hardMultiple ChoiceObjective-mapped
SPLK-1001 Practice Question: A large e-commerce company uses Splunk to monitor…
A large e-commerce company uses Splunk to monitor its web application. They have a data model named 'Web_Transactions' that contains fields: status_code, response_time, uri, user_agent. The data model is accelerated with a 30-day time range. Recently, the operations team reported that the dashboard showing average response time by URI is loading slowly, taking over 30 seconds to display. Upon investigation, you find that the data model acceleration summary job is taking longer to complete and sometimes fails. The indexers have sufficient CPU and memory, but the disk I/O is high during the summary job. The volume of web logs is approximately 500 GB per day. Which action should the Splunk administrator take to improve dashboard performance?
⚠ Common exam trap
Watch out — candidates often assume adding more indexers (Option C) is the universal fix for performance issues, but the question explicitly states indexers have sufficient CPU and memory, and the bottleneck is the acceleration summary job's parallelism, not data distribution.
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
✓
Increase the maximum number of parallel searches for the data model acceleration job in the limits.conf.
Increasing the `max_concurrent_parallel_searches` for the data model acceleration job in `limits.conf` allows the summary process to use more parallel searches against the indexers, which can reduce the time it takes to build the acceleration summary. Since the indexers have sufficient CPU and memory but disk I/O is high, parallelizing the search workload can better utilize available resources and prevent the job from timing out, thereby improving dashboard performance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Disable data model acceleration and create a report that runs a scheduled search every 30 minutes to pre-compute the averages.
Why it's wrong here
Disabling acceleration removes the summary, likely making the dashboard slower.
- ✓
Increase the maximum number of parallel searches for the data model acceleration job in the limits.conf.
Why this is correct
Increasing parallelism can reduce the time to build summaries by allowing more concurrent disk reads.
- ✗
Add more indexers to distribute the data and reduce the load per indexer.
Why it's wrong here
Adding indexers without rebalancing existing data may not reduce I/O per indexer; also, the bottleneck is disk I/O on current indexers.
- ✗
Decrease the acceleration time range from 30 days to 7 days.
Why it's wrong here
This would reduce the amount of data summarized, but the dashboard queries the last 30 days, so it would still need to scan raw data; also the summary job would still be slow for the 7-day range if I/O is the issue.
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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.