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Splunk SPLK-5002 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 3
  • 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.
Topic 4
  • 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 5
  • 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.

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Splunk Certified Cybersecurity Defense Engineer Sample Questions (Q69-Q74):

NEW QUESTION # 69
MITRE D3FEND is designed to compliment MITRE ' s list of adversarial tactics, techniques, and common knowledge (ATT & CK). Which tactics are associated with MITRE D3FEND in order to detect, deny, and disrupt adversarial efforts?

Answer: D

Explanation:
The defensive tactic categories represented by MITRE D3FEND in this course context are Harden, Detect, Isolate, Deceive, and Evict .
Harden focuses on increasing resistance to adversary techniques through defensive configuration and protection. Detect encompasses techniques intended to identify malicious activity or artifacts. Isolate limits adversary access or interaction with protected resources. Deceive deliberately manipulates an adversary ' s perception of the environment, often using decoys or deceptive information. Evict concerns removing adversarial presence and associated persistence from the defended environment.
D3FEND complements ATT & CK because the two frameworks address opposite sides of the defensive problem. ATT & CK catalogs observable adversary behaviors and techniques, while D3FEND provides structured defensive knowledge that can be associated with those behaviors. Detection engineers can therefore use ATT & CK to understand what an adversary may do and D3FEND to reason about defensive countermeasures.
The supplied Cybersecurity Defense Engineer material directly includes this D3FEND tactic-set question.
Study Guide topics: MITRE D3FEND, MITRE ATT & CK, defensive countermeasures, threat-informed defense, detection strategy.


NEW QUESTION # 70
What feature allows you to extract additional fields from events at search time?

Answer: D

Explanation:
Splunk allows dynamic field extraction to enhance data analysis without modifying raw indexed data.
Search-Time Field Extraction:
Extracts fields on-demand when running searches.
Uses Splunk's Field Extraction Engine (rex,spath, or automatic field discovery).
Minimizes indexing overhead by keeping the raw data unchanged.


NEW QUESTION # 71
When creating a case in Splunk SOAR, which action should be taken to correlate various findings (risk notables) to ensure all are actioned?

Answer: A

Explanation:
When creating a case in Splunk SOAR, correlation is achieved by searching Splunk Enterprise Security for all related events based on key fields in a risk notable, then deciding how to process and merge those events into the investigation. This ensures that all relevant risk notables are actioned together for a complete response.


NEW QUESTION # 72
An engineer receives a report that the "Traffic over time by action" dashboard is not populating. It has been confirmed that the relevant logs are being ingested properly and they are CIM compliant. What other configuration may be missing?

Answer: B

Explanation:
The "Traffic over time by action" dashboard relies on the Network Traffic data model. For it to populate correctly, the data model must be accelerated, ensuring that the dashboard can pull from the accelerated summaries instead of raw data.


NEW QUESTION # 73
In which threat intelligence KV store would a list of malicious domains (FQDNs) be stored?

Answer: A

Explanation:
The correct collection is service_intel . Splunk Enterprise Security ' s Threat Intelligence Framework separates indicators into intelligence collections according to the type of observable being represented. Fully Qualified Domain Names are service-oriented network identifiers and are handled through the service intelligence collection in the context tested by this question.
This classification matters because the Threat Intelligence Framework must know which event fields and indicator types can be meaningfully compared. A domain such as malicious.example.com is semantically different from a raw IPv4/IPv6 address, a certificate fingerprint, or a complete HTTP URL. The ip_intel collection is intended for IP-oriented indicators, while certificate_intel deals with certificate-related intelligence. http_intel is associated with HTTP-oriented indicators such as URLs and related HTTP observables rather than the standalone FQDN type being asked about here.
Detection engineering depends on this normalization because matching searches must compare compatible indicator types. Correct placement also supports deduplication, expiration, weighting, threat matching, and downstream enrichment of security findings.
Study Guide topics: Threat Intelligence Framework, KV Store collections, indicator normalization, FQDN intelligence, threat matching, intelligence enrichment.


NEW QUESTION # 74
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