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

1. A data scientist of an e-commerce company is working with user data obtained from its subscriber database and has stored the data in a DataFrame df_user. Before further processing the data, the data scientist wants to create another DataFrame df_user_non_pii and store only the non-PII columns in this DataFrame. The PII columns in df_user are first_name, last_name, email, and birthdate.
Which code snippet can be used to meet this requirement?

A) df_user_non_pii = df_user.drop("first_name", "last_name", "email", "birthdate")
B) df_user_non_pii = df_user.dropfields("first_name", "last_name", "email", "birthdate")
C) df_user_non_pii = df_user.drop("first_name", "last_name", "email", "birthdate")
D) df_user_non_pii = df_user.dropfields("first_name, last_name, email, birthdate")

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

3. 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_idvalues.
Which code fragment meets the requirements?
A)

B)

C)

D)

The resulting Python dictionary must contain a mapping ofregion -> region_idfor the smallest
3region_idvalues.
Which code fragment meets the requirements?

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

4. A data engineer is working with a large JSON dataset containing order information. The dataset is stored in a distributed file system and needs to be loaded into a Spark DataFrame for analysis. The data engineer wants to ensure that the schema is correctly defined and that the data is read efficiently.
Which approach should the data scientist use to efficiently load the JSON data into a Spark DataFrame with a predefined schema?

A) Define a StructType schema and use spark.read.schema(predefinedSchema).json() to load the data.
B) Use spark.read.json() to load the data, then use DataFrame.printSchema() to view the inferred schema, and finally use DataFrame.cast() to modify column types.
C) Use spark.read.format("json").load() and then use DataFrame.withColumn() to cast each column to the desired data type.
D) Use spark.read.json() with the inferSchema option set to true

5. An engineer notices a significant increase in the job execution time during the execution of a Spark job. After some investigation, the engineer decides to check the logs produced by the Executors.
How should the engineer retrieve the Executor logs to diagnose performance issues in the Spark application?

A) Locate the executor logs on the Spark master node, typically under the/tmpdirectory.
B) Use the Spark UI to select the stage and view the executor logs directly from the stages tab.
C) Use the commandspark-submitwith the-verboseflag to print the logs to the console.
D) Fetch the logs by running a Spark job with thespark-sqlCLI tool.

Solutions: