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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 filters the id values before performing the join.
C) It increases the partition size for df1 and df2.
D) It ensures that the join happens only when the id values are identical.

2. 14 of 55.
A developer created a DataFrame with columns color, fruit, and taste, and wrote the data to a Parquet directory using:
df.write.partitionBy("color", "taste").parquet("/path/to/output")
What is the result of this code?

A) It creates separate directories for each unique combination of color and taste.
B) It stores all data in a single Parquet file.
C) It appends new partitions to an existing Parquet file.
D) It throws an error if there are null values in either partition column.

3. A data engineer needs to write a Streaming DataFrame as Parquet files.
Given the code:

Which code fragment should be inserted to meet the requirement?
A)

B)

C)

D)

Which code fragment should be inserted to meet the requirement?

A) .format("parquet")
.option("location", "path/to/destination/dir")
B) .format("parquet")
.option("path", "path/to/destination/dir")
C) CopyEdit
.option("format", "parquet")
.option("destination", "path/to/destination/dir")
D) .option("format", "parquet")
.option("location", "path/to/destination/dir")

4. 34 of 55.
A data engineer is investigating a Spark cluster that is experiencing underutilization during scheduled batch jobs.
After checking the Spark logs, they noticed that tasks are often getting killed due to timeout errors, and there are several warnings about insufficient resources in the logs.
Which action should the engineer take to resolve the underutilization issue?

A) Increase the number of executor instances to handle more concurrent tasks.
B) Reduce the size of the data partitions to improve task scheduling.
C) Set the spark.network.timeout property to allow tasks more time to complete without being killed.
D) Increase the executor memory allocation in the Spark configuration.

5. 10 of 55.
What is the benefit of using Pandas API on Spark for data transformations?

A) It computes results immediately using eager execution.
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 is available only with Python, thereby reducing the learning curve.
D) It runs on a single node only, utilizing memory efficiently.

Solutions: