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

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

2. 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 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()
B) Use a regular Spark UDF:
from pyspark.sql.functions import mean
df.groupBy("user_id").agg(mean("value")).show()
C) Use the applyInPandas API:
df.groupby("user_id").applyInPandas(mean_func, schema="user_id long, value double").show()
D) Use the mapInPandas API:
df.mapInPandas(mean_func, schema="user_id long, value double").show()

3. 38 of 55.
A data engineer is working with Spark SQL and has a large JSON file stored at /data/input.json.
The file contains records with varying schemas, and the engineer wants to create an external table in Spark SQL that:
Reads directly from /data/input.json.
Infers the schema automatically.
Merges differing schemas.
Which code snippet should the engineer use?

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

4. 33 of 55.
The data engineering team created a pipeline that extracts data from a transaction system.
The transaction system stores timestamps in UTC, and the data engineers must now transform the transaction_datetime field to the "America/New_York" timezone for reporting.
Which code should be used to convert the timestamp to the target timezone?

A) raw.withColumn("transaction_datetime", to_utc_timestamp(col("transaction_datetime"), "America/New_York"))
B) raw.withColumn("transaction_datetime", date_format(col("transaction_datetime"), "America/New_York"))
C) raw.withColumn("transaction_datetime", convert_timezone(col("transaction_datetime"), "America/New_York"))
D) raw.withColumn("transaction_datetime", from_utc_timestamp(col("transaction_datetime"), "America/New_York"))

5. A data engineer is building an Apache Spark™ Structured Streaming application to process a stream of JSON events in real time. The engineer wants the application to be fault-tolerant and resume processing from the last successfully processed record in case of a failure. To achieve this, the data engineer decides to implement checkpoints.
Which code snippet should the data engineer use?

A) query = streaming_df.writeStream \
.format("console") \
.option("checkpoint", "/path/to/checkpoint") \
.outputMode("append") \
.start()
B) query = streaming_df.writeStream \
.format("console") \
.outputMode("append") \
.option("checkpointLocation", "/path/to/checkpoint") \
.start()
C) query = streaming_df.writeStream \
.format("console") \
.outputMode("append") \
.start()
D) query = streaming_df.writeStream \
.format("console") \
.outputMode("complete") \
.start()

Solutions: