Expert Answers to Databricks Exam Databricks-Certified-Associate-Developer-for-Apache-Spark-3.5 Questions

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Databricks Certification Databricks Certified Associate Developer for Apache Spark 3.5 – Python

Databricks Certified Associate Developer for Apache Spark 3.5 – Python

Last Update Jul 30, 2026
Total Questions : 136

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Questions 2

3 of 55. A data engineer observes that the upstream streaming source feeds the event table frequently and sends duplicate records. Upon analyzing the current production table, the data engineer found that the time difference in the event_timestamp column of the duplicate records is, at most, 30 minutes.

To remove the duplicates, the engineer adds the code:

df = df.withWatermark("event_timestamp", "30 minutes")

What is the result?

Options:

It removes all duplicates regardless of when they arrive.

It accepts watermarks in seconds and the code results in an error.

It removes duplicates that arrive within the 30-minute window specified by the watermark.

It is not able to handle deduplication in this scenario.

Discussion 0

Questions 3

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?

Options:

customerDF.select(

col("email").substr(0, 5).alias("username"),

col("email").substr(-5).alias("domain")

)

customerDF.withColumn("username", split(col("email"), "@").getItem(0)) \

.withColumn("domain", split(col("email"), "@").getItem(1))

customerDF.withColumn("username", substring_index(col("email"), "@", 1)) \

.withColumn("domain", substring_index(col("email"), "@", -1))

customerDF.select(

regexp_replace(col("email"), "@", "").alias("username"),

regexp_replace(col("email"), "@", "").alias("domain")

)

Discussion 0

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Questions 4

A data engineer is working on the DataFrame:

Questions 4

(Referring to the table image: it has columns Id, Name, count, and timestamp.)

Which code fragment should the engineer use to extract the unique values in the Name column into an alphabetically ordered list?

Options:

df.select("Name").orderBy(df["Name"].asc())

df.select("Name").distinct().orderBy(df["Name"])

df.select("Name").distinct()

df.select("Name").distinct().orderBy(df["Name"].desc())

Discussion 0

Questions 5

A data engineer wants to process a streaming DataFrame that receives sensor readings every second with columns sensor_id, temperature, and timestamp. The engineer needs to calculate the average temperature for each sensor over the last 5 minutes while the data is streaming.

Which code implementation achieves the requirement?

Options from the images provided:

Questions 5