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

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

2. A data engineer writes the following code to join two DataFrames df1 and df2:
df1 = spark.read.csv("sales_data.csv") # ~10 GB
df2 = spark.read.csv("product_data.csv") # ~8 MB
result = df1.join(df2, df1.product_id == df2.product_id)

Which join strategy will Spark use?

A) Shuffle join, as the size difference between df1 and df2 is too large for a broadcast join to work efficiently
B) Broadcast join, as df2 is smaller than the default broadcast threshold
C) Shuffle join, because AQE is not enabled, and Spark uses a static query plan
D) Shuffle join because no broadcast hints were provided

3. 49 of 55.
In the code block below, aggDF contains aggregations on a streaming DataFrame:
aggDF.writeStream \
.format("console") \
.outputMode("???") \
.start()
Which output mode at line 3 ensures that the entire result table is written to the console during each trigger execution?

A) APPEND
B) AGGREGATE
C) REPLACE
D) COMPLETE

4. 24 of 55.
Which code should be used to display the schema of the Parquet file stored in the location events.parquet?

A) spark.read.parquet("events.parquet").printSchema()
B) spark.read.format("parquet").load("events.parquet").show()
C) spark.sql("SELECT schema FROM events.parquet").show()
D) spark.sql("SELECT * FROM events.parquet").show()

5. A Spark application is experiencing performance issues in client mode because the driver is resource-constrained.
How should this issue be resolved?

A) Switch the deployment mode to local mode
B) Switch the deployment mode to cluster mode
C) Increase the driver memory on the client machine
D) Add more executor instances to the cluster

Solutions: