hardMultiple ChoiceObjective-mapped
SPLK-1001 Practice Question: A financial services company uses Splunk to…
A financial services company uses Splunk to monitor authentication logs from 500 remote servers. They created a data model named 'Authentication' with 15 fields including 'user', 'src_ip', 'dest_ip', 'action', and 'status'. They enabled acceleration with a summary range of 1 day and set the maximum search time range to 30 days. After one month of operation, searches against the data model that used to complete in seconds now time out after 60 seconds. The average daily log volume is 10 GB. The admin runs | datamodel Audit and discovers that the summary size is approximately 5 GB per day, which is similar to the raw data index size. The search head has 16 GB RAM and 4 CPU cores, and no other resource issues are observed. What is the most likely cause of the performance degradation?
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
✓
Review the data model fields and remove high-cardinality fields from the acceleration or the data model itself.
When the summary size is nearly equal to the raw data volume, it indicates that acceleration is not effectively reducing the data. This typically occurs when the data model includes high-cardinality fields (e.g., 'user', 'src_ip', 'dest_ip') that create too many unique combinations, preventing meaningful summarization. As a result, the acceleration summary remains large, causing searches to time out due to I/O overhead. Option A is incorrect because increasing the summary range would further increase the summary size and worsen performance. Option B is incorrect because search-time vs. indexed extractions do not address the root cause of high cardinality. Option D is incorrect because the number of fields alone is not the issue; it is the high cardinality of specific fields that degrades acceleration efficiency.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Optimize the underlying searches by using indexed field extractions instead of search-time field extractions.
Why it's wrong here
This may help search performance but does not address the acceleration summary size issue.
- ✗
Increase the summary range from 1 day to 7 days to reduce the number of summaries.
Why it's wrong here
A longer summary range would create larger summaries, worsening performance.
- ✓
Review the data model fields and remove high-cardinality fields from the acceleration or the data model itself.
Why this is correct
High-cardinality fields prevent effective summarization, causing summary size to approach raw data size.
- ✗
Reduce the number of fields in the data model to fewer than 10 to improve acceleration efficiency.
Why it's wrong here
The number of fields is not the issue; it is the cardinality of those fields.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This SPLK-1001 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-1001 exam.