Question 500 of 510

Data Models and Best Practices →hardMultiple ChoiceObjective-mapped

Quick Answer

The answer is to review the data model fields and remove high-cardinality fields from the acceleration or the data model itself. This is correct because when the summary size equals the raw index size, it indicates that data model acceleration is failing to reduce the data volume—a classic symptom of high cardinality fields like `src_ip` or `user`, which create too many unique combinations for the summary to compress effectively. On the Splunk SPLK-1002 exam, this scenario tests your understanding of how acceleration works and the common trap of assuming more fields or longer summary ranges improve performance, when in fact they worsen it. The key insight is that acceleration relies on reducing unique values; high cardinality defeats that purpose. Memory tip: if your summary is as big as your raw data, think "high cardinality is the enemy of acceleration."

SPLK-1002 Data Models and Best Practices Practice Question

This SPLK-1002 practice question tests your understanding of data models and best practices. 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 financial services company uses Splunk to monitor authentication logs from 500 remote servers. They created a data model named 'Authentication' with 15 fields including 'user', 'src_ip', 'dest_ip', 'action', and 'status'. They enabled acceleration with a summary range of 1 day and set the maximum search time range to 30 days. After one month of operation, searches against the data model that used to complete in seconds now time out after 60 seconds. The average daily log volume is 10 GB. The admin runs | datamodel Audit and discovers that the summary size is approximately 5 GB per day, which is similar to the raw data index size. The search head has 16 GB RAM and 4 CPU cores, and no other resource issues are observed. What is the most likely cause of the performance degradation?

Clue words in this question

Noticing these words before you look at the options changes how you read each choice.

Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.

Question 1hardmultiple choice

Full question →

A
Optimize the underlying searches by using indexed field extractions instead of search-time field extractions.
Why wrong: This may help search performance but does not address the acceleration summary size issue.
B
Increase the summary range from 1 day to 7 days to reduce the number of summaries.
Why wrong: A longer summary range would create larger summaries, worsening performance.
C
Review the data model fields and remove high-cardinality fields from the acceleration or the data model itself.
High-cardinality fields prevent effective summarization, causing summary size to approach raw data size.
D
Reduce the number of fields in the data model to fewer than 10 to improve acceleration efficiency.
Why wrong: The number of fields is not the issue; it is the cardinality of those fields.

Full breakdown with real-world context →

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

✓

Review the data model fields and remove high-cardinality fields from the acceleration or the data model itself.

Option B is correct because the summary size being nearly equal to the raw index indicates that the accelerated data is not significantly reduced; this typically happens when the data model has high cardinality fields (like src_ip or user) that produce many unique combinations, preventing effective summarization. Option A is wrong because increasing the summary range would only make the summary larger and exacerbate the problem. Option C is wrong because default field extraction tuning does not directly cause acceleration to fail; the issue is cardinality. Option D is wrong because the data model design itself is flawed; using all 15 fields in the data model is not the problem—the high cardinality fields are the issue.

Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
Optimize the underlying searches by using indexed field extractions instead of search-time field extractions.
Why it's wrong here
This may help search performance but does not address the acceleration summary size issue.
✗
Increase the summary range from 1 day to 7 days to reduce the number of summaries.
Why it's wrong here
A longer summary range would create larger summaries, worsening performance.
✓
Review the data model fields and remove high-cardinality fields from the acceleration or the data model itself.
Why this is correct
High-cardinality fields prevent effective summarization, causing summary size to approach raw data size.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Static NAT maps one inside address to one outside address.
✗
Reduce the number of fields in the data model to fewer than 10 to improve acceleration efficiency.
Why it's wrong here
The number of fields is not the issue; it is the cardinality of those fields.

Common exam traps

Common exam trap: NAT rules depend on direction and matching traffic

NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.

Detailed technical explanation

How to think about this question

NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.

KKey Concepts to Remember

Static NAT maps one inside address to one outside address.
PAT allows many inside hosts to share one public address using ports.
Inside local and inside global describe the private and translated addresses.
NAT ACLs identify traffic for translation, not always security filtering.

TExam Day Tips

Identify inside and outside interfaces first.
Check whether the scenario needs static NAT, dynamic NAT or PAT.
Do not confuse NAT matching ACLs with normal packet-filtering intent.

Key takeaway

NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.

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.

Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related SPLK-1002 NAT questions on configuration and troubleshooting.

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FAQ

Questions learners often ask

What does this SPLK-1002 question test?

Data Models and Best Practices — This question tests Data Models and Best Practices — Static NAT maps one inside address to one outside address..

What is the correct answer to this question?

The correct answer is: Review the data model fields and remove high-cardinality fields from the acceleration or the data model itself. — Option B is correct because the summary size being nearly equal to the raw index indicates that the accelerated data is not significantly reduced; this typically happens when the data model has high cardinality fields (like src_ip or user) that produce many unique combinations, preventing effective summarization. Option A is wrong because increasing the summary range would only make the summary larger and exacerbate the problem. Option C is wrong because default field extraction tuning does not directly cause acceleration to fail; the issue is cardinality. Option D is wrong because the data model design itself is flawed; using all 15 fields in the data model is not the problem—the high cardinality fields are the issue.

What should I do if I get this SPLK-1002 question wrong?

Are there clue words in this question I should notice?

Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.

What is the key concept behind this question?

Static NAT maps one inside address to one outside address.

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Last reviewed: Jun 24, 2026

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