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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 engineer needs to write a DataFrame df to a Parquet file, partitioned by the column country, and overwrite any existing data at the destination path.
Which code should the data engineer use to accomplish this task in Apache Spark?

A) df.write.mode("overwrite").parquet("/data/output")
B) df.write.partitionBy("country").parquet("/data/output")
C) df.write.mode("append").partitionBy("country").parquet("/data/output")
D) df.write.mode("overwrite").partitionBy("country").parquet("/data/output")

2. 8 of 55.
A data scientist at a large e-commerce company needs to process and analyze 2 TB of daily customer transaction data. The company wants to implement real-time fraud detection and personalized product recommendations.
Currently, the company uses a traditional relational database system, which struggles with the increasing data volume and velocity.
Which feature of Apache Spark effectively addresses this challenge?

A) Support for SQL queries on structured data
B) Ability to process small datasets efficiently
C) Built-in machine learning libraries
D) In-memory computation and parallel processing capabilities

3. A developer initializes a SparkSession:

spark = SparkSession.builder \
.appName("Analytics Application") \
.getOrCreate()
Which statement describes the spark SparkSession?

A) A SparkSession is unique for each appName, and calling getOrCreate() with the same name will return an existing SparkSession once it has been created.
B) If a SparkSession already exists, this code will return the existing session instead of creating a new one.
C) The getOrCreate() method explicitly destroys any existing SparkSession and creates a new one.
D) A new SparkSession is created every time the getOrCreate() method is invoked.

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

5. How can a Spark developer ensure optimal resource utilization when running Spark jobs in Local Mode for testing?
Options:

A) Use the spark.dynamicAllocation.enabled property to scale resources dynamically.
B) Increase the number of local threads based on the number of CPU cores.
C) Configure the application to run in cluster mode instead of local mode.
D) Set the spark.executor.memory property to a large value.

Solutions: