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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q111-Q116):

NEW QUESTION # 111
48 of 55.
A data engineer needs to join multiple DataFrames and has written the following code:
from pyspark.sql.functions import broadcast
data1 = [(1, "A"), (2, "B")]
data2 = [(1, "X"), (2, "Y")]
data3 = [(1, "M"), (2, "N")]
df1 = spark.createDataFrame(data1, ["id", "val1"])
df2 = spark.createDataFrame(data2, ["id", "val2"])
df3 = spark.createDataFrame(data3, ["id", "val3"])
df_joined = df1.join(broadcast(df2), "id", "inner")
.join(broadcast(df3), "id", "inner")
What will be the output of this code?

A. The code will work correctly and perform two broadcast joins simultaneously to join df1 with df2, and then the result with df3.
B. The code will result in an error because broadcast() must be called before the joins, not inline.
C. The code will fail because only one broadcast join can be performed at a time.
D. The code will fail because the second join condition (df2.id == df3.id) is incorrect.

Answer: A

Explanation:
Spark supports multiple broadcast joins in a single query plan, as long as each broadcasted DataFrame is small enough to fit under the configured threshold.
Execution Plan:
Spark broadcasts df2 to all executors.
Joins df1 (big) with broadcasted df2.
Then broadcasts df3 and performs another join with the intermediate result.
The result is efficient and avoids shuffling large data.
Why the other options are incorrect:
B: Multiple broadcast joins are supported in Spark 3.x.
C: The join condition is correct since all use id as the key.
D: broadcast() can be used inline; it's valid syntax.
Reference:
PySpark SQL Functions - broadcast() usage.
Databricks Exam Guide (June 2025): Section "Developing Apache Spark DataFrame/DataSet API Applications" - multiple broadcast join optimization.

NEW QUESTION # 112
Given this view definition:
df.createOrReplaceTempView("users_vw")
Which approach can be used to query the users_vw view after the session is terminated?
Options:

A. Query the users_vw using Spark
B. Persist the users_vw data as a table
C. Save the users_vw definition and query using Spark
D. Recreate the users_vw and query the data using Spark

Answer: B

Explanation:
Temp views likecreateOrReplaceTempVieware session-scoped.
They disappear once the Spark session ends.
To retain data across sessions, it must be persisted:
df.write.saveAsTable("users_vw")
Thus, the view needs to be persisted as a table to survive session termination.
Reference:Databricks - Temp vs Global vs Permanent Views

NEW QUESTION # 113
A data engineer needs to write a DataFramedfto a Parquet file, partitioned by the columncountry, and overwrite any existing data at the destination path.
Which code should the data engineer use to accomplish this task in Apache Spark?

A. df.write.partitionBy("country").parquet("/data/output")
B. df.write.mode("overwrite").parquet("/data/output")
C. df.write.mode("append").partitionBy("country").parquet("/data/output")
D. df.write.mode("overwrite").partitionBy("country").parquet("/data/output")

Answer: D

Explanation:
The.mode("overwrite")ensures that existing files at the path will be replaced.
partitionBy("country")optimizes queries by writing data into partitioned folders.
Correct syntax:
df.write.mode("overwrite").partitionBy("country").parquet("/data/output")
- Source:Spark SQL, DataFrames and Datasets Guide

NEW QUESTION # 114
Which command overwrites an existing JSON file when writing a DataFrame?

A. df.write.format("json").save("path/to/file", mode="overwrite")
B. df.write.overwrite.json("path/to/file")
C. df.write.mode("overwrite").json("path/to/file")
D. df.write.json("path/to/file", overwrite=True)

Answer: C

Explanation:
The correct way to overwrite an existing file using the DataFrameWriter is:
df.write.mode("overwrite").json("path/to/file")
Option D is also technically valid, but Option A is the most concise and idiomatic PySpark syntax.

NEW QUESTION # 115
Which Spark configuration controls the number of tasks that can run in parallel on the executor?
Options:

A. spark.task.maxFailures
B. spark.driver.cores
C. spark.executor.cores
D. spark.executor.memory

Answer: C

Explanation:
spark.executor.cores determines how many concurrent tasks an executor can run.
For example, if set to 4, each executor can run up to 4 tasks in parallel.
Other settings:
spark.task.maxFailures controls task retry logic.
spark.driver.cores is for the driver, not executors.
spark.executor.memory sets memory limits, not task concurrency.

NEW QUESTION # 116
......

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