What is a common scalability strategy for handling increasing volumes of security data?

Distributing the data processing across multiple nodes

Adding more storage without changing processing

Increasing the frequency of full data exports

Reducing the number of data sources

What is a key performance factor to consider when integrating large volumes of security data?

The throughput of the data ingestion pipeline

The brand of the server hardware

The number of security analysts

A security team is integrating a new data source and has set up a test ingestion. They notice that the data is being indexed, but some events have missing fields that are critical for correlation. The source system's logs are known to occasionally omit these fields. What is the best approach to ensure data quality?

Reject all events that are missing the critical fields

Implement a validation and enrichment step that fills in missing fields using other data sources or default values

Ignore the missing fields because they are not always needed

Ask the source system vendor to fix the logging issue

A company is integrating a new data source that will double their daily data volume. They are concerned about the cost of storage and the impact on search performance. They need to maintain full-fidelity data for compliance but also want to optimize costs. What is the best strategy?

Reduce the retention period for the new data source to lower costs

Store the data in a separate, low-cost index with no search acceleration

Use a tiered storage strategy: keep hot data in fast storage and archive older data to cheaper storage

Sample the data to reduce volume, keeping only a percentage of events

After integrating a new data source, the Splunk search head is experiencing slow search performance. The team suspects that the new source is causing the issue. What should they do to optimize the integration?

Increase the search head's memory allocation

Disable all other data sources to reduce the load

Review the data model acceleration settings and consider accelerating the relevant data models

Move the new data source to a separate Splunk instance

SplunkCertified Cybersecurity Defense Architect

Domain 2Objective 1

Explain How to Develop and Implement Integration Strategies for Data-Driven Security Operations. CYBERSECURITY-DEFENSE-ARCHITECT Practice Questions (Page 4)

Part of the Security Data Management domain, which accounts for 20% of the CYBERSECURITY-DEFENSE-ARCHITECT exam. Splunk does not publish an official question count, but from its 75-minute exam (~30–50 total, ~6–10 in this domain), expect 1–1 from this objective — we provide 30 practice questions to prepare you well beyond it. (estimate)

30questions here

6free pages

7concepts

20%of the exam

Questions 16–20

16foundation · easy
What is a common scalability strategy for handling increasing volumes of security data?
Select an answer first
17foundation · easy
What is a key performance factor to consider when integrating large volumes of security data?
Select an answer first
18expert · hard
A security team is integrating a new data source and has set up a test ingestion. They notice that the data is being indexed, but some events have missing fields that are critical for correlation. The source system's logs are known to occasionally omit these fields. What is the best approach to ensure data quality?
Select an answer first
19expert · hard
A company is integrating a new data source that will double their daily data volume. They are concerned about the cost of storage and the impact on search performance. They need to maintain full-fidelity data for compliance but also want to optimize costs. What is the best strategy?
Select an answer first
20application · medium
After integrating a new data source, the Splunk search head is experiencing slow search performance. The team suspects that the new source is causing the issue. What should they do to optimize the integration?
Select an answer first

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