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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. 12 of 55.
A data scientist has been investigating user profile data to build features for their model. After some exploratory data analysis, the data scientist identified that some records in the user profiles contain NULL values in too many fields to be useful.
The schema of the user profile table looks like this:
user_id STRING,
username STRING,
date_of_birth DATE,
country STRING,
created_at TIMESTAMP
The data scientist decided that if any record contains a NULL value in any field, they want to remove that record from the output before further processing.
Which block of Spark code can be used to achieve these requirements?

A) filtered_users = raw_users.dropna(how="any")
B) filtered_users = raw_users.dropna(how="all")
C) filtered_users = raw_users.na.drop("all")
D) filtered_users = raw_users.na.drop("any")

2. 31 of 55.
Given a DataFrame df that has 10 partitions, after running the code:
df.repartition(20)
How many partitions will the result DataFrame have?

A) 20
B) Same number as the cluster executors
C) 5
D) 10

3. A data engineer is working on a real-time analytics pipeline using Apache Spark Structured Streaming. The engineer wants to process incoming data and ensure that triggers control when the query is executed. The system needs to process data in micro-batches with a fixed interval of 5 seconds.
Which code snippet the data engineer could use to fulfil this requirement?
A)

B)

C)

D)

Options:

A) Uses trigger() - default micro-batch trigger without interval.
B) Uses trigger(processingTime=5000) - invalid, as processingTime expects a string.
C) Uses trigger(continuous='5 seconds') - continuous processing mode.
D) Uses trigger(processingTime='5 seconds') - correct micro-batch trigger with interval.

4. How can a Spark developer ensure optimal resource utilization when running Spark jobs in Local Mode for testing?
Options:

A) Use the spark.dynamicAllocation.enabled property to scale resources dynamically.
B) Set the spark.executor.memory property to a large value.
C) Configure the application to run in cluster mode instead of local mode.
D) Increase the number of local threads based on the number of CPU cores.

5. Given the code fragment:

import pyspark.pandas as ps
psdf = ps.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
Which method is used to convert a Pandas API on Spark DataFrame (pyspark.pandas.DataFrame) into a standard PySpark DataFrame (pyspark.sql.DataFrame)?

A) psdf.to_pandas()
B) psdf.to_pyspark()
C) psdf.to_dataframe()
D) psdf.to_spark()

Solutions: