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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 uses a broadcast variable to share a DataFrame containing millions of rows across executors for lookup purposes. What will be the outcome?

A) The job may fail because the driver does not have enough CPU cores to serialize the large DataFrame
B) The job may fail if the memory on each executor is not large enough to accommodate the DataFrame being broadcasted
C) The job may fail if the executors do not have enough CPU cores to process the broadcasted dataset
D) The job will hang indefinitely as Spark will struggle to distribute and serialize such a large broadcast variable to all executors

2. Given this view definition:
df.createOrReplaceTempView("users_vw")
Which approach can be used to query the users_vw view after the session is terminated?
Options:

A) Persist the users_vw data as a table
B) Query the users_vw using Spark
C) Save the users_vw definition and query using Spark
D) Recreate the users_vw and query the data using Spark

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

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

5. 6 of 55.
Which components of Apache Spark's Architecture are responsible for carrying out tasks when assigned to them?

A) Executors
B) CPU Cores
C) Driver Nodes
D) Worker Nodes

Solutions: