Splunk Enterprise Certified Architect Practice Test

Disable ads (and more) with a membership for a one time $2.99 payment

Study for the Splunk Enterprise Certified Architect Test with our engaging quiz. Utilize flashcards and multiple choice questions complete with hints and explanations for each question. Prepare confidently for your certification exam!

Each practice test/flash card set has 50 randomly selected questions from a bank of over 500. You'll get a new set of questions each time!

Practice this question and more.


Which element is critical for maintaining consistent data layouts across all forwarders?

  1. Uniform configuration of inputs and props on each forwarder

  2. Utilizing shared storage among all forwarders

  3. Regular updates to the forwarders without testing

  4. Active monitoring of logs to catch discrepancies

The correct answer is: Uniform configuration of inputs and props on each forwarder

Maintaining consistent data layouts across all forwarders is vital for ensuring that data is processed and indexed uniformly. The key to achieving this consistency lies in the uniform configuration of inputs and props on each forwarder. Inputs dictate how data is ingested, specifying where the data is coming from and what format it is in. The props definitions are crucial as they determine how the data is parsed, transformed, and indexed within Splunk. When the configurations are uniform across all forwarders, it guarantees that each instance handles the data in the same manner, resulting in predictable and comparable data structuring. This consistency is important for effective search and reporting, as discrepancies in data handling could lead to challenges in data analysis and integrity. While utilizing shared storage, regular updates, and active monitoring can enhance overall system performance and reliability, they do not directly address the need for consistent data layout across all forwarders the same way that a uniform configuration does. Without uniformity in configuration, data discrepancies, variations in parsing, or inconsistency in input handling can occur, leading to issues in data management and analysis.