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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q16-Q21):

NEW QUESTION # 16
Given:
python
CopyEdit
spark.sparkContext.setLogLevel("<LOG_LEVEL>")
Which set contains the suitable configuration settings for Spark driver LOG_LEVELs?

A. ALL, DEBUG, FAIL, INFO
B. WARN, NONE, ERROR, FATAL
C. FATAL, NONE, INFO, DEBUG
D. ERROR, WARN, TRACE, OFF

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
ThesetLogLevel()method ofSparkContextsets the logging level on the driver, which controls the verbosity of logs emitted during job execution. Supported levels are inherited from log4j and include the following:
ALL
DEBUG
ERROR
FATAL
INFO
OFF
TRACE
WARN
According to official Spark and Databricks documentation:
"Valid log levels include: ALL, DEBUG, ERROR, FATAL, INFO, OFF, TRACE, and WARN." Among the choices provided, only option B (ERROR, WARN, TRACE, OFF) includes four valid log levels and excludes invalid ones like "FAIL" or "NONE".
Reference: Apache Spark API docs # SparkContext.setLogLevel

NEW QUESTION # 17
A data engineer is building a Structured Streaming pipeline and wants the pipeline to recover from failures or intentional shutdowns by continuing where the pipeline left off.
How can this be achieved?

A. By configuring the optioncheckpointLocationduringreadStream
B. By configuring the optioncheckpointLocationduringwriteStream
C. By configuring the optionrecoveryLocationduringwriteStream
D. By configuring the optionrecoveryLocationduring the SparkSession initialization

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
To enable a Structured Streaming query to recover from failures or intentional shutdowns, it is essential to specify thecheckpointLocationoption during thewriteStreamoperation. This checkpoint location stores the progress information of the streaming query, allowing it to resume from where it left off.
According to the Databricks documentation:
"You must specify thecheckpointLocationoption before you run a streaming query, as in the following example:
option("checkpointLocation", "/path/to/checkpoint/dir")
toTable("catalog.schema.table")
- Databricks Documentation: Structured Streaming checkpoints
By setting thecheckpointLocationduringwriteStream, Spark can maintain state information and ensure exactly- once processing semantics, which are crucial for reliable streaming applications.

NEW QUESTION # 18
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 from a Series # Series UDF to a Series # Scalar UDF
C. Convert the Pandas UDF to a PySpark UDF
D. Run thein_spanish_inner()function in amapInPandas()function call

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The provided code defines a Pandas UDF of type Series-to-Series, where a new instance of the language modelis 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 theget_translation_model()is invoked once per executor process, not per batch, significantly improving pipeline performance.

NEW QUESTION # 19
A data engineer is building an Apache Spark™ Structured Streaming application to process a stream of JSON events in real time. The engineer wants the application to be fault-tolerant and resume processing from the last successfully processed record in case of a failure. To achieve this, the data engineer decides to implement checkpoints.
Which code snippet should the data engineer use?

A. query = streaming_df.writeStream
.format("console")
.option("checkpoint", "/path/to/checkpoint")
.outputMode("append")
.start()
B. query = streaming_df.writeStream
.format("console")
.outputMode("append")
.option("checkpointLocation", "/path/to/checkpoint")
.start()
C. query = streaming_df.writeStream
.format("console")
.outputMode("complete")
.start()
D. query = streaming_df.writeStream
.format("console")
.outputMode("append")
.start()

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
To enable fault tolerance and ensure that Spark can resume from the last committed offset after failure, you must configure a checkpoint location using the correct option key:"checkpointLocation".
From the official Spark Structured Streaming guide:
"To make a streaming query fault-tolerant and recoverable, a checkpoint directory must be specified using.
option("checkpointLocation", "/path/to/dir")."
Explanation of options:
Option A uses an invalid option name:"checkpoint"(should be"checkpointLocation") Option B is correct: it setscheckpointLocationproperly Option C lacks checkpointing and won't resume after failure Option D also lacks checkpointing configuration Reference: Apache Spark 3.5 Documentation # Structured Streaming # Fault Tolerance Semantics

NEW QUESTION # 20
A data engineer needs to persist a file-based data source to a specific location. However, by default, Spark writes to the warehouse directory (e.g., /user/hive/warehouse). To override this, the engineer must explicitly define the file path.
Which line of code ensures the data is saved to a specific location?
Options:

A. users.write.saveAsTable("default_table", path="/some/path")
B. users.write.saveAsTable("default_table").option("path", "/some/path")
C. users.write(path="/some/path").saveAsTable("default_table")
D. users.write.option("path", "/some/path").saveAsTable("default_table")

Answer: D

Explanation:
To persist a table and specify the save path, use:
users.write.option("path","/some/path").saveAsTable("default_table")
The .option("path", ...) must be applied before calling saveAsTable.
Option A uses invalid syntax (write(path=...)).
Option B applies.option()after.saveAsTable()-which is too late.
Option D uses incorrect syntax (no path parameter in saveAsTable).
Reference:Spark SQL - Save as Table

NEW QUESTION # 21
......

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