The answer is that the base search for the data model must be efficient and not timeout. This is correct because data model acceleration works by pre-computing results into a summary index; if the base search is too slow or times out, the acceleration process cannot build or update that summary, effectively breaking the accelerated data model. On the Splunk SPLK-1002 exam, this concept tests your understanding of how acceleration relies on underlying search performance and storage integrity—common traps include assuming acceleration fixes slow searches or ignoring disk space issues. To remember, think of acceleration as a “pre-built shortcut”: if the original path (the base search) is too long or blocked, the shortcut can never be constructed.
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.
Which THREE are valid considerations when troubleshooting data model acceleration? (Choose three.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The summary index must be writable and have enough disk space.
Option A is correct because data model acceleration creates a summary index that stores pre-computed results. If the summary index is not writable or lacks sufficient disk space, the acceleration process will fail, preventing the data model from being accelerated.
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.
✓
The summary index must be writable and have enough disk space.
Why this is correct
Acceleration writes summaries to a summary index; it must be writable.
Related concept
Read the scenario before looking for a memorised answer.
✗
Too many fields in the data model can cause acceleration to fail.
Why it's wrong here
Many fields may reduce performance but do not cause failure.
✗
The data model must be based on a real-time search to be accelerated.
Why it's wrong here
Acceleration works on historical time ranges, not only real-time.
✓
Insufficient memory on the indexer for the summary build process.
Why this is correct
Memory shortage can halt acceleration builds.
Related concept
Read the scenario before looking for a memorised answer.
✓
The base search for the data model must be efficient and not timeout.
Why this is correct
A slow or timing out base search prevents acceleration from completing.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Splunk often tests the misconception that data model acceleration requires real-time data, but in reality, acceleration is designed for historical data and uses scheduled summary builds, not real-time searches.
Detailed technical explanation
How to think about this question
Data model acceleration uses a summary index that is rebuilt periodically based on the data model's base search. The summary index is stored on the indexer, and if it becomes full or unwritable, the acceleration job fails. Additionally, the base search must complete within the configured timeout (default 300 seconds) to avoid failure, as the summary build process is a single-threaded operation that cannot be interrupted.
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.
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: The summary index must be writable and have enough disk space. — Option A is correct because data model acceleration creates a summary index that stores pre-computed results. If the summary index is not writable or lacks sufficient disk space, the acceleration process will fail, preventing the data model from being accelerated.
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.
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Question Discussion
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