hardMultiple ChoiceObjective-mapped
SPLK-1001 Practice Question: A large e-commerce company ingests 10 TB/day of…
A large e-commerce company ingests 10 TB/day of web access logs into Splunk. They have enabled the CIM-compliant Web data model and created data model acceleration with a 90-day range. Users run reports using pivot to analyze HTTP status codes, client IPs, and URIs. Recently, two issues arose: (1) Pivot reports are returning incomplete or outdated results, sometimes missing data from the last few hours. (2) Acceleration summary size has ballooned to over 500 GB, causing search head performance degradation. The Splunk admin suspects that data model acceleration is not configured optimally. Upon inspection, the Web data model's root search contains a complex filter with multiple eval commands and lookups, and the acceleration time range is set to the same 90 days as the summary range. The admin also notices that the data model is defined as non-time-based, even though the events have timestamps and the pivot often uses time ranges. What is the best course of action to resolve both issues while maintaining accuracy and performance?
⚠ Common exam trap
Candidates often think keeping a non-time-based data model is acceptable for time-based pivots, but Splunk's acceleration engine requires a time-based model to correctly partition summaries for time-range queries, and they may also overlook that a 90-day acceleration range on high-volume data causes summary bloat.
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
✓
Change the data model to time-based, narrow acceleration range to 7 days, and simplify the root search by removing expensive eval/ lookups and using search-time field extractions instead.
Making the data model time-based allows acceleration to use time-bucketed summaries, which ensures recent data is included in pivot results and prevents incomplete results. Narrowing the acceleration range to 7 days reduces the summary size drastically (from 500+ GB to a manageable size), and simplifying the root search by removing expensive eval/lookups improves acceleration build performance and reduces overhead. This directly addresses both issues: incomplete recent data and excessive summary size.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Change the data model to time-based, narrow acceleration range to 7 days, and simplify the root search by removing expensive eval/ lookups and using search-time field extractions instead.
Why this is correct
Time-based allows efficient time bucketing and fresh summaries. A shorter acceleration range reduces size and rebuild time. Simplifying root search improves acceleration performance.
- ✗
Change the data model to time-based and set acceleration to 180 days to cover all data.
Why it's wrong here
Increasing the acceleration range to 180 days will further balloon the summary size and degrade performance. While time-based is good, the range is too wide.
- ✗
Keep the data model as non-time-based but reduce acceleration range to 30 days and add a constraint to filter out irrelevant events.
Why it's wrong here
Non-time-based models do not partition by time, so pivot queries that use time ranges may still scan large summaries. Reducing range might help size but not the freshness issue.
- ✗
Disable acceleration for the Web data model and instead create an accelerated search report for each common pivot query.
Why it's wrong here
Disabling acceleration removes the benefit of pre-computed summaries, forcing pivot to search raw data, which is extremely slow for 10 TB/day. Accelerated search reports would require manual maintenance and do not integrate with pivot.
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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.