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

1. A Spark application suffers from too many small tasks due to excessive partitioning. How can this be fixed without a full shuffle?
Options:

A) Use the coalesce() transformation with a lower number of partitions
B) Use the distinct() transformation to combine similar partitions
C) Use the repartition() transformation with a lower number of partitions
D) Use the sortBy() transformation to reorganize the data

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

3. 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'), 'right_outer')
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'), 'left')

4. An application architect has been investigating Spark Connect as a way to modernize existing Spark applications running in their organization.
Which requirement blocks the adoption of Spark Connect in this organization?

A) Complete Spark API support: the ability to migrate all existing code to Spark Connect without modification, including the RDD APIs
B) Upgradability: the ability to upgrade the Spark applications independently from the Spark driver itself
C) Debuggability: the ability to perform interactive debugging directly from the application code
D) Stability: isolation of application code and dependencies from each other and the Spark driver

5. A data engineer is working with a large JSON dataset containing order information. The dataset is stored in a distributed file system and needs to be loaded into a Spark DataFrame for analysis. The data engineer wants to ensure that the schema is correctly defined and that the data is read efficiently.
Which approach should the data scientist use to efficiently load the JSON data into a Spark DataFrame with a predefined schema?

A) Use spark.read.json() with the inferSchema option set to true
B) Use spark.read.format("json").load() and then use DataFrame.withColumn() to cast each column to the desired data type.
C) Define a StructType schema and use spark.read.schema(predefinedSchema).json() to load the data.
D) Use spark.read.json() to load the data, then use DataFrame.printSchema() to view the inferred schema, and finally use DataFrame.cast() to modify column types.

Solutions: