of using Pandas API on Spark for data transformations?

A) It runs on a single node only, utilizing memory efficiently.
B) It executes queries faster using all the available cores in the cluster as well as provides Pandas's rich set of features.
C) It computes results immediately using eager execution.
D) It is available only with Python, thereby reducing the learning curve.

5. 47 of 55.
A data engineer has written the following code to join two DataFrames df1 and df2:
df1 = spark.read.csv("sales_data.csv")
df2 = spark.read.csv("product_data.csv")
df_joined = df1.join(df2, df1.product_id == df2.product_id)
The DataFrame df1 contains ~10 GB of sales data, and df2 contains ~8 MB of product data.
Which join strategy will Spark use?

A) Broadcast join, as df2 is smaller than the default broadcast threshold.
B) Shuffle join, as the size difference between df1 and df2 is too large for a broadcast join to work efficiently.
C) Shuffle join, because AQE is not enabled, and Spark uses a static query plan.
D) Shuffle join because no broadcast hints were provided.

Solutions: