- A
`| streamstats count by user src_ip | where count > 3`
Why wrong: `streamstats` counts running total within the stream, not grouping events into transactions.
- B
`| timechart span=5m limit=0 values(src_ip) by user | eval count=mvcount(values(src_ip)) | where count > 3`
Why wrong: This creates time buckets, but the same user might appear in multiple buckets; also it uses timechart which is less efficient.
- C
`| stats count by user, src_ip | where count > 3`
Why wrong: This counts events per user-IP combo, not distinct IPs per user over time.
- D
`| transaction user maxspan=5m | eval distinct_ip=mvcount(src_ip) | where distinct_ip > 3`
Efficiently groups events by user within a 5-minute window and then counts distinct IP addresses.
Question 453 of 500
SPLK-1003 Advanced Searching and Statistics Practice Question
This SPLK-1003 practice question tests your understanding of advanced searching and statistics. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 search needs to find events where the same user logged in from more than 3 different IP addresses within a 5-minute window. Which combination of commands is most efficient?
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
✓
`| transaction user maxspan=5m | eval distinct_ip=mvcount(src_ip) | where distinct_ip > 3`
Option D is correct because the `transaction` command groups events by `user` within a 5-minute window (`maxspan=5m`), then `eval distinct_ip=mvcount(src_ip)` counts the unique IP addresses in that transaction. This directly answers the requirement of finding users who logged in from more than 3 different IPs within a 5-minute window, and it is efficient because `transaction` handles the time-bounded grouping natively without needing to pre-aggregate or use subsearches.
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.
- ✗
`| streamstats count by user src_ip | where count > 3`
Why it's wrong here
`streamstats` counts running total within the stream, not grouping events into transactions.
- ✗
`| timechart span=5m limit=0 values(src_ip) by user | eval count=mvcount(values(src_ip)) | where count > 3`
Why it's wrong here
This creates time buckets, but the same user might appear in multiple buckets; also it uses timechart which is less efficient.
- ✗
`| stats count by user, src_ip | where count > 3`
Why it's wrong here
This counts events per user-IP combo, not distinct IPs per user over time.
- ✓
`| transaction user maxspan=5m | eval distinct_ip=mvcount(src_ip) | where distinct_ip > 3`
Why this is correct
Efficiently groups events by user within a 5-minute window and then counts distinct IP addresses.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often choose `streamstats` or `stats` because they are familiar with counting, but they fail to realize that those commands count events per user+IP pair rather than distinct IPs per user within a time window, which is the core requirement.
Detailed technical explanation
How to think about this question
The `transaction` command groups events based on fields (here `user`) and time constraints (`maxspan=5m`), creating a multivalue field for `src_ip` that can be counted with `mvcount`. Under the hood, `transaction` uses a sliding window to group events, which can be memory-intensive for large datasets, but for this specific use case it is the most direct approach. A real-world scenario might involve detecting brute-force attacks where a single user account is accessed from multiple IPs in rapid succession, and `transaction` ensures that only IPs within the same 5-minute window are considered together.
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-1003 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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Advanced Searching and Statistics — study guide chapter
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FAQ
Questions learners often ask
What does this SPLK-1003 question test?
Advanced Searching and Statistics — This question tests Advanced Searching and Statistics — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: `| transaction user maxspan=5m | eval distinct_ip=mvcount(src_ip) | where distinct_ip > 3` — Option D is correct because the `transaction` command groups events by `user` within a 5-minute window (`maxspan=5m`), then `eval distinct_ip=mvcount(src_ip)` counts the unique IP addresses in that transaction. This directly answers the requirement of finding users who logged in from more than 3 different IPs within a 5-minute window, and it is efficient because `transaction` handles the time-bounded grouping natively without needing to pre-aggregate or use subsearches.
What should I do if I get this SPLK-1003 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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Last reviewed: Jun 25, 2026
This SPLK-1003 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-1003 exam.
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