- A
Disable data model acceleration and create a report that runs a scheduled search every 30 minutes to pre-compute the averages.
Why wrong: Disabling acceleration removes the summary, likely making the dashboard slower.
- B
Increase the maximum number of parallel searches for the data model acceleration job in the limits.conf.
Increasing parallelism can reduce the time to build summaries by allowing more concurrent disk reads.
- C
Add more indexers to distribute the data and reduce the load per indexer.
Why wrong: Adding indexers without rebalancing existing data may not reduce I/O per indexer; also, the bottleneck is disk I/O on current indexers.
- D
Decrease the acceleration time range from 30 days to 7 days.
Why wrong: 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.
Quick Answer
The answer is to increase the maximum number of parallel searches for the data model acceleration job in the limits.conf file. This is correct because data model acceleration relies on a summary job that runs searches against the indexers to precompute results; when disk I/O is high but CPU and memory are sufficient, the bottleneck is the sequential nature of the searches. By raising the `max_concurrent_parallel_searches` setting, you enable the acceleration process to run multiple searches simultaneously, better utilizing available resources and reducing the time needed to build the summary—directly addressing the slow dashboard loading. On the SPLK-1002 exam, this scenario tests your understanding of how acceleration tuning affects search performance, with a common trap being to mistakenly adjust indexer resources or time ranges instead. Remember the mnemonic: “Parallel paths prevent pileups”—when I/O is high but hardware is ample, parallelize the searches to accelerate the acceleration.
SPLK-1002 Data Models and Best Practices Practice Question
This SPLK-1002 practice question tests your understanding of data models and best practices. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
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.
Option B is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that 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.
Detailed technical explanation
How to think about this question
Data model acceleration uses a summary job that runs periodically to pre-compute aggregations, and the `max_concurrent_parallel_searches` setting in `limits.conf` controls how many parallel search processes the summary job can spawn. When disk I/O is high, increasing parallelism can help by spreading the read workload across more threads, but if I/O is the bottleneck, too much parallelism can actually worsen contention; the optimal setting often requires tuning based on the specific storage subsystem (e.g., SSD vs. HDD) and the number of CPU cores available.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A practitioner preparing for the SPLK-1002 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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Data Models and Best Practices — study guide chapter
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FAQ
Questions learners often ask
What does this SPLK-1002 question test?
Data Models and Best Practices — This question tests Data Models and Best Practices — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Increase the maximum number of parallel searches for the data model acceleration job in the limits.conf. — Option B is correct because 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.
What should I do if I get this SPLK-1002 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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Last reviewed: Jun 11, 2026
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.
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