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

1. 22 of 55.
A Spark application needs to read multiple Parquet files from a directory where the files have differing but compatible schemas.
The data engineer wants to create a DataFrame that includes all columns from all files.
Which code should the data engineer use to read the Parquet files and include all columns using Apache Spark?

A) spark.read.format("parquet").option("inferSchema", "true").load("/data/parquet/")
B) spark.read.parquet("/data/parquet/").option("mergeAllCols", True)
C) spark.read.option("mergeSchema", True).parquet("/data/parquet/")
D) spark.read.parquet("/data/parquet/")

2. A data engineer wants to create an external table from a JSON file located at /data/input.json with the following requirements:
Create an external table named users
Automatically infer schema
Merge records with differing schemas
Which code snippet should the engineer use?
Options:

A) CREATE EXTERNAL TABLE users USING json OPTIONS (path '/data/input.json', mergeSchema 'true')
B) CREATE EXTERNAL TABLE users USING json OPTIONS (path '/data/input.json')
C) CREATE EXTERNAL TABLE users USING json OPTIONS (path '/data/input.json', schemaMerge 'true')
D) CREATE TABLE users USING json OPTIONS (path '/data/input.json')

3. 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 an Iterator[Series] → Iterator[Series] UDF
C) Convert the Pandas UDF from a Series → Series UDF to a Series → Scalar UDF
D) Convert the Pandas UDF to a PySpark UDF

4. 25 of 55.
A Data Analyst is working on employees_df and needs to add a new column where a 10% tax is calculated on the salary.
Additionally, the DataFrame contains the column age, which is not needed.
Which code fragment adds the tax column and removes the age column?

A) employees_df = employees_df.withColumn("tax", col("salary") + 0.1).drop("age")
B) employees_df = employees_df.withColumn("tax", lit(0.1)).drop("age")
C) employees_df = employees_df.dropField("age").withColumn("tax", col("salary") * 0.1)
D) employees_df = employees_df.withColumn("tax", col("salary") * 0.1).drop("age")

5. 23 of 55.
A data scientist is working with a massive dataset that exceeds the memory capacity of a single machine. The data scientist is considering using Apache Spark™ instead of traditional single-machine languages like standard Python scripts.
Which two advantages does Apache Spark™ offer over a normal single-machine language in this scenario? (Choose 2 answers)

A) It requires specialized hardware to run, making it unsuitable for commodity hardware clusters.
B) It has built-in fault tolerance, allowing it to recover seamlessly from node failures during computation.
C) It can distribute data processing tasks across a cluster of machines, enabling horizontal scalability.
D) It eliminates the need to write any code, automatically handling all data processing.
E) It processes data solely on disk storage, reducing the need for memory resources.

Solutions: