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

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

2. What is a feature of Spark Connect?

A) Supports DataFrame, Functions, Column, SparkContext PySpark APIs
B) It supports DataStreamReader, DataStreamWriter, StreamingQuery, and Streaming APIs
C) It supports only PySpark applications
D) It has built-in authentication

3. 21 of 55.
What is the behavior of the function date_sub(start, days) if a negative value is passed into the days parameter?

A) The same start date will be returned.
B) The number of days specified will be added to the start date.
C) An error message of an invalid parameter will be returned.
D) The number of days specified will be removed from the start date.

4. A Spark DataFrame df is cached using the MEMORY_AND_DISK storage level, but the DataFrame is too large to fit entirely in memory.
What is the likely behavior when Spark runs out of memory to store the DataFrame?

A) Spark will store as much data as possible in memory and spill the rest to disk when memory is full, continuing processing with performance overhead.
B) Spark stores the frequently accessed rows in memory and less frequently accessed rows on disk, utilizing both resources to offer balanced performance.
C) Spark duplicates the DataFrame in both memory and disk. If it doesn't fit in memory, the DataFrame is stored and retrieved from the disk entirely.
D) Spark splits the DataFrame evenly between memory and disk, ensuring balanced storage utilization.

5. You have:
DataFrame A: 128 GB of transactions
DataFrame B: 1 GB user lookup table
Which strategy is correct for broadcasting?

A) DataFrame A should be broadcasted because it is smaller and will eliminate the need for shuffling itself
B) DataFrame A should be broadcasted because it is larger and will eliminate the need for shuffling DataFrame B
C) DataFrame B should be broadcasted because it is smaller and will eliminate the need for shuffling itself
D) DataFrame B should be broadcasted because it is smaller and will eliminate the need for shuffling DataFrame A

Solutions: