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

1. A developer is trying to join two tables, sales.purchases_fct and sales.customer_dim, using the following code:

fact_df = purch_df.join(cust_df, F.col('customer_id') == F.col('custid')) The developer has discovered that customers in the purchases_fct table that do not exist in the customer_dim table are being dropped from the joined table.
Which change should be made to the code to stop these customer records from being dropped?

A) fact_df = purch_df.join(cust_df, F.col('customer_id') == F.col('custid'), 'left')
B) fact_df = purch_df.join(cust_df, F.col('cust_id') == F.col('customer_id'))
C) fact_df = cust_df.join(purch_df, F.col('customer_id') == F.col('custid'))
D) fact_df = purch_df.join(cust_df, F.col('customer_id') == F.col('custid'), 'right_outer')

2. 41 of 55.
A data engineer is working on the DataFrame df1 and wants the Name with the highest count to appear first (descending order by count), followed by the next highest, and so on.
The DataFrame has columns:
id | Name | count | timestamp
---------------------------------
1 | USA | 10
2 | India | 20
3 | England | 50
4 | India | 50
5 | France | 20
6 | India | 10
7 | USA | 30
8 | USA | 40
Which code fragment should the engineer use to sort the data in the Name and count columns?

A) df1.orderBy("Name", "count")
B) df1.sort("Name", "count")
C) df1.orderBy(col("count").desc(), col("Name").asc())
D) df1.orderBy(col("Name").desc(), col("count").asc())

3. A data analyst wants to add a column date derived from a timestamp column.
Options:

A) dates_df.withColumn("date", f.unix_timestamp("timestamp")).show()
B) dates_df.withColumn("date", f.from_unixtime("timestamp")).show()
C) dates_df.withColumn("date", f.date_format("timestamp", "yyyy-MM-dd")).show()
D) dates_df.withColumn("date", f.to_date("timestamp")).show()

4. Given this view definition:
df.createOrReplaceTempView("users_vw")
Which approach can be used to query the users_vw view after the session is terminated?
Options:

A) Persist the users_vw data as a table
B) Recreate the users_vw and query the data using Spark
C) Save the users_vw definition and query using Spark
D) Query the users_vw using Spark

5. 40 of 55.
A developer wants to refactor older Spark code to take advantage of built-in functions introduced in Spark 3.5.
The original code:
from pyspark.sql import functions as F
min_price = 110.50
result_df = prices_df.filter(F.col("price") > min_price).agg(F.count("*")) Which code block should the developer use to refactor the code?

A) result_df = prices_df.withColumn("valid_price", when(col("price") > F.lit(min_price), True))
B) result_df = prices_df.filter(F.col("price") > F.lit(min_price)).agg(F.count("*"))
C) result_df = prices_df.filter(F.lit(min_price) > F.col("price")).count()
D) result_df = prices_df.where(F.lit("price") > min_price).groupBy().count()

Solutions: