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

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

2. 44 of 55.
A data engineer is working on a real-time analytics pipeline using Spark Structured Streaming.
They want the system to process incoming data in micro-batches at a fixed interval of 5 seconds.
Which code snippet fulfills this requirement?

A) query = df.writeStream \
.outputMode("append") \
.trigger(once=True) \
.start()
B) query = df.writeStream \
.outputMode("append") \
.trigger(processingTime="5 seconds") \
.start()
C) query = df.writeStream \
.outputMode("append") \
.start()
D) query = df.writeStream \
.outputMode("append") \
.trigger(continuous="5 seconds") \
.start()

3. A data engineer is reviewing a Spark application that applies several transformations to a DataFrame but notices that the job does not start executing immediately.
Which two characteristics of Apache Spark's execution model explain this behavior?
Choose 2 answers:

A) The Spark engine requires manual intervention to start executing transformations.
B) Only actions trigger the execution of the transformation pipeline.
C) The Spark engine optimizes the execution plan during the transformations, causing delays.
D) Transformations are executed immediately to build the lineage graph.
E) Transformations are evaluated lazily.

4. The following code fragment results in an error:
@F.udf(T.IntegerType())
def simple_udf(t: str) -> str:
return answer * 3.14159
Which code fragment should be used instead?

A) @F.udf(T.IntegerType())
def simple_udf(t: float) -> float:
return t * 3.14159
B) @F.udf(T.DoubleType())
def simple_udf(t: float) -> float:
return t * 3.14159
C) @F.udf(T.IntegerType())
def simple_udf(t: int) -> int:
return t * 3.14159
D) @F.udf(T.DoubleType())
def simple_udf(t: int) -> int:
return t * 3.14159

5. 15 of 55.
A data engineer is working on a Streaming DataFrame (streaming_df) with the following streaming data:
id
name
count
timestamp
1
Delhi
20
2024-09-19T10:11
1
Delhi
50
2024-09-19T10:12
2
London
50
2024-09-19T10:15
3
Paris
30
2024-09-19T10:18
3
Paris
20
2024-09-19T10:20
4
Washington
10
2024-09-19T10:22
Which operation is supported with streaming_df?

A) streaming_df.select(countDistinct("name"))
B) streaming_df.count()
C) streaming_df.filter("count < 30")
D) streaming_df.show()

Solutions: