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

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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:

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

Question 2

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

Question 3

A data engineer observes that an upstream streaming source sends duplicate records, where duplicates share the same key and have at most a 30-minute difference in event_timestamp. The engineer adds:
dropDuplicatesWithinWatermark("event_timestamp", "30 minutes")
What is the result?

A. It removes all duplicates regardless of when they arrive
B. It is not able to handle deduplication in this scenario
C. It removes duplicates that arrive within the 30-minute window specified by the watermark
D. It accepts watermarks in seconds and the code results in an error

Question 4

An MLOps engineer is building a Pandas UDF that applies a language model that translates English strings into Spanish. The initial code is loading the model on every call to the UDF, which is hurting the performance of the data pipeline.
The initial code is:

def in_spanish_inner(df: pd.Series) -> pd.Series:
model = get_translation_model(target_lang='es')
return df.apply(model)
in_spanish = sf.pandas_udf(in_spanish_inner, StringType())
How can the MLOps engineer change this code to reduce how many times the language model is loaded?

A. Convert the Pandas UDF from a Series → Series UDF to a Series → Scalar UDF
B. Convert the Pandas UDF to a PySpark UDF
C. Run the in_spanish_inner() function in a mapInPandas() function call
D. Convert the Pandas UDF from a Series → Series UDF to an Iterator[Series] → Iterator[Series] UDF

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

Solutions: