SPLK-1002 Macros, Saved Searches and CIM Practice Question
A team regularly runs a saved search that joins two large indexes. Performance is poor. Which design change would MOST improve query performance?
⚠ Common exam trap
Splunk often tests the misconception that subsearches are always faster than joins, but in reality, subsearches can be equally or more resource-intensive when dealing with large datasets, and the correct optimization is to pre-aggregate data using data model summaries.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Create a data model summary to pre-aggregate the data.
A data model summary pre-aggregates data at search time, reducing the volume of data that the join operation must process. This is the most effective way to improve performance when joining two large indexes, as it avoids scanning and joining raw events repeatedly.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Convert the saved search to a scheduled report.
Why it's wrong here
Scheduling does not improve query performance.
- ✓
Create a data model summary to pre-aggregate the data.
Why this is correct
Summaries reduce the amount of data scanned.
- ✗
Replace the join with a subsearch.
Why it's wrong here
Subsearches can be slower than joins.
- ✗
Use the `fields` command to remove unnecessary fields before the join.
Why it's wrong here
Removing fields may not resolve the join overhead.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This SPLK-1002 practice question is part of Courseiva's free Splunk certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the SPLK-1002 exam.