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

1. 47 of 55.
A data engineer has written the following code to join two DataFrames df1 and df2:
df1 = spark.read.csv("sales_data.csv")
df2 = spark.read.csv("product_data.csv")
df_joined = df1.join(df2, df1.product_id == df2.product_id)
The DataFrame df1 contains ~10 GB of sales data, and df2 contains ~8 MB of product data.
Which join strategy will Spark use?

A) Shuffle join because no broadcast hints were provided.
B) Shuffle join, as the size difference between df1 and df2 is too large for a broadcast join to work efficiently.
C) Broadcast join, as df2 is smaller than the default broadcast threshold.
D) Shuffle join, because AQE is not enabled, and Spark uses a static query plan.

2. 3 of 55. A data engineer observes that the upstream streaming source feeds the event table frequently and sends duplicate records. Upon analyzing the current production table, the data engineer found that the time difference in the event_timestamp column of the duplicate records is, at most, 30 minutes.
To remove the duplicates, the engineer adds the code:
df = df.withWatermark("event_timestamp", "30 minutes")
What is the result?

A) It is not able to handle deduplication in this scenario.
B) It accepts watermarks in seconds and the code results in an error.
C) It removes all duplicates regardless of when they arrive.
D) It removes duplicates that arrive within the 30-minute window specified by the watermark.

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

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) 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 the in_spanish_inner() function in a mapInPandas() function call

5. A data engineer is working on the DataFrame:

(Referring to the table image: it has columns Id, Name, count, and timestamp.) Which code fragment should the engineer use to extract the unique values in the Name column into an alphabetically ordered list?

A) df.select("Name").distinct()
B) df.select("Name").distinct().orderBy(df["Name"].desc())
C) df.select("Name").orderBy(df["Name"].asc())
D) df.select("Name").distinct().orderBy(df["Name"])

Solutions: