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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:

Question 1

A developer is working with a pandas DataFrame containing user behavior data from a web application.
Which approach should be used for executing a groupBy operation in parallel across all workers in Apache Spark 3.5?
A)
Use the applylnPandas API
B)

C)

A. Use a regular Spark UDF:
from pyspark.sql.functions import mean
df.groupBy("user_id").agg(mean("value")).show()
B. Use the mapInPandas API:
df.mapInPandas(mean_func, schema="user_id long, value double").show()
C. Use a Pandas UDF:
@pandas_udf("double")
def mean_func(value: pd.Series) -> float:
return value.mean()
df.groupby("user_id").agg(mean_func(df["value"])).show()
D. Use the applyInPandas API:
df.groupby("user_id").applyInPandas(mean_func, schema="user_id long, value double").show()

Question 2

37 of 55.
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 = customerDF \
.withColumn("username", split(col("email"), "@").getItem(0)) \
.withColumn("domain", split(col("email"), "@").getItem(1))
B. customerDF = customerDF.withColumn("domain", col("email").split("@")[1])
C. customerDF = customerDF.select("email").alias("username", "domain")
D. customerDF = customerDF.withColumn("username", regexp_replace(col("email"), "@", ""))

Question 3

What is the benefit of Adaptive Query Execution (AQE)?

A. It enables the adjustment of the query plan during runtime, handling skewed data, optimizing join strategies, and improving overall query performance.
B. It allows Spark to optimize the query plan before execution but does not adapt during runtime.
C. It optimizes query execution by parallelizing tasks and does not adjust strategies based on runtime metrics like data skew.
D. It automatically distributes tasks across nodes in the clusters and does not perform runtime adjustments to the query plan.

Question 4

A data engineer needs to write a DataFrame df to a Parquet file, partitioned by the column country, and overwrite any existing data at the destination path.
Which code should the data engineer use to accomplish this task in Apache Spark?

A. df.write.mode("overwrite").partitionBy("country").parquet("/data/output")
B. df.write.mode("overwrite").parquet("/data/output")
C. df.write.partitionBy("country").parquet("/data/output")
D. df.write.mode("append").partitionBy("country").parquet("/data/output")

Question 5

Given a CSV file with the content:

And the following code:
from pyspark.sql.types import *
schema = StructType([
StructField("name", StringType()),
StructField("age", IntegerType())
])
spark.read.schema(schema).csv(path).collect()
What is the resulting output?

A. [Row(name='bambi'), Row(name='alladin', age=20)]
B. The code throws an error due to a schema mismatch.
C. [Row(name='bambi', age=None), Row(name='alladin', age=20)]
D. [Row(name='alladin', age=20)]

Solutions: