The correct answer is the Authentication dataset with the fields user, action, and _time. This is because the Authentication data model is purpose-built to capture login events, where the action field distinguishes between success and failure, and the _time field can be bucketed into hourly intervals to satisfy the requirement of counting failed attempts by user and hour. On the SPLK-1002 exam, this question tests your ability to map a reporting need to the correct data model and fields, a common task when you choose dataset and fields for pivot report. A frequent trap is selecting the Alerts or Audit datasets, which lack the specific action field for login outcomes. Remember the memory tip: "Auth for logins, action for wins or fails, _time for the hour that prevails."
SPLK-1002 Data Models and Best Practices Practice Question
This SPLK-1002 practice question tests your understanding of data models and best practices. 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 user wants to create a Pivot report that counts failed login attempts by user and hour. Which data model dataset and fields are most appropriate?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Authentication dataset, fields: user, action, and use _time for hour
Option D is correct because the Authentication dataset is specifically designed to store login-related events, and the 'action' field indicates success or failure. Using 'user' and '_time' (extracted to hour via time bucketing) allows counting failed attempts per user per hour. The other datasets either lack the necessary fields or are not focused on authentication events.
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.
✗
Web dataset, fields: user, status, time
Why it's wrong here
Web dataset is for HTTP traffic, not authentication failures.
src is not required for counting by user and hour.
✓
Authentication dataset, fields: user, action, and use _time for hour
Why this is correct
Authentication dataset with action=failure and _time allows counting by user and hour.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Splunk often tests the distinction between dataset purpose and field relevance, trapping candidates who choose a dataset with a plausible field name (like 'status' in Web) but that isn't designed for authentication events.
Detailed technical explanation
How to think about this question
In Splunk, Pivot relies on data model fields and their types (e.g., timestamp, string). The '_time' field is automatically indexed and can be bucketed into time spans (e.g., hour) using the Pivot interface's time range options. The 'action' field in the Authentication dataset typically contains values like 'success' or 'failure', enabling a filter for 'failure' before counting. This approach avoids writing raw SPL and leverages the data model's acceleration for faster queries.
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.
Data Models and Best Practices — This question tests Data Models and Best Practices — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Authentication dataset, fields: user, action, and use _time for hour — Option D is correct because the Authentication dataset is specifically designed to store login-related events, and the 'action' field indicates success or failure. Using 'user' and '_time' (extracted to hour via time bucketing) allows counting failed attempts per user per hour. The other datasets either lack the necessary fields or are not focused on authentication events.
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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