The answer is that the unexpected spikes are caused by events containing different time zones. When Splunk aggregates data by hour in a timechart, it converts all timestamps to the search head’s local time zone using the `_time` field. Events originally logged in UTC, EST, or other zones get shifted into different hour buckets, creating artificial spikes at time zone boundaries—especially during daylight saving time transitions. On the SPLK-1002 exam, this question tests your understanding of how Splunk normalizes timestamps and how misaligned time zones distort time-series analysis. A common trap is to blame data volume spikes or indexing delays, but the real culprit is inconsistent time zone handling across your data sources. To remember this, think of the “time zone shuffle”: if your chart jumps at odd hours, your timestamps are likely speaking different time zone languages.
SPLK-1002 Practice Question: Creating Reports, Dashboards and Visualizations
This SPLK-1002 practice question tests your understanding of creating reports, dashboards and visualizations. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 visualization is showing unexpected spikes in a timechart. The data is aggregated by hour, but the spikes align with time zone changes. What is the likely cause?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The data contains events with different time zones
The unexpected spikes in the timechart are caused by events being timestamped with different time zones. When Splunk aggregates data by hour using `_time`, it converts all timestamps to the search head's local time zone. Events originally logged in different time zones (e.g., UTC vs. EST) will be shifted into different hour buckets, creating artificial spikes at the boundaries of time zone changes (e.g., daylight saving time transitions). This is a classic time zone misalignment issue in time-series analysis.
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.
✓
The data contains events with different time zones
Why this is correct
Mixed time zones cause events to be bucketed into incorrect hours, creating spikes.
Related concept
Read the scenario before looking for a memorised answer.
✗
The timechart uses a gap threshold
Why it's wrong here
Gap threshold controls how to fill missing data, not spikes.
✗
The search uses _time instead of _indextime
Why it's wrong here
Using _time is standard; using _indextime could cause different issues but not spikes related to time zones.
✗
The bin span is too small
Why it's wrong here
A smaller span would increase granularity but not cause spikes related to time zones.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume spikes are caused by data volume or indexing delays, rather than recognizing that time zone misalignment in event timestamps can create artificial spikes in timecharts, especially during daylight saving time transitions.
Detailed technical explanation
How to think about this question
Under the hood, Splunk's `_time` field is stored as epoch time (Unix timestamp) in UTC. When a timechart command uses `_time` with a span, Splunk bins events into time buckets based on the search head's local time zone. If events have different original time zones (e.g., some in UTC, some in America/New_York), the conversion to UTC and then to the local time zone can cause events from the same real-world hour to fall into different buckets. For example, during the fall-back daylight saving time transition, events logged at 1:30 AM EDT and 1:30 AM EST both become 5:30 AM UTC and 6:30 AM UTC respectively, creating a double-count in the local time zone bucket. A real-world scenario is analyzing logs from globally distributed servers where each server logs in its local time zone without explicit time zone normalization.
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-1002 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.
Creating Reports, Dashboards and Visualizations — This question tests Creating Reports, Dashboards and Visualizations — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: The data contains events with different time zones — The unexpected spikes in the timechart are caused by events being timestamped with different time zones. When Splunk aggregates data by hour using `_time`, it converts all timestamps to the search head's local time zone. Events originally logged in different time zones (e.g., UTC vs. EST) will be shifted into different hour buckets, creating artificial spikes at the boundaries of time zone changes (e.g., daylight saving time transitions). This is a classic time zone misalignment issue in time-series analysis.
What should I do if I get this SPLK-1002 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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