The correct answer is to use `eventstats` to compute the global average and standard deviation, then filter with `where`, and finally calculate the clean average with `stats`. This approach is efficient because `eventstats` appends the aggregate values—like `avg(response_time)` and `stdev(response_time)`—to every event without collapsing the dataset, allowing you to apply a `where` clause that removes any event where `response_time` falls outside the range of the mean plus or minus three standard deviations. On the Splunk SPLK-1003 exam, this question tests your understanding of the critical difference between `eventstats` (which retains all events) and `stats` (which reduces rows), a common trap where candidates mistakenly use `stats` first and lose the raw data needed for filtering. A solid memory tip: think of `eventstats` as “sticky stats” that cling to each event, letting you slice outliers before re-aggregating.
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 returns raw events with a field 'response_time'. The analyst wants to calculate the average response time excluding any outliers that are more than 3 standard deviations from the mean. Which SPL approach 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
✓
Use | eventstats avg, stdev(response_time) then | where response_time<=avg+3*stdev and response_time>=avg-3*stdev then | stats avg(response_time)
Option A is correct because it uses `eventstats` to compute the global average and standard deviation of `response_time` across all events, then filters out outliers (values more than 3 standard deviations from the mean) with a `where` clause, and finally calculates the clean average with `stats avg(response_time)`. This approach is efficient because `eventstats` adds the aggregate values to each event without reducing the dataset, allowing a single pass through the data for filtering and aggregation.
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.
✓
Use | eventstats avg, stdev(response_time) then | where response_time<=avg+3*stdev and response_time>=avg-3*stdev then | stats avg(response_time)
Why this is correct
Efficient one-pass calculation with filtering
Related concept
Read the scenario before looking for a memorised answer.
✗
Use | top response_time
Why it's wrong here
Top shows most frequent values, not average
✗
Use | stats avg(response_time) and then filter with where
Why it's wrong here
Requires two searches and is inefficient
✗
Use | outlier action=remove
Why it's wrong here
Removes outliers but not based on standard deviation
Common exam traps
Common exam trap: answer the scenario, not the keyword
Splunk often tests the distinction between `eventstats` and `stats`, where candidates mistakenly use `stats` first and then try to filter, not realizing that `stats` collapses events and loses the ability to apply per-event conditions.
Trap categories for this question
Command / output trap
Top shows most frequent values, not average
Detailed technical explanation
How to think about this question
The `eventstats` command computes aggregate statistics (like `avg` and `stdev`) across the entire result set and appends them as new fields to each event, enabling row-level filtering with `where`. This is more efficient than using `stats` first, which would require a subsearch or a second pass. In real-world scenarios, such as monitoring API response times, removing outliers based on standard deviation helps identify performance anomalies without skewing averages from transient spikes.
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.
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: Use | eventstats avg, stdev(response_time) then | where response_time<=avg+3*stdev and response_time>=avg-3*stdev then | stats avg(response_time) — Option A is correct because it uses `eventstats` to compute the global average and standard deviation of `response_time` across all events, then filters out outliers (values more than 3 standard deviations from the mean) with a `where` clause, and finally calculates the clean average with `stats avg(response_time)`. This approach is efficient because `eventstats` adds the aggregate values to each event without reducing the dataset, allowing a single pass through the data for filtering and aggregation.
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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Question Discussion
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