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

1. Which UDF implementation calculates the length of strings in a Spark DataFrame?

A) df.withColumn("length", udf(lambda s: len(s), StringType()))
B) spark.udf.register("stringLength", lambda s: len(s))
C) df.select(length(col("stringColumn")).alias("length"))
D) df.withColumn("length", spark.udf("len", StringType()))

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

3. A developer notices that all the post-shuffle partitions in a dataset are smaller than the value set for spark.sql.adaptive.maxShuffledHashJoinLocalMapThreshold.
Which type of join will Adaptive Query Execution (AQE) choose in this case?

A) A shuffled hash join
B) A Cartesian join
C) A broadcast nested loop join
D) A sort-merge join

4. 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"), "@", ""))

5. An application architect has been investigating Spark Connect as a way to modernize existing Spark applications running in their organization.
Which requirement blocks the adoption of Spark Connect in this organization?

A) Complete Spark API support: the ability to migrate all existing code to Spark Connect without modification, including the RDD APIs
B) Upgradability: the ability to upgrade the Spark applications independently from the Spark driver itself
C) Stability: isolation of application code and dependencies from each other and the Spark driver
D) Debuggability: the ability to perform interactive debugging directly from the application code

Solutions: