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

1. A data engineer is working on the DataFrame:

(Referring to the table image: it has columns Id, Name, count, and timestamp.) Which code fragment should the engineer use to extract the unique values in the Name column into an alphabetically ordered list?

A) df.select("Name").distinct().orderBy(df["Name"])
B) df.select("Name").distinct()
C) df.select("Name").orderBy(df["Name"].asc())
D) df.select("Name").distinct().orderBy(df["Name"].desc())

2. 40 of 55.
A developer wants to refactor older Spark code to take advantage of built-in functions introduced in Spark 3.5.
The original code:
from pyspark.sql import functions as F
min_price = 110.50
result_df = prices_df.filter(F.col("price") > min_price).agg(F.count("*")) Which code block should the developer use to refactor the code?

A) result_df = prices_df.filter(F.lit(min_price) > F.col("price")).count()
B) result_df = prices_df.withColumn("valid_price", when(col("price") > F.lit(min_price), True))
C) result_df = prices_df.filter(F.col("price") > F.lit(min_price)).agg(F.count("*"))
D) result_df = prices_df.where(F.lit("price") > min_price).groupBy().count()

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

4. 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 program runs on one of the worker nodes, allowing the application to fully utilize the distributed resources of the cluster.
D) In cluster mode, the driver runs on the client machine, which can limit the application's ability to handle large datasets efficiently.

5. 25 of 55.
A Data Analyst is working on employees_df and needs to add a new column where a 10% tax is calculated on the salary.
Additionally, the DataFrame contains the column age, which is not needed.
Which code fragment adds the tax column and removes the age column?

A) employees_df = employees_df.dropField("age").withColumn("tax", col("salary") * 0.1)
B) employees_df = employees_df.withColumn("tax", col("salary") + 0.1).drop("age")
C) employees_df = employees_df.withColumn("tax", col("salary") * 0.1).drop("age")
D) employees_df = employees_df.withColumn("tax", lit(0.1)).drop("age")

Solutions: