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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. A data scientist is analyzing a large dataset and has written a PySpark script that includes several transformations and actions on a DataFrame. The script ends with a collect() action to retrieve the results.
How does Apache Spark™'s execution hierarchy process the operations when the data scientist runs this script?

A) Spark creates a single task for each transformation and action in the script, and these tasks are grouped into stages and jobs based on their dependencies.
B) The collect() action triggers a job, which is divided into stages at shuffle boundaries, and each stage is split into tasks that operate on individual data partitions.
C) The entire script is treated as a single job, which is then divided into multiple stages, and each stage is further divided into tasks based on data partitions.
D) The script is first divided into multiple applications, then each application is split into jobs, stages, and finally tasks.

2. 35 of 55.
A data engineer is building a Structured Streaming pipeline and wants it to recover from failures or intentional shutdowns by continuing where it left off.
How can this be achieved?

A) By configuring the option checkpointLocation during writeStream.
B) By configuring the option recoveryLocation during writeStream.
C) By configuring the option recoveryLocation during SparkSession initialization.
D) By configuring the option checkpointLocation during readStream.

3. An engineer notices a significant increase in the job execution time during the execution of a Spark job. After some investigation, the engineer decides to check the logs produced by the Executors.
How should the engineer retrieve the Executor logs to diagnose performance issues in the Spark application?

A) Use the command spark-submit with the -verbose flag to print the logs to the console.
B) Fetch the logs by running a Spark job with the spark-sql CLI tool.
C) Locate the executor logs on the Spark master node, typically under the /tmp directory.
D) Use the Spark UI to select the stage and view the executor logs directly from the stages tab.

4. 14 of 55.
A developer created a DataFrame with columns color, fruit, and taste, and wrote the data to a Parquet directory using:
df.write.partitionBy("color", "taste").parquet("/path/to/output")
What is the result of this code?

A) It appends new partitions to an existing Parquet file.
B) It creates separate directories for each unique combination of color and taste.
C) It throws an error if there are null values in either partition column.
D) It stores all data in a single Parquet file.

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 to a PySpark UDF
B) Convert the Pandas UDF from a Series → Series UDF to a Series → Scalar 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

Solutions: