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

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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:

Question #1
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 program runs on one of the worker nodes, allowing the application to fully utilize the distributed resources of the cluster.
B. In cluster mode, resources are allocated from a resource manager on the cluster, enabling better performance and scalability for large jobs
C. In cluster mode, the driver is responsible for executing all tasks locally without distributing them across the worker nodes.
D. In cluster mode, the driver runs on the client machine, which can limit the application's ability to handle large datasets efficiently.

Question #2
33 of 55.
The data engineering team created a pipeline that extracts data from a transaction system.
The transaction system stores timestamps in UTC, and the data engineers must now transform the transaction_datetime field to the "America/New_York" timezone for reporting.
Which code should be used to convert the timestamp to the target timezone?

A. raw.withColumn("transaction_datetime", to_utc_timestamp(col("transaction_datetime"), "America/New_York"))
B. raw.withColumn("transaction_datetime", date_format(col("transaction_datetime"), "America/New_York"))
C. raw.withColumn("transaction_datetime", from_utc_timestamp(col("transaction_datetime"), "America/New_York"))
D. raw.withColumn("transaction_datetime", convert_timezone(col("transaction_datetime"), "America/New_York"))

Question #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. .option("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. .format("parquet")
.option("location", "path/to/destination/dir")

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

Question #5
A developer needs to produce a Python dictionary using data stored in a small Parquet table, which looks like this:

The resulting Python dictionary must contain a mapping of region -> region id containing the smallest 3 region_id values.
Which code fragment meets the requirements?
A)

B)

C)

D)

The resulting Python dictionary must contain a mapping of region -> region_id for the smallest 3 region_id values.
Which code fragment meets the requirements?

A. regions = dict(
regions_df
.select('region_id', 'region')
.limit(3)
.collect()
)
B. regions = dict(
regions_df
.select('region_id', 'region')
.sort('region_id')
.take(3)
)
C. regions = dict(
regions_df
.select('region', 'region_id')
.sort(desc('region_id'))
.take(3)
)
D. regions = dict(
regions_df
.select('region', 'region_id')
.sort('region_id')
.take(3)
)

Solutions: