Quick Answer
The answer is the search using `... | stats dc(user) by department`. This is correct because the `dc()` function, short for "distinct count," calculates the number of unique values in a specified field—in this case, `user`—while the `by department` clause groups those counts per department, directly fulfilling the analyst’s need for a distinct user count over the last week. On the Splunk SPLK-1003 exam, this question tests your understanding of the `stats` command’s aggregation functions, particularly `dc()`, which is a common tool for cardinality analysis. A frequent trap is confusing `dc()` with `count()`, which counts all events including duplicates, so remember that `dc()` is for uniqueness. For a memory tip, think "dc = distinct count" or simply "d for distinct, c for count."
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.
An analyst wants to see the count of distinct users for each department over the last week. The data contains fields: user, department, date. Which search is correct?
... | stats distinct_count(user) by department
Why wrong: distinct_count is not valid; use dc().
... | stats dc(user) by department
dc() calculates distinct count.
... | eval distinct_count=dc(user) | stats sum(distinct_count) by department
Why wrong: dc cannot be used in eval; also sum of distinct counts is not meaningful.
... | stats count(user) by department
Why wrong: count counts all occurrences, not distinct.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
... | stats dc(user) by department
Option B is correct because the `dc()` function in Splunk's `stats` command calculates the distinct count of values in a field, which is exactly what the analyst needs: the count of distinct users per department over the last week. The `by department` clause groups the results by department, and the implicit time range (last week) is applied via the search time picker or an explicit time filter in the query.
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.
... | stats distinct_count(user) by department
Why it's wrong here
distinct_count is not valid; use dc().
... | stats dc(user) by department
Why this is correct
dc() calculates distinct count.
Related concept
Read the scenario before looking for a memorised answer.
... | eval distinct_count=dc(user) | stats sum(distinct_count) by department
Why it's wrong here
dc cannot be used in eval; also sum of distinct counts is not meaningful.
... | stats count(user) by department
Why it's wrong here
count counts all occurrences, not distinct.
Common exam traps
The trap here is that candidates often confuse `count()` (total events) with `dc()` (distinct count) or try to use `distinct_count` as a function name, which is a common Splunk syntax mistake tested in the SPLK-1003 exam.
Detailed technical explanation
The `dc()` function in Splunk uses a hyperloglog algorithm to estimate distinct counts efficiently, which is memory-optimized for large datasets. When used with `by department`, Splunk internally creates a separate hyperloglog sketch for each department, then merges them to produce the distinct count per group. This is particularly useful in scenarios like tracking unique visitors per site or unique error codes per host over time, where exact counts are less critical than performance.
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: ... | stats dc(user) by department — Option B is correct because the `dc()` function in Splunk's `stats` command calculates the distinct count of values in a field, which is exactly what the analyst needs: the count of distinct users per department over the last week. The `by department` clause groups the results by department, and the implicit time range (last week) is applied via the search time picker or an explicit time filter in the query.
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 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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