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

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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. 49 of 55.
In the code block below, aggDF contains aggregations on a streaming DataFrame:
aggDF.writeStream \
.format("console") \
.outputMode("???") \
.start()
Which output mode at line 3 ensures that the entire result table is written to the console during each trigger execution?

A) APPEND
B) COMPLETE
C) REPLACE
D) AGGREGATE

2. A Spark developer is building an app to monitor task performance. They need to track the maximum task processing time per worker node and consolidate it on the driver for analysis.
Which technique should be used?

A) Configure the Spark UI to automatically collect maximum times
B) Use an RDD action like reduce() to compute the maximum time
C) Use an accumulator to record the maximum time on the driver
D) Broadcast a variable to share the maximum time among workers

3. 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") \
.start()
C) query = streaming_df.writeStream \
.format("console") \
.outputMode("append") \
.option("checkpointLocation", "/path/to/checkpoint") \
.start()
D) query = streaming_df.writeStream \
.format("console") \
.outputMode("complete") \
.start()

4. Which command overwrites an existing JSON file when writing a DataFrame?

A) df.write.json("path/to/file", overwrite=True)
B) df.write.mode("overwrite").json("path/to/file")
C) df.write.format("json").save("path/to/file", mode="overwrite")
D) df.write.overwrite.json("path/to/file")

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

Solutions: