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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. 26 of 55.
A data scientist at an e-commerce company is working with user data obtained from its subscriber database and has stored the data in a DataFrame df_user.
Before further processing, the data scientist wants to create another DataFrame df_user_non_pii and store only the non-PII columns.
The PII columns in df_user are name, email, and birthdate.
Which code snippet can be used to meet this requirement?

A) df_user_non_pii = df_user.drop("name", "email", "birthdate")
B) df_user_non_pii = df_user.select("name", "email", "birthdate")
C) df_user_non_pii = df_user.remove("name", "email", "birthdate")
D) df_user_non_pii = df_user.dropFields("name", "email", "birthdate")

2. 17 of 55.
A data engineer has noticed that upgrading the Spark version in their applications from Spark 3.0 to Spark 3.5 has improved the runtime of some scheduled Spark applications.
Looking further, the data engineer realizes that Adaptive Query Execution (AQE) is now enabled.
Which operation should AQE be implementing to automatically improve the Spark application performance?

A) Dynamically switching join strategies
B) Improving the performance of single-stage Spark jobs
C) Collecting persistent table statistics and storing them in the metastore for future use
D) Optimizing the layout of Delta files on disk

3. A data analyst wants to add a column date derived from a timestamp column.
Options:

A) dates_df.withColumn("date", f.to_date("timestamp")).show()
B) dates_df.withColumn("date", f.from_unixtime("timestamp")).show()
C) dates_df.withColumn("date", f.date_format("timestamp", "yyyy-MM-dd")).show()
D) dates_df.withColumn("date", f.unix_timestamp("timestamp")).show()

4. A developer is working with a pandas DataFrame containing user behavior data from a web application.
Which approach should be used for executing a groupBy operation in parallel across all workers in Apache Spark 3.5?
A)
Use the applylnPandas API
B)

C)

A) Use a regular Spark UDF:
from pyspark.sql.functions import mean
df.groupBy("user_id").agg(mean("value")).show()
B) Use the applyInPandas API:
df.groupby("user_id").applyInPandas(mean_func, schema="user_id long, value double").show()
C) Use a Pandas UDF:
@pandas_udf("double")
def mean_func(value: pd.Series) -> float:
return value.mean()
df.groupby("user_id").agg(mean_func(df["value"])).show()
D) Use the mapInPandas API:
df.mapInPandas(mean_func, schema="user_id long, value double").show()

5. A Spark application developer wants to identify which operations cause shuffling, leading to a new stage in the Spark execution plan.
Which operation results in a shuffle and a new stage?

A) DataFrame.select()
B) DataFrame.withColumn()
C) DataFrame.groupBy().agg()
D) DataFrame.filter()

Solutions: