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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:

1. 33 of 55.
The data engineering team created a pipeline that extracts data from a transaction system.
The transaction system stores timestamps in UTC, and the data engineers must now transform the transaction_datetime field to the "America/New_York" timezone for reporting.
Which code should be used to convert the timestamp to the target timezone?

A) raw.withColumn("transaction_datetime", from_utc_timestamp(col("transaction_datetime"), "America/New_York"))
B) raw.withColumn("transaction_datetime", to_utc_timestamp(col("transaction_datetime"), "America/New_York"))
C) raw.withColumn("transaction_datetime", convert_timezone(col("transaction_datetime"), "America/New_York"))
D) raw.withColumn("transaction_datetime", date_format(col("transaction_datetime"), "America/New_York"))

2. A Data Analyst is working on the DataFrame sensor_df, which contains two columns:
Which code fragment returns a DataFrame that splits the record column into separate columns and has one array item per row?
A)

B)

C)

D)

A) exploded_df = exploded_df.select(
"record_datetime",
"record_exploded.sensor_id",
"record_exploded.status",
"record_exploded.health"
)
exploded_df = sensor_df.withColumn("record_exploded", explode("record"))
B) exploded_df = exploded_df.select("record_datetime", "record_exploded")
C) exploded_df = sensor_df.withColumn("record_exploded", explode("record")) exploded_df = exploded_df.select("record_datetime", "sensor_id", "status", "health")
D) exploded_df = exploded_df.select(
"record_datetime",
"record_exploded.sensor_id",
"record_exploded.status",
"record_exploded.health"
)
exploded_df = sensor_df.withColumn("record_exploded", explode("record"))

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 number of days specified will be removed from the start date.
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 same start date will be returned.

4. 12 of 55.
A data scientist has been investigating user profile data to build features for their model. After some exploratory data analysis, the data scientist identified that some records in the user profiles contain NULL values in too many fields to be useful.
The schema of the user profile table looks like this:
user_id STRING,
username STRING,
date_of_birth DATE,
country STRING,
created_at TIMESTAMP
The data scientist decided that if any record contains a NULL value in any field, they want to remove that record from the output before further processing.
Which block of Spark code can be used to achieve these requirements?

A) filtered_users = raw_users.dropna(how="all")
B) filtered_users = raw_users.na.drop("all")
C) filtered_users = raw_users.na.drop("any")
D) filtered_users = raw_users.dropna(how="any")

5. 27 of 55.
A data engineer needs to add all the rows from one table to all the rows from another, but not all the columns in the first table exist in the second table.
The error message is:
AnalysisException: UNION can only be performed on tables with the same number of columns.
The existing code is:
au_df.union(nz_df)
The DataFrame au_df has one extra column that does not exist in the DataFrame nz_df, but otherwise both DataFrames have the same column names and data types.
What should the data engineer fix in the code to ensure the combined DataFrame can be produced as expected?

A) df = au_df.unionByName(nz_df, allowMissingColumns=True)
B) df = au_df.unionAll(nz_df)
C) df = au_df.unionByName(nz_df, allowMissingColumns=False)
D) df = au_df.union(nz_df, allowMissingColumns=True)

Solutions: