The correct answer is the combination `| transaction user maxspan=5m | eval distinct_ip=mvcount(src_ip) | where distinct_ip > 3`. This works because the `transaction` command natively groups all events sharing the same `user` field into a single multivalue result, and the `maxspan=5m` parameter enforces a strict 5-minute time window for that grouping. The `eval` then uses `mvcount` to count the distinct IP addresses within each transaction, and `where` filters for those exceeding three. On the SPLK-1003 exam, this question tests your understanding of time-bounded event grouping versus using stats or subsearches, which are less efficient for this specific requirement. A common trap is trying to use `stats values(src_ip) by user` with a time bucket, but that requires manual time-range handling and can miss events that span bucket boundaries. Remember the memory tip: “Transaction ties time and user together; mvcount counts the IPs you gather.”
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 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 small business has 20 workstations on the 192.168.1.0/24 network and one public IP from its ISP. The router uses PAT (NAT overload) so all 20 devices share one public address using different source ports. NAT questions test whether you understand the four address terms and which direction each translation applies.
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.
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.
About these practice questions
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These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A user needs to find events where a user had a failed login followed by a successful login within 10 minutes, and then list the total number of such occurrences per user. Which THREE steps are necessary? (Select three.)
medium
A.Use the eval command to set a field for failure status
✓ B.Use the stats command to count by user
C.Use the where command to filter transactions with both failure and success
✓ D.Use the transaction command with maxspan=10m
✓ E.Use the transaction command with startswith and endswith
Why B: Options A, B, and C are correct. The transaction command (A) with maxspan=10m groups events, and startswith/endswith (B) define the transaction boundaries. Then stats (C) counts the transactions per user. Option D is not needed because transaction ensures the pattern. Option E is not necessary as fields exist.
Last reviewed: Jun 25, 2026
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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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