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

1. A data scientist is working on a large dataset in Apache Spark using PySpark. The data scientist has a DataFrame df with columns user_id, product_id, and purchase_amount and needs to perform some operations on this data efficiently.
Which sequence of operations results in transformations that require a shuffle followed by transformations that do not?

A) df.groupBy("user_id").agg(sum("purchase_amount").alias("total_purchase")).repartition(10)
B) df.withColumn("discount", df.purchase_amount * 0.1).select("discount")
C) df.withColumn("purchase_date", current_date()).where("total_purchase > 50")
D) df.filter(df.purchase_amount > 100).groupBy("user_id").sum("purchase_amount")

2. A data engineer is running a batch processing job on a Spark cluster with the following configuration:
10 worker nodes
16 CPU cores per worker node
64 GB RAM per node
The data engineer wants to allocate four executors per node, each executor using four cores.
What is the total number of CPU cores used by the application?

A) 64
B) 160
C) 40
D) 80

3. 13 of 55.
A developer needs to produce a Python dictionary using data stored in a small Parquet table, which looks like this:
region_id
region_name
10
North
12
East
14
West
The resulting Python dictionary must contain a mapping of region_id to region_name, containing the smallest 3 region_id values.
Which code fragment meets the requirements?

A) regions_dict = regions.select("region_id", "region_name").take(3)
B) regions_dict = dict(regions.select("region_id", "region_name").rdd.collect())
C) regions_dict = dict(regions.orderBy("region_id").limit(3).rdd.map(lambda x: (x.region_id, x.region_name)).collect())
D) regions_dict = dict(regions.take(3))

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

5. 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, resources are allocated from a resource manager on the cluster, enabling better performance and scalability for large jobs
C) In cluster mode, the driver runs on the client machine, which can limit the application's ability to handle large datasets efficiently.
D) 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.

Solutions: