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Splunk SPLK-5002 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Automation and Efficiency: This section assesses Automation Engineers and SOAR Specialists in streamlining security operations. It covers developing automation for SOPs, optimizing case management workflows, utilizing REST APIs, designing SOAR playbooks for response automation, and evaluating integrations between Splunk Enterprise Security and SOAR tools.
Topic 2
  • Data Engineering: This section of the exam measures the skills of Security Analysts and Cybersecurity Engineers and covers foundational data management tasks. It includes performing data review and analysis, creating and maintaining efficient data indexing, and applying Splunk methods for data normalization to ensure structured and usable datasets for security operations.
Topic 3
  • Auditing and Reporting on Security Programs: This section tests Auditors and Security Architects on validating and communicating program effectiveness. It includes designing security metrics, generating compliance reports, and building dashboards to visualize program performance and vulnerabilities for stakeholders.
Topic 4
  • Detection Engineering: This section evaluates the expertise of Threat Hunters and SOC Engineers in developing and refining security detections. Topics include creating and tuning correlation searches, integrating contextual data into detections, applying risk-based modifiers, generating actionable Notable Events, and managing the lifecycle of detection rules to adapt to evolving threats.
Topic 5
  • Building Effective Security Processes and Programs: This section targets Security Program Managers and Compliance Officers, focusing on operationalizing security workflows. It involves researching and integrating threat intelligence, applying risk and detection prioritization methodologies, and developing documentation or standard operating procedures (SOPs) to maintain robust security practices.

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Splunk Certified Cybersecurity Defense Engineer Sample Questions (Q48-Q53):

NEW QUESTION # 48
What field is used by default to direct data into CIM data model datasets?

Answer: A

Explanation:
By default, data is directed into CIM (Common Information Model) data model datasets using the tag field. Tags applied to events determine which datasets the events populate, enabling normalization and alignment with CIM.


NEW QUESTION # 49
When setting Common Information Model (CIM) accelerations, which parameter should be defined to set how far back in time (specified as a relative time string) the Splunk platform creates its column stores?

Answer: B

Explanation:
The Summary range parameter in CIM accelerations defines how far back in time (using a relative time string) the Splunk platform creates its column stores. This determines the historical coverage of accelerated data available for searches and dashboards.


NEW QUESTION # 50
An engineer wants to track and report on all authentication to corporate assets, and wants to prioritize critical assets without significantly increasing the number of findings (notable events) generated. What process could be used to accomplish this goal?

Answer: C

Explanation:
By adding all access attempts to the Risk Index and then increasing the Criticality of critical assets, the engineer ensures all authentication activity is tracked while prioritizing findings involving high-value assets. This approach leverages risk-based alerting without flooding the SOC with unnecessary notable events.


NEW QUESTION # 51
Which Splunk feature helps to standardize data for better search accuracy and detection logic?

Answer: D

Explanation:
Why Use "Data Models" for Standardized Search Accuracy and Detection Logic?
SplunkData Modelsprovide astructured, normalized representationof raw logs, improving:
#Search consistency across different log sources#Detection logic by ensuring standardized field names#Faster and more efficient querieswith data model acceleration
#Example in Splunk Enterprise Security:#Scenario:A SOC team monitors login failures acrossmultiple authentication systems.#Without Data Models:Different logs usesrc_ip, source_ip, or ip_address, making searches complex.#With Data Models:All fieldsmap to a standard format, enablingconsistent detection logic.
Why Not the Other Options?
#A. Field Extraction- Extracts fields from raw events butdoes not standardize field names across sources.#C.
Event Correlation- Detects relationships between logsbut doesn't normalize data for search accuracy.#D.
Normalization Rules- A general term; Splunkuses CIM & Data Models for normalization.
References & Learning Resources
#Splunk Data Models Documentation: https://docs.splunk.com/Documentation/Splunk/latest/Knowledge
/Aboutdatamodels#Using CIM & Data Models for Security Analytics: https://splunkbase.splunk.com/app
/263#How Data Models Improve Search Performance: https://www.splunk.com/en_us/blog/tips-and-


NEW QUESTION # 52
When creating detections, which of the following sequences would result in the most performant SPL query?

Answer: B

Explanation:
The most performant SPL query sequence is:
Define base query → Minimize data → Combine/Summarize data → Execute calculations → Format the data.
Minimizing the data early (using filters, time constraints, and field limitations) reduces the dataset before expensive operations like summarization or calculations, resulting in optimal performance.


NEW QUESTION # 53
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