Quick Answer
The correct answer is `index=web | timechart dc(user) span=1h`. This search works because `timechart` automatically bins events into time-based buckets and applies a statistical function—here, `dc(user)` calculates the distinct count of users per bucket—while `span=1h` explicitly sets each bucket to one hour, ensuring the output shows exactly one data point per hour over the past week. On the Splunk SPLK-1003 exam, this question tests your understanding of how `timechart` implicitly groups by `_time` without needing a `by` clause, and it commonly traps candidates who mistakenly add a `by` field or use `count` instead of `dc`. A key memory tip: think of `dc()` as “distinct count” and remember that `timechart` always needs a `span` to control bucket size—if omitted, Splunk chooses a default that may not match your intent.
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 user wants to create a timechart showing the number of distinct users per hour over the past week. Which search is correct?
index=web | bucket _time span=1h | stats dc(user) by _time
Why wrong: Produces a table, not a timechart.
index=web | timechart dc(user) span=1h
Correctly uses timechart with distinct count function.
index=web | timechart dc(user) by _time span=1h
Why wrong: The 'by _time' clause is unnecessary and may cause issues.
index=web | timechart count by user span=1h
Why wrong: Shows count per user, not distinct users.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
index=web | timechart dc(user) span=1h
Option B is correct because the `timechart` command automatically creates a time-based chart with a default or specified `span`, and `dc(user)` calculates the distinct count of users per time bucket. The `span=1h` argument sets the bucket size to one hour, and the `by` clause is not needed because `timechart` implicitly groups by `_time`. This produces the desired output of distinct users per hour over the past week.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
index=web | bucket _time span=1h | stats dc(user) by _time
Why it's wrong here
Produces a table, not a timechart.
index=web | timechart dc(user) span=1h
Why this is correct
Correctly uses timechart with distinct count function.
Related concept
Read the scenario before looking for a memorised answer.
index=web | timechart dc(user) by _time span=1h
Why it's wrong here
The 'by _time' clause is unnecessary and may cause issues.
index=web | timechart count by user span=1h
Why it's wrong here
Shows count per user, not distinct users.
Common exam traps
Splunk often tests the distinction between `stats` and `timechart` — the trap here is that candidates mistakenly use `stats ... by _time` (Option A) thinking it produces a timechart, or misuse the `by` clause in `timechart` (Option C) by including `_time` as a split-by field, which breaks the time-based grouping.
Trap categories for this question
Command / output trap
Shows count per user, not distinct users.
Detailed technical explanation
The `timechart` command in Splunk uses a fixed time field (`_time`) and automatically bins events into time buckets based on the `span` argument, then applies an aggregate function (like `dc()`) to each bucket. Under the hood, `timechart` is equivalent to `bin _time span=1h | stats dc(user) by _time` but with additional formatting for charting, including filling gaps for empty time buckets. In real-world scenarios, using `timechart` with `dc()` is essential for monitoring unique user activity over time, such as tracking daily active users (DAU) or hourly unique visitors, where missing buckets could misrepresent trends.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Advanced Searching and Statistics — study guide chapter
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FAQ
Advanced Searching and Statistics — This question tests Advanced Searching and Statistics — Read the scenario before looking for a memorised answer..
The correct answer is: index=web | timechart dc(user) span=1h — Option B is correct because the `timechart` command automatically creates a time-based chart with a default or specified `span`, and `dc(user)` calculates the distinct count of users per time bucket. The `span=1h` argument sets the bucket size to one hour, and the `by` clause is not needed because `timechart` implicitly groups by `_time`. This produces the desired output of distinct users per hour over the past week.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 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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