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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. A data scientist has identified that some records in the user profile table contain null values in any of the fields, and such records should be removed from the dataset before processing. The schema includes fields like user_id, username, date_of_birth, created_ts, etc.
The schema of the user profile table looks like this:

Which block of Spark code can be used to achieve this requirement?
Options:

A) filtered_df = users_raw_df.na.drop(how='all')
B) filtered_df = users_raw_df.na.drop(thresh=0)
C) filtered_df = users_raw_df.na.drop(how='any')
D) filtered_df = users_raw_df.na.drop(how='all', thresh=None)

2. 48 of 55.
A data engineer needs to join multiple DataFrames and has written the following code:
from pyspark.sql.functions import broadcast
data1 = [(1, "A"), (2, "B")]
data2 = [(1, "X"), (2, "Y")]
data3 = [(1, "M"), (2, "N")]
df1 = spark.createDataFrame(data1, ["id", "val1"])
df2 = spark.createDataFrame(data2, ["id", "val2"])
df3 = spark.createDataFrame(data3, ["id", "val3"])
df_joined = df1.join(broadcast(df2), "id", "inner") \
.join(broadcast(df3), "id", "inner")
What will be the output of this code?

A) The code will work correctly and perform two broadcast joins simultaneously to join df1 with df2, and then the result with df3.
B) The code will fail because only one broadcast join can be performed at a time.
C) The code will fail because the second join condition (df2.id == df3.id) is incorrect.
D) The code will result in an error because broadcast() must be called before the joins, not inline.

3. 7 of 55.
A developer has been asked to debug an issue with a Spark application. The developer identified that the data being loaded from a CSV file is being read incorrectly into a DataFrame.
The CSV file has been read using the following Spark SQL statement:
CREATE TABLE locations
USING csv
OPTIONS (path '/data/locations.csv')
The first lines of the command SELECT * FROM locations look like this:
| city | lat | long |
| ALTI Sydney | -33... | ... |
Which parameter can the developer add to the OPTIONS clause in the CREATE TABLE statement to read the CSV data correctly again?

A) 'sep' ','
B) 'sep' '|'
C) 'header' 'false'
D) 'header' 'true'

4. A data scientist is working with a Spark DataFrame called customerDF that contains customer information. The DataFrame has a column named email with customer email addresses. The data scientist needs to split this column into username and domain parts.
Which code snippet splits the email column into username and domain columns?

A) customerDF.withColumn("username", split(col("email"), "@").getItem(0)) \
.withColumn("domain", split(col("email"), "@").getItem(1))
B) customerDF.withColumn("username", substring_index(col("email"), "@", 1)) \
.withColumn("domain", substring_index(col("email"), "@", -1))
C) customerDF.select(
regexp_replace(col("email"), "@", "").alias("username"),
regexp_replace(col("email"), "@", "").alias("domain")
)
D) customerDF.select(
col("email").substr(0, 5).alias("username"),
col("email").substr(-5).alias("domain")
)

5. A data scientist is working on a project that requires processing large amounts of structured data, performing SQL queries, and applying machine learning algorithms. The data scientist is considering using Apache Spark for this task.
Which combination of Apache Spark modules should the data scientist use in this scenario?
Options:

A) Spark Streaming, GraphX, and Pandas API on Spark
B) Spark DataFrames, Spark SQL, and MLlib
C) Spark DataFrames, Structured Streaming, and GraphX
D) Spark SQL, Pandas API on Spark, and Structured Streaming

Solutions: