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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
  • 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 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
  • 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
  • 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.

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Splunk Certified Cybersecurity Defense Engineer Sample Questions (Q27-Q32):

NEW QUESTION # 27
When creating a case in Splunk SOAR, which action should be taken to correlate various findings (risk notables) to ensure all are actioned?

Answer: C

Explanation:
The correct approach is to search Enterprise Security for all related events using the significant fields in the risk notable , review those results, and determine which findings should be incorporated into the active investigation. This makes D more complete than simply searching for duplicate values of risk_object or threat_object.
Risk-based detections often aggregate multiple behavioral observations around an entity such as a user, host, or other risk object. A single investigation may therefore involve several risk notables representing different behaviors, techniques, or stages of activity. Correlating only on risk_object can be too restrictive or produce misleading associations because the analyst also needs relevant event context and other identifying fields.
The workflow described by D supports the investigation objective: locate related security events , assess their contextual relationship, and deliberately merge the appropriate findings into the current case so that they are tracked and actioned together.
The uploaded study-guide extract does not contain this exact question, so this selection is based on the Enterprise Security/SOAR case-correlation workflow represented by the terminology in the question.
Study Guide topics: investigation management, risk notables, risk objects, case correlation, SOAR case handling, finding aggregation.


NEW QUESTION # 28
The following SPL is designed to report on a certain SOC metric. Which metric is the most likely topic for this report?
| tstats summariesonly=true earliest(_time) as _time
FROM datamodel=Incident_Management
BY " Notable_Events.Meta.rule_id "
| rename " Notable_Events.Meta.* " as " * "
| lookup update=true incident_updates_lookup rule_id OUTPUTNEW time
| search time=*
| stats earliest(_time) as create_time, min(time) as triage_time by rule_id
| eval diff=triage_time-create_time,
stat_type=if(
create_time < relative_time(now(), " -7d@d " ),
" past " ,
" current "
),
past=if(stat_type= " past " , 1, 0),
current=if(stat_type= " current " , 1, 0),
past_diff=if(stat_type= " past " , diff, 0),
current_diff=if(stat_type= " current " , diff, 0)
| stats sum(past) AS past,
sum(current) AS current,
sum(past_diff) AS past_diff,
sum(current_diff) AS current_diff
| eval past=round(past_diff/past/60),
current=round(current_diff/current/60)
| table past, current
| transpose

Answer: D

Explanation:
The SPL is measuring Mean Time to Triage because it calculates the elapsed time between creation of a notable event and the earliest recorded triage-related update.
The most important portion is:
| stats earliest(_time) as create_time, min(time) as triage_time by rule_id
| eval diff=triage_time-create_time
create_time represents when the notable was first generated, while triage_time is derived from the earliest applicable incident update. Subtracting the creation timestamp from the triage timestamp produces the amount of time analysts required to begin processing or triaging the finding.
The search then categorizes records into past and current periods, calculates average differences, and converts those values into minutes. This permits comparison of SOC triage performance across reporting periods.
Mean Time to Respond would require a timestamp representing a response action. Mean Time to Resolve requires closure or resolution information. Dwell Time measures how long adversary activity remains present or undetected and is therefore conceptually different from the notable-event lifecycle calculation shown.
Study Guide topics: SOC metrics, Mean Time to Triage, Incident Management data model, notable lifecycle, incident_updates_lookup, operational performance reporting.


NEW QUESTION # 29
When creating detections, which of the following sequences would result in the most performant SPL query?

Answer: D

Explanation:
The most performant SPL design is to define the base query, minimize the data set as early as possible, combine or summarize the remaining data, perform calculations, and format the final output last.
The critical optimization principle is early reduction of search cardinality. Filtering unnecessary events and fields before expensive aggregation or calculation means downstream commands operate on substantially less data. Once the search has constrained the relevant events, aggregation commands such as stats, tstats, or equivalent summarization reduce the event stream further. Calculations with eval or related functions should then operate on this smaller result set, and display-oriented operations such as table, rename, or final formatting should be performed only after analytical processing is complete.
Option A performs aggregation before minimizing the dataset, potentially requiring unnecessary events to participate in expensive operations. Options C and D perform formatting too early, which does not improve detection execution and can complicate or increase downstream processing.
The supplied study material also emphasizes efficient indexed/accelerated searching such as tstats instead of unnecessarily broad raw-event processing, reinforcing the same performance principle.
Study Guide topics: performant SPL, early filtering, aggregation, stats, tstats, search optimization, detection engineering efficiency.


NEW QUESTION # 30
What document can be helpful in understanding the prioritization of risk when comparing entities in an organization?

Answer: D

Explanation:
A Business Continuity or Disaster Recovery (BC/DR) plan identifies critical business processes, systems, and dependencies. It helps in understanding the prioritization of risk across entities in the organization, ensuring that the most business-critical assets are given higher priority in risk- based alerting and response.


NEW QUESTION # 31
An engineer has been asked to build a new dashboard after an increase in login failures across the organization ' s Microsoft Azure domain. They need to construct a search to only display failed logins for their Azure Active Directory users and create a visualization that will help quickly identify failed logins that originate outside of North America. Which search and visualization type combination will achieve this?

Answer: A

Explanation:
The required combination must satisfy two independent requirements : restrict the dataset to failed Azure Active Directory authentication activity and represent the origin of those events geographically. Option D provides that combination by selecting the failure-oriented Azure AD sign-in data and using geographic- coordinate information with a Cluster Map .
A geographic cluster visualization is appropriate when individual events contain point locations such as latitude and longitude. Multiple sign-in failures originating from the same geographic area can then be grouped visually, making concentrations outside the expected operating region-here, North America- immediately apparent. This is particularly useful for authentication monitoring because a raw table of IP addresses does not provide the same rapid geographic interpretation.
The search must also distinguish failures from successful authentication. Merely plotting all sign-ins would allow large quantities of legitimate activity to obscure the specific anomalous behavior being investigated.
Conversely, choosing an inappropriate geographic visualization for point-coordinate data would not provide the intended presentation.
Study Guide topics: Azure AD authentication data, failed-login filtering, geospatial enrichment, geographic dashboards, Cluster Map visualization, security analytics.


NEW QUESTION # 32
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