In Spark, when a CSV row does not match the provided schema, Spark does not raise an error by default. Instead, it returns null for fields that cannot be parsed correctly.
In the first row, "hello" cannot be cast to Integer for the age field → Spark sets age=None In the second row, "20" is a valid integer → age=20 So the output will be:
[Row(name='bambi', age=None), Row(name='alladin', age=20)]
Final answer: C

The provided code defines a Pandas UDF of type Series-to-Series, where a new instance of the language model is created on each call, which happens per batch. This is inefficient and results in significant overhead due to repeated model initialization.
To reduce the frequency of model loading, the engineer should convert the UDF to an iterator-based Pandas UDF (Iterator[pd.Series] -> Iterator[pd.Series]). This allows the model to be loaded once per executor and reused across multiple batches, rather than once per call.
From the official Databricks documentation:
"Iterator of Series to Iterator of Series UDFs are useful when the UDF initialization is expensive... For example, loading a ML model once per executor rather than once per row/batch."
- Databricks Official Docs: Pandas UDFs
Correct implementation looks like:
python
CopyEdit
@pandas_udf("string")
def translate_udf(batch_iter: Iterator[pd.Series]) -> Iterator[pd.Series]:
model = get_translation_model(target_lang='es')
for batch in batch_iter:
yield batch.apply(model)
This refactor ensures the get_translation_model() is invoked once per executor process, not per batch, significantly improving pipeline performance.

Option B uses Spark's built-in SQL function length(), which is efficient and avoids the overhead of a Python UDF:
from pyspark.sql.functions import length, col
df.select(length(col("stringColumn")).alias("length"))
Explanation of other options:
Option A is incorrect syntax; spark.udf is not called this way.
Option C registers a UDF but doesn't apply it in the DataFrame transformation.
Option D is syntactically valid but uses a Python UDF which is less efficient than built-in functions.
Final answer: B

The message "GC overhead limit exceeded" typically indicates that the JVM is spending too much time in garbage collection with little memory recovery. This suggests that the driver or executor is under-provisioned in memory.
The most effective remedy is to increase the driver memory using:
--driver-memory 4g
This is confirmed in Spark's official troubleshooting documentation:
"If you see a lot of GC overhead limit exceeded errors in the driver logs, it's a sign that the driver is running out of memory."
- Spark Tuning Guide
"If you see a lot of GC overhead limit exceeded errors in the driver logs, it's a sign that the driver is running out of memory."
- Spark Tuning Guide
Why others are incorrect:
A may help but does not directly address the driver memory shortage.
B is not a valid action; GC cannot be disabled.
D increases memory usage, worsening the problem.

To exclude sensitive (PII) columns from a DataFrame, the easiest method is to use the .drop() function with the list of column names to remove.
Correct syntax:
df_user_non_pii = df_user.drop("name", "email", "birthdate")
This creates a new DataFrame containing all remaining columns.
Why the other options are incorrect:
B: .dropFields() is not valid for standard DataFrames - it's used for struct fields only.
C: .select() would keep only PII columns, not remove them.
D: .remove() does not exist in Spark DataFrame API.
Reference:
PySpark DataFrame API - drop() method for removing multiple columns.
Databricks Exam Guide (June 2025): Section "Developing Apache Spark DataFrame/DataSet API Applications" - data manipulation, selecting, and dropping columns.