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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. A data engineer is working on a Streaming DataFrame streaming_df with the given streaming data:

Which operation is supported with streamingdf ?

A) streaming_df.orderBy("timestamp").limit(4)
B) streaming_df.filter (col("count") < 30).show()
C) streaming_df. select (countDistinct ("Name") )
D) streaming_df.groupby("Id") .count ()

2. A Spark engineer must select an appropriate deployment mode for the Spark jobs.
What is the benefit of using cluster mode in Apache Spark™?

A) In cluster mode, the driver is responsible for executing all tasks locally without distributing them across the worker nodes.
B) In cluster mode, the driver runs on the client machine, which can limit the application's ability to handle large datasets efficiently.
C) In cluster mode, the driver program runs on one of the worker nodes, allowing the application to fully utilize the distributed resources of the cluster.
D) In cluster mode, resources are allocated from a resource manager on the cluster, enabling better performance and scalability for large jobs

3. A data engineer wants to create an external table from a JSON file located at /data/input.json with the following requirements:
Create an external table named users
Automatically infer schema
Merge records with differing schemas
Which code snippet should the engineer use?
Options:

A) CREATE TABLE users USING json OPTIONS (path '/data/input.json')
B) CREATE EXTERNAL TABLE users USING json OPTIONS (path '/data/input.json', mergeSchema 'true')
C) CREATE EXTERNAL TABLE users USING json OPTIONS (path '/data/input.json')
D) CREATE EXTERNAL TABLE users USING json OPTIONS (path '/data/input.json', schemaMerge 'true')

4. A data engineer needs to persist a file-based data source to a specific location. However, by default, Spark writes to the warehouse directory (e.g., /user/hive/warehouse). To override this, the engineer must explicitly define the file path.
Which line of code ensures the data is saved to a specific location?
Options:

A) users.write.saveAsTable("default_table").option("path", "/some/path")
B) users.write.saveAsTable("default_table", path="/some/path")
C) users.write.option("path", "/some/path").saveAsTable("default_table")
D) users.write(path="/some/path").saveAsTable("default_table")

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

A) Data will be lost during conversion
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) The conversion will automatically distribute the data across worker nodes

Solutions: