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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. What is the risk associated with this operation when converting a large Pandas API on Spark DataFrame back to a Pandas DataFrame?

A) The operation will load all data into the driver's memory, potentially causing memory overflow
B) The conversion will automatically distribute the data across worker nodes
C) The operation will fail if the Pandas DataFrame exceeds 1000 rows
D) Data will be lost during conversion

2. 49 of 55.
In the code block below, aggDF contains aggregations on a streaming DataFrame:
aggDF.writeStream \
.format("console") \
.outputMode("???") \
.start()
Which output mode at line 3 ensures that the entire result table is written to the console during each trigger execution?

A) COMPLETE
B) APPEND
C) AGGREGATE
D) REPLACE

3. A data analyst builds a Spark application to analyze finance data and performs the following operations: filter, select, groupBy, and coalesce.
Which operation results in a shuffle?

A) groupBy
B) filter
C) coalesce
D) select

4. What is the benefit of using Pandas on Spark for data transformations?
Options:

A) It runs on a single node only, utilizing the memory with memory-bound DataFrames and hence cost-efficient.
B) It executes queries faster using all the available cores in the cluster as well as provides Pandas's rich set of features.
C) It is available only with Python, thereby reducing the learning curve.
D) It computes results immediately using eager execution, making it simple to use.

5. 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 an Iterator[Series] → Iterator[Series] 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 a Series → Scalar UDF

Solutions: