The answer is a column chart with | stats count by src_ip | where count > 10. This combination is correct because it first uses the `stats` command to aggregate login failures per source IP, then applies the `where` command to filter out any IPs with a count of 10 or fewer, ensuring only IPs exceeding the threshold are visualized. The column chart is ideal here because it clearly compares discrete counts across categorical values like IP addresses. On the Splunk Core Certified Power User SPLK-1003 exam, this question tests your understanding of post-aggregation filtering—a common trap is applying `where` before `stats`, which would filter raw events rather than aggregated results. Remember the memory tip: “Stats first, then where—filter the group, not the raw.” This ensures you meet the requirement to visualize only high-frequency failures while keeping the chart readable and actionable.
SPLK-1003 Advanced Visualization and Lookups Practice Question
This SPLK-1003 practice question tests your understanding of advanced visualization and lookups. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. 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 security analyst wants to visualize the count of login failures by source IP over the last 24 hours, but only for IPs with more than 10 failures. Which visualization type and SPL command combination is most appropriate?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Column chart with | stats count by src_ip | where count > 10
Option B is correct because it uses `stats count by src_ip` to aggregate login failures per source IP, then `where count > 10` to filter only IPs exceeding 10 failures, and a column chart is ideal for comparing discrete counts across categories (IP addresses). This combination directly answers the requirement: visualize count of failures by IP, with a threshold filter applied after 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.
✗
Line chart with | top limit=10 showcount=1 by src_ip
Why it's wrong here
Top command shows most frequent values but does not allow filtering by count threshold.
✓
Column chart with | stats count by src_ip | where count > 10
Why this is correct
Correctly uses stats to count, filters, and column chart for comparison.
Related concept
Read the scenario before looking for a memorised answer.
✗
Scatter plot with | stats dc(src_ip) by failure
Why it's wrong here
Scatter plot requires two numeric axes; dc computes distinct count.
✗
Pie chart with | chart count over src_ip | where count > 10
Why it's wrong here
Chart command syntax is incorrect; pie chart is not ideal for many categories.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse `top` with a threshold filter or misuse `chart` with incorrect syntax, thinking it can replace `stats` for aggregation, or they choose a visualization type (like scatter or pie) that is inappropriate for comparing counts across many categories.
Trap categories for this question
Command / output trap
Top command shows most frequent values but does not allow filtering by count threshold.
Detailed technical explanation
How to think about this question
The `stats count by src_ip` command groups events by the `src_ip` field and computes a count for each group, producing a table with `src_ip` and `count` columns. The `where count > 10` clause then filters rows where the count exceeds 10, which is a post-aggregation filter (not a pre-aggregation filter like `search`). Column charts (bar charts) are optimal for comparing discrete categorical values (IP addresses) against a numeric measure (count), as they allow easy visual comparison of heights. In Splunk, the `where` command operates on results after `stats`, so it correctly applies the threshold to aggregated counts.
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 Visualization and Lookups — This question tests Advanced Visualization and Lookups — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Column chart with | stats count by src_ip | where count > 10 — Option B is correct because it uses `stats count by src_ip` to aggregate login failures per source IP, then `where count > 10` to filter only IPs exceeding 10 failures, and a column chart is ideal for comparing discrete counts across categories (IP addresses). This combination directly answers the requirement: visualize count of failures by IP, with a threshold filter applied after 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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