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

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

2. What is the risk associated with this operation when converting a large Pandas API on Spark DataFrame back to a Pandas DataFrame?

A) The conversion will automatically distribute the data across worker nodes
B) The operation will fail if the Pandas DataFrame exceeds 1000 rows
C) The operation will load all data into the driver's memory, potentially causing memory overflow
D) Data will be lost during conversion

3. An engineer has two DataFrames: df1 (small) and df2 (large). A broadcast join is used:
python
CopyEdit
from pyspark.sql.functions import broadcast
result = df2.join(broadcast(df1), on='id', how='inner')
What is the purpose of using broadcast() in this scenario?
Options:

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

4. Which feature of Spark Connect is considered when designing an application to enable remote interaction with the Spark cluster?

A) It can be used to interact with any remote cluster using the REST API
B) It provides a way to run Spark applications remotely in any programming language
C) It allows for remote execution of Spark jobs
D) It is primarily used for data ingestion into Spark from external sources

5. A developer is running Spark SQL queries and notices underutilization of resources. Executors are idle, and the number of tasks per stage is low.
What should the developer do to improve cluster utilization?

A) Increase the value of spark.sql.shuffle.partitions
B) Increase the size of the dataset to create more partitions
C) Reduce the value of spark.sql.shuffle.partitions
D) Enable dynamic resource allocation to scale resources as needed

Solutions: