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| Certification Vendor: | Splunk |
|---|---|
| Exam Name: | Splunk Certified Cybersecurity Defense Engineer |
| Exam Number: | SPLK-5002 |
| Exam Price: | $200 USD |
| Related Certifications: | Splunk Core Certified User Splunk SOAR Certified Automation Developer Splunk Enterprise Security Certified Admin |
| Exam Duration: | 120 minutes |
| Certificate Validity Period: | 3 years |
| Available Languages: | English |
| Exam Format: | Multiple select, Multiple choice, Hands-on lab simulation |
| Passing Score: | 65-70% (variable) |
| Real Exam Qty: | 82 |
| Sample Questions: | Splunk SPLK-5002 Sample Questions |
| Exam Way: | Online proctored exam at Pearson VUE testing centers or remote proctoring |
| Pre Condition: | Splunk Core Certified User, Splunk Enterprise Security Certified Admin, and Splunk SOAR Certified Automation Developer recommended; minimum 1-2 years hands-on Splunk security experience strongly advised |
| Official Syllabus URL: | https://www.splunk.com/en_us/training/certification-track/splunk-certified-cybersecurity-defense-engineer.html |
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NEW QUESTION # 10
What must be configured as a setting in a correlation search for a notable to be generated?
Answer: D
Explanation:
A correlation search must have the appropriate Adaptive Response Action configured when its intended outcome is creation of a notable event. Consequently, option C is correct.
The correlation search itself defines the analytics used to identify suspicious activity. Running that search successfully does not, by itself, mean every result automatically becomes a notable. The response configuration determines what Enterprise Security should do after the detection conditions are satisfied.
Configuring the notable-related adaptive response action supplies that operational behavior.
This separation is important because the same detection framework can support different outcomes.
Depending on design requirements, a correlation search may create analyst-facing findings, generate risk, invoke another response mechanism, or participate in additional automated workflows. The detection logic and response behavior therefore represent distinct parts of the engineering process.
A SOAR playbook is not a prerequisite for generating a notable; SOAR normally operates as a subsequent orchestration or response capability. Likewise, appending a | notable command to the SPL is not the configuration requirement being tested by this question.
Study Guide topics: Enterprise Security correlation searches, notable generation, Adaptive Response Actions, detection outcomes, response configuration.
NEW QUESTION # 11
Which of the following should an engineer do as they evaluate their Threat Detection and Incident Response lifecycle?
Answer: A
Explanation:
Threat Detection and Incident Response must be evaluated in the context of the organization ' s business model, operating environment, assets, adversaries, and industry-specific threat landscape . Detection engineering cannot be prioritized effectively using technical indicators alone.
For example, credential-access activity affecting an ordinary laboratory workstation and the same activity affecting a privileged financial system may require substantially different priorities. Similarly, a healthcare organization, financial institution, manufacturer, and cloud provider have different critical systems, regulatory requirements, attack surfaces, and likely adversary objectives.
MITRE ATT & CK provides an excellent taxonomy for adversary tactics and techniques, but ATT & CK itself does not define an organization ' s risk appetite . Risk appetite depends on business governance and tolerance for operational, financial, regulatory, and security impact. Therefore, option B improperly substitutes a threat-behavior framework for a business risk-management decision.
Focusing on the least impactful threat vectors is equally inconsistent with risk-based security engineering.
Effective programs allocate detection and response resources according to realistic threats and organizational consequences.
The question appears in the lifecycle section on page 3 of the supplied material.
Study Guide topics: Threat Detection and Incident Response lifecycle, business context, risk prioritization, threat modeling, MITRE ATT & CK, program maturity.
NEW QUESTION # 12
What is one method used in ESCU content to calculate a risk score when creating a detection that uses the Risk Analysis adaptive response action?
Answer: A
Explanation:
A common ESCU methodology calculates risk as:
Risk Score = Impact × Confidence / 100
Impact represents the potential significance or consequence of the detected behavior, while confidence represents how strongly the analytic supports the conclusion that the activity is security-relevant. Dividing by
100 normalizes the confidence percentage when combining the two values.
For example, if a detection has an impact value of 80 and confidence of 75%, the resulting score is:
80 × 75 / 100 = 60
This methodology prevents a high-impact but low-confidence analytic from automatically producing the same risk contribution as a high-impact, high-confidence detection. It therefore supports Risk-Based Alerting by allowing individual detections to contribute proportional evidence to a user, host, or other risk object.
Risk-object priority or severity can still influence downstream prioritization through contextual enrichment and Risk Factors, but those concepts are distinct from this ESCU risk-score calculation. The supplied Cybersecurity Defense Engineer material separately reinforces the role of risk scores, Risk Factors, and contextual prioritization in Enterprise Security.
Study Guide topics: ESCU, Risk Analysis adaptive response action, risk score, impact, confidence, Risk- Based Alerting, risk objects.
NEW QUESTION # 13
Which actions enhance the accuracy of Splunk dashboards?(Choosetwo)
Answer: A,D
Explanation:
How to Improve Dashboard Accuracy in Splunk?
#1. Using Accelerated Data Models (Answer A)#Increases search speedand ensuresdashboards load faster.
#Provides pre-processed structured dataforreal-time analysis.#Example:ASOC dashboard tracking failed loginsuses an accelerated authentication data model forfaster rendering.
#2. Performing Regular Data Validation (Answer C)#Ensures that the indexed data is accurate and complete.
#Prevents misleading dashboardscaused by incomplete logs or incorrect field extractions.#Example:If afirewall log source stops sending data, regular validation detects missing logsbefore analysts rely on incorrect dashboards.
Why Not the Other Options?
#B. Avoiding token-based filters- Tokensimprovedashboard flexibility; avoiding themreduces usability.#D.
Disabling drill-down features- Drill-downsenhance insightsby allowing analysts to investigate details easily.
References & Learning Resources
#Splunk Dashboard Performance Optimization: https://docs.splunk.com/Documentation/Splunk/latest/Viz
/Dashboards#Using Data Models for Fast and Accurate Dashboards: https://splunkbase.splunk.com#Regular Data Validation for SOC Dashboards: https://www.splunk.com/en_us/blog/security
NEW QUESTION # 14
Which Splunk Enterprise Security add-on facilitates the ingestion of Threat Intelligence data?
Answer: C
Explanation:
The correct component is SA-ThreatIntelligence . Within Splunk Enterprise Security, this supporting add-on is associated with the threat-intelligence framework and the processing of threat indicators used for matching, enrichment, and security analytics.
Threat intelligence may contain observables such as malicious IP addresses, domains, URLs, email indicators, certificate information, file hashes, or other intelligence objects. The framework must normalize and process these indicators so that Enterprise Security searches can compare them with telemetry observed in the environment.
The SA- prefix is significant in the Splunk application ecosystem because supporting add-ons frequently provide underlying searches, knowledge objects, configurations, or framework functionality that other Splunk applications consume. The other names shown in the question are distractors and are not the designated Enterprise Security supporting add-on requested.
Threat intelligence ingestion is more than simply indexing a feed. The resulting indicators must be structured into appropriate collections and made usable by matching processes so that detections can identify interactions between internal activity and known threat objects.
Question 9 is displayed on page 3 of the supplied certification material.
Study Guide topics: SA-ThreatIntelligence, Threat Intelligence Framework, indicator ingestion, threat matching, intelligence normalization, Enterprise Security architecture.
NEW QUESTION # 15
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