Databricks Associate-Developer-Apache-Spark-3.5

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

1. A data engineer is streaming data from Kafka and requires:
Minimal latency
Exactly-once processing guarantees
Which trigger mode should be used?

A) .trigger(availableNow=True)
B) .trigger(continuous=True)
C) .trigger(continuous='1 second')
D) .trigger(processingTime='1 second')

2. A data engineer is working on a real-time analytics pipeline using Apache Spark Structured Streaming. The engineer wants to process incoming data and ensure that triggers control when the query is executed. The system needs to process data in micro-batches with a fixed interval of 5 seconds.
Which code snippet the data engineer could use to fulfil this requirement?
A)

B)

C)

D)

Options:

A) Uses trigger() - default micro-batch trigger without interval.
B) Uses trigger(continuous='5 seconds') - continuous processing mode.
C) Uses trigger(processingTime=5000) - invalid, as processingTime expects a string.
D) Uses trigger(processingTime='5 seconds') - correct micro-batch trigger with interval.

3. Given a CSV file with the content:

And the following code:
from pyspark.sql.types import *
schema = StructType([
StructField("name", StringType()),
StructField("age", IntegerType())
])
spark.read.schema(schema).csv(path).collect()
What is the resulting output?

A) The code throws an error due to a schema mismatch.
B) [Row(name='alladin', age=20)]
C) [Row(name='bambi', age=None), Row(name='alladin', age=20)]
D) [Row(name='bambi'), Row(name='alladin', age=20)]

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

5. A DataFrame df has columns name, age, and salary. The developer needs to sort the DataFrame by age in ascending order and salary in descending order.
Which code snippet meets the requirement of the developer?

A) df.sort("age", "salary", ascending=[True, True]).show()
B) df.orderBy("age", "salary", ascending=[True, False]).show()
C) df.sort("age", "salary", ascending=[False, True]).show()
D) df.orderBy(col("age").asc(), col("salary").asc()).show()

Solutions: