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Databricks Certified Associate Developer for Apache Spark 3.5 Exam - Topic 7 Question 19 Discussion

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

Databricks Certified Associate Developer for Apache Spark 3.5 Exam - Topic 7 Question 19 Discussion

Actual exam question for Databricks's Databricks Certified Associate Developer for Apache Spark 3.5 exam
Question #: 19
Topic #: 7
[All Databricks Certified Associate Developer for Apache Spark 3.5 Questions]

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?

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Suggested Answer: A

The correct way to aggregate information (e.g., max value) from distributed workers back to the driver is using RDD actions such as reduce() or aggregate().

From the documentation:

''To perform global aggregations on distributed data, actions like reduce() are commonly used to collect summaries such as min/max/avg.''

Accumulators (Option B) do not support max operations directly and are not intended for such analytics.

Broadcast (Option C) is used to send data to workers, not collect from them.

Spark UI (Option D) is a monitoring tool --- not an analytics collection interface.

Final Answer: A


Contribute your Thoughts:

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I think using an accumulator could be the right choice here, but I'm not entirely sure how it works in this context.
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