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

2. What is the behavior for function date_sub(start, days) if a negative value is passed into the days parameter?

A) The number of days specified will be added to the start date
B) The number of days specified will be removed from the start date
C) An error message of an invalid parameter will be returned
D) The same start date will be returned

3. What is the relationship between jobs, stages, and tasks during execution in Apache Spark?
Options:

A) A stage contains multiple jobs, and each job contains multiple tasks.
B) A job contains multiple stages, and each stage contains multiple tasks.
C) A job contains multiple tasks, and each task contains multiple stages.
D) A stage contains multiple tasks, and each task contains multiple jobs.

4. An engineer has two DataFrames: df1 (small) and df2 (large). A broadcast join is used:
python
CopyEdit
from pyspark.sql.functions import broadcast
result = df2.join(broadcast(df1), on='id', how='inner')
What is the purpose of using broadcast() in this scenario?
Options:

A) It reduces the number of shuffle operations by replicating the smaller DataFrame to all nodes.
B) It increases the partition size for df1 and df2.
C) It ensures that the join happens only when the id values are identical.
D) It filters the id values before performing the join.

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

Solutions: