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

2. 54 of 55.
What is the benefit of Adaptive Query Execution (AQE)?

A) It allows Spark to optimize the query plan before execution but does not adapt during runtime.
B) It enables the adjustment of the query plan during runtime, handling skewed data, optimizing join strategies, and improving overall query performance.
C) It optimizes query execution by parallelizing tasks and does not adjust strategies based on runtime metrics like data skew.
D) It automatically distributes tasks across nodes in the clusters and does not perform runtime adjustments to the query plan.

3. 13 of 55.
A developer needs to produce a Python dictionary using data stored in a small Parquet table, which looks like this:
region_id
region_name
10
North
12
East
14
West
The resulting Python dictionary must contain a mapping of region_id to region_name, containing the smallest 3 region_id values.
Which code fragment meets the requirements?

A) regions_dict = dict(regions.select("region_id", "region_name").rdd.collect())
B) regions_dict = dict(regions.orderBy("region_id").limit(3).rdd.map(lambda x: (x.region_id, x.region_name)).collect())
C) regions_dict = regions.select("region_id", "region_name").take(3)
D) regions_dict = dict(regions.take(3))

4. A data engineer wants to write a Spark job that creates a new managed table. If the table already exists, the job should fail and not modify anything.
Which save mode and method should be used?

A) saveAsTable with mode ErrorIfExists
B) saveAsTable with mode Overwrite
C) save with mode ErrorIfExists
D) save with mode Ignore

5. 30 of 55.
A data engineer is working on a num_df DataFrame and has a Python UDF defined as:
def cube_func(val):
return val * val * val
Which code fragment registers and uses this UDF as a Spark SQL function to work with the DataFrame num_df?

A) num_df.register("cube_func").select("num").show()
B) spark.createDataFrame(cube_func("num")).show()
C) spark.udf.register("cube_func", cube_func)
num_df.selectExpr("cube_func(num)").show()
D) num_df.select(cube_func("num")).show()

Solutions: