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| Certification Vendor: | Splunk |
|---|---|
| Exam Name: | Splunk Certified Cybersecurity Defense Engineer |
| Exam Number: | SPLK-5002 |
| Passing Score: | 65-70% (variable) |
| Real Exam Qty: | 82 |
| Exam Price: | $200 USD |
| Exam Duration: | 120 minutes |
| Related Certifications: | Splunk Enterprise Security Certified Admin Splunk Core Certified User Splunk SOAR Certified Automation Developer |
| Available Languages: | English |
| Exam Format: | Multiple select, Hands-on lab simulation, Multiple choice |
| Certificate Validity Period: | 3 years |
| 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 |
Die Fragenkataloge zur Splunk SPLK-5002 Prüfung von Fast2test sind die besten im Vergleich zu den anderen Materialien. Wenn Sie Fragenkataloge suchen, wählen Sie doch die Fragenkataloge zur Splunk SPLK-5002 Prüfung von Fast2test. Und Sie würden viel davonprofitieren. Sonst würden Sie bereuen.
| Thema | Einzelheiten |
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46. Frage
A SOC's Incident Response Standard Operating Procedure (SOP) calls for any phishing emails containing files to be detonated in Splunk Attack Analyzer for evaluation. Which of the following can an engineer implement to gain efficiency through automation?
Antwort: B
Begründung:
The most efficient approach is to use a SOAR playbook to automatically handle the Splunk Attack Analyzer submission and data collection steps, then present the results to the assigned analyst.
This reduces manual effort, accelerates phishing investigation workflows, and aligns directly with the SOC's SOP.
47. Frage
What provides consistency for data mapping applied to data model and saved search exports between Splunk Enterprise Security and Splunk SOAR?
Antwort: A
Begründung:
Global field mappings provide a consistent mapping layer when data is exported from Enterprise Security data models or saved searches into Splunk SOAR. The objective is to ensure that semantically equivalent values are represented consistently regardless of which detection, search, or data model produced the source event.
This becomes important when SOAR receives security events from multiple searches. One source might expose an endpoint as dest, another as host, and another might use a product-specific field. Consistent global mapping allows downstream SOAR workflows to operate on standardized field semantics instead of requiring every playbook to understand every upstream schema variation.
A field alias is a Splunk knowledge object that can make one indexed or extracted field available under another name during search processing, but that is not the cross-export mapping mechanism requested here.
"Field labels" likewise do not establish the global ES-to-SOAR translation layer. The important clue is the requirement for consistency across both data-model and saved-search exports , which implies a centrally applied mapping configuration.
This design reduces duplicated transformation logic and improves the portability and reliability of automated response workflows.
Study Guide topics: Global field mappings, Enterprise Security-to-SOAR integration, data normalization, event export, automation data consistency.
48. Frage
Which of the following is not a type of metadata that can be returned by the metadata command?
Antwort: A
Begründung:
Assets are not one of the metadata types returned by Splunk ' s metadata command. The command supports indexed metadata categories including hosts, sources, and sourcetypes , making option C the exception.
Typical examples include:
| metadata type=hosts
| metadata type=sources
| metadata type=sourcetypes
The command provides summarized information about indexed data and can expose attributes such as event counts and first or last observed times without requiring analysts to perform a broad raw-event search. This makes it useful when validating data availability, determining whether a particular host or sourcetype is still reporting, and performing general data-source inventory work.
An asset , by contrast, is an enriched security entity within the broader Enterprise Security context. Asset information can be maintained through Assets and Identities mechanisms and associated lookup structures, but it is not a metadata command type.
The distinction is important in security data engineering. Indexed metadata describes how Splunk stores and categorizes incoming event data; asset context describes organizational entities and their attributes. Confusing those layers can result in inefficient searches or incorrect assumptions about where contextual information resides.
Study Guide topics: metadata, hosts, sources, sourcetypes, indexed metadata, data-source inventory, Assets and Identities.
49. Frage
Which action improves the effectiveness of notable events in Enterprise Security?
Antwort: B
Begründung:
Notable events in Splunk Enterprise Security (ES) are triggered by correlation searches, which generate alerts when suspicious activity is detected. However, if too many false positives occur, analysts waste time investigating non-issues, reducing SOC efficiency.
How to Improve Notable Events Effectiveness:
Apply suppression rules to filter out known false positives and reduce alert fatigue.
Refine correlation searches by adjusting thresholds and tuning event detection logic.
Leverage risk-based alerting (RBA) to prioritize high-risk events.
Use adaptive response actions to enrich events dynamically.
By suppressing false positives, SOC analysts focus on real threats, making notable events more actionable. Thus, the correct answer is A. Applying suppression rules for false positives.
50. Frage
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
Antwort: D
Begründung:
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.
51. Frage
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