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| Certification Vendor: | Splunk |
|---|---|
| Exam Name: | Splunk Certified Cybersecurity Defense Engineer Exam |
| Exam Number: | SPLK-5002 |
| Exam Price: | $130 USD |
| Real Exam Qty: | 60 |
| Exam Format: | Multiple choice, Multiple response |
| Available Languages: | English |
| Related Certifications: | Splunk Certified Cybersecurity Defense Analyst Splunk Core Certified Power User |
| Exam Duration: | 75 minutes |
| Certificate Validity Period: | 3 years |
| Passing Score: | 700 / 1000 |
| Recommended Training: | Splunk Training & Certification |
| Exam Registration: | Pearson VUE Registration |
| Sample Questions: | Splunk SPLK-5002 Sample Questions |
| Exam Way: | Online proctored or onsite testing center via Pearson VUE |
| Pre Condition: | Recommended: Splunk Certified Cybersecurity Defense Analyst, or equivalent experience; Splunk Core Certified Power User knowledge |
| Official Syllabus URL: | https://www.splunk.com/en_us/training/certification/splk-5002-cybersecurity-defense-engineer.html |
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NEW QUESTION # 39
While working with the SOC analysts to review current contextualization processes, a request for automation has been raised by the SOC team. They are asking for a new automation that will check a potentially malicious URL against a remote URL filtering list. Which of the following options will work for them?
Answer: B
Explanation:
The SOC can implement this automation using either an Adaptive Response Action (triggered from a notable or event) or an Input Playbook (triggered when a URL is submitted for analysis).
Both approaches allow automated checks against a remote URL filtering list to enrich and contextualize findings.
NEW QUESTION # 40
When should a detection be reviewed or retuned after deployment?
Answer: B
Explanation:
Detection review and tuning should occur according to the organization ' s established detection lifecycle , rather than only in response to a single symptom or an arbitrary universal interval.
A mature detection lifecycle defines how security content progresses through design, development, testing, deployment, validation, operational monitoring, and subsequent tuning or maintenance. Post-deployment review should consider multiple signals: false-positive volume, unexpected false negatives, changes in telemetry, new application behavior, data-model changes, threat-intelligence developments, ATT & CK coverage requirements, analyst feedback, and changes in the environment.
Option A imposes a fixed 30-day interval without considering the organization ' s lifecycle or risk requirements. A monthly review may be appropriate in a particular program, but it is not universally required.
Option B is too narrow because a detection may require tuning even when false positives are low-for example, if required fields disappear or attacker behavior changes. Option D is similarly incomplete because an absence of findings does not automatically prove that the detection is ineffective.
The supplied material explicitly tests the Detection Development Lifecycle (DDLC) and emphasizes structured detection development rather than ad hoc changes.
Study Guide topics: DDLC, detection validation, post-deployment tuning, lifecycle governance, false- positive management, detection maintenance.
NEW QUESTION # 41
Risk scores are associated with how many levels of risk in Enterprise Security by default?
Answer: B
Explanation:
Splunk Enterprise Security uses five default qualitative risk levels: Info, Low, Medium, High, and Critical
. These levels provide an analyst-friendly interpretation of numeric risk scores and support prioritization within Risk-Based Alerting workflows.
Risk events are generated against a risk object , such as a user or system, and carry a numeric risk score. As multiple detections contribute evidence, risk can accumulate around that entity. Enterprise Security can then represent the resulting score through severity-style categories that allow analysts to distinguish routine informational activity from conditions requiring urgent investigation.
The five-tier model is useful because numeric scores alone do not immediately communicate operational priority. For example, an analyst viewing a Critical entity can rapidly prioritize it over an entity categorized as Low. Asset and identity context, Risk Factors, and detection-specific scoring can further influence the effective significance of accumulated risk.
"Unknown" is not part of the five default risk levels described by the answer set, while options A and B omit established categories.
The supplied study material directly covers Enterprise Security risk scoring, Risk Factors, risk objects, and contextual prioritization, although this exact stem is not present verbatim in the uploaded 60-question set.
Study Guide topics: Risk Framework, Risk-Based Alerting, risk scores, risk objects, Risk Factors, risk prioritization.
NEW QUESTION # 42
How can an engineer verify if results will return for a potential detection based on historical events within the organization?
Answer: D
Explanation:
A potential detection based on known historical organizational events should be validated by running its SPL across the specific historical interval in which those events occurred. In Splunk, this is accomplished by supplying appropriate earliest and latest time constraints .
This technique allows the engineer to answer a fundamental detection-development question: if the analytic had existed at the time of the known activity, would it have returned the expected events? The engineer can compare the resulting fields, entities, counts, and event relationships with the historical evidence and tune the detection accordingly.
Testing only against the present production interval can produce a false negative simply because the relevant behavior is no longer occurring. Attack Range and Atomic Red Team are valuable for controlled detection testing, but they do not answer the question posed here, which specifically concerns historical events within the organization .
Historical validation also helps establish an initial understanding of expected result volume and potential false- positive conditions before deployment. Once confirmed, additional controlled testing can complement the historical test.
Study Guide topics: detection validation, historical search, earliest, latest, detection testing, SPL time constraints.
NEW QUESTION # 43
For detections that leverage a CIM data model, which aspect of the configuration is responsible for determining which indexes are being searched?
Answer: D
Explanation:
A CIM data model commonly uses a constraint macro to control the indexes from which qualifying events are retrieved. Splunk CIM implementations use macros to abstract index-selection logic away from individual searches. This provides administrators with a centralized mechanism for defining which indexes contain data relevant to a particular CIM domain.
Conceptually, a data-model constraint can resolve to logic comparable to:
index=wineventlog OR index=endpoint
Rather than embedding those indexes separately into every detection, the associated macro can be maintained centrally. Consequently, changing the macro modifies the effective search scope for searches that depend on that CIM configuration.
The dataset hierarchy describes relationships between root datasets and child datasets but does not itself serve as the primary mechanism for enumerating indexes. Likewise, an " index list " is not the configuration construct being referenced by the CIM architecture in this question. Root dataset constraints define qualifying event characteristics, but the constraint macro is what enables deployment-specific index scoping.
This distinction matters operationally because a perfectly written detection can return incomplete results when its CIM constraint macro excludes an index containing relevant normalized data.
Study Guide topics: CIM data models, constraints, CIM macros, index scoping, data-model configuration, detection data availability.
NEW QUESTION # 44
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