The Splunk Core Certified Power User (SPLK-1002) exam validates your ability to perform advanced search and analysis tasks within Splunk. This certification is designed for users who have moved beyond basic searches and need to master data transformation, field management, and data modeling. This page provides a clear roadmap of the exam syllabus, question formats, and actionable preparation strategies to help you pass with confidence.
Use this topic map to guide your study for Splunk SPLK-1002 (Splunk Core Certified Power User) within the Splunk Core Certified Power User path.
The SPLK-1002 exam combines multiple-choice and scenario-based questions to assess both conceptual knowledge and practical problem-solving ability. Questions progress in difficulty and reflect real-world situations you will encounter when managing Splunk environments.
Questions are designed to measure both breadth of knowledge and depth of understanding, ensuring you can apply concepts to solve actual problems in production environments.
A structured study approach mapped to the ten exam topics will maximize your retention and confidence. Plan to spend 4-6 weeks reviewing each topic thoroughly, practicing with realistic scenarios, and refining your weak areas through targeted review.
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Field management (topic 4), data models (topic 9), and the Common Information Model (topic 10) typically account for a significant portion of exam questions because they form the foundation for advanced Splunk usage. However, all ten topics are represented, so balanced preparation across all areas is essential for success.
Transforming commands reshape raw search results; fields organize and label that data; and data models package both together into a business-friendly layer. For example, you extract a field (topic 4), create an alias to standardize its name (topic 5), then include it in a data model (topic 9) so other users can build reports without writing complex searches. Understanding these connections helps you design efficient, scalable solutions.
Hands-on experience is invaluable because it builds muscle memory and intuition. Prioritize labs that let you create fields, build simple data models, write macros, and test transforming commands in a sandbox environment. If possible, work with real or realistic data to understand how field extraction and aliasing solve actual data quality challenges.
Common pitfalls include confusing field extraction at index time versus search time, misunderstanding when to use macros versus calculated fields, and overlooking the role of the CIM add-on in standardizing field names. Many candidates also underestimate the importance of data model design and how it impacts search performance and user adoption. Careful review of explanations in practice tests helps avoid these errors on exam day.
In the final week, focus on high-weight topics (field management, data models, and CIM) and avoid introducing new material. Complete one full-length timed practice test under realistic exam conditions, review any questions you missed, and spend time on topics where you scored below 80 percent. Get adequate sleep the night before the exam, and on test day, read each question carefully and manage your time to allow for review of flagged items.
In which of the following scenarios is an event type more effective than a saved search?
If a calculated field has the same name as an extracted field, what happens to the extracted field?
When should the regular expression mode of Field Extractor (FX) be used? (select all that apply)
The regular expression mode of Field Extractor (FX) should be used for data with multiple, different characters separating fields or for unstructured dat
A field alias has been created based on an original field. A search without any transforming commands is then executed in Smart Mode. Which field name appears in the results?
What does the fillnull command replace null values with, if the value argument is not specified?
The fillnull command replaces null values with 0 by default, if the value argument is not specified. You can use the value argument to specify a different value to replace null values with, such as N/A or NULL.