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

1. A Spark application is experiencing performance issues in client mode because the driver is resource-constrained.
How should this issue be resolved?

A) Switch the deployment mode to cluster mode
B) Increase the driver memory on the client machine
C) Switch the deployment mode to local mode
D) Add more executor instances to the cluster

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

3. What is the difference between df.cache() and df.persist() in Spark DataFrame?

A) Both functions perform the same operation. The persist() function provides improved performance as its default storage level is DISK_ONLY.
B) Both cache() and persist() can be used to set the default storage level (MEMORY_AND_DISK_SER)
C) persist() - Persists the DataFrame with the default storage level (MEMORY_AND_DISK_SER) and cache() - Can be used to set different storage levels to persist the contents of the DataFrame.
D) cache() - Persists the DataFrame with the default storage level (MEMORY_AND_DISK) and persist() - Can be used to set different storage levels to persist the contents of the DataFrame

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

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

Solutions: