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The web-based SPLK-5002 practice exam is similar to the desktop-based software. You can take the web-based SPLK-5002 practice exam on any browser without needing to install separate software. In addition, all operating systems also support this web-based Splunk SPLK-5002 Practice Exam. Both Splunk Certified Cybersecurity Defense Engineer practice exams track your performance and help to overcome mistakes. Furthermore, you can customize your Splunk Certified Cybersecurity Defense Engineer practice exams according to your needs.
| Section | Weight | Objectives |
|---|---|---|
| Incident Response and Investigation | 20-25% | - Investigation best practices - Container and cloud environment investigation - Using correlation searches for investigation - Incident response workflows - Malware analysis and forensics - Timeline reconstruction |
| Splunk SOAR for Security Automation | 10-15% | - SOAR and ES integration - Incident response automation - Automation workflows and integrations - Creating and managing playbooks - SOAR platform fundamentals |
| Security Operations Center (SOC) Fundamentals | 10-15% | - SOC roles and responsibilities - Alert triage workflow - Security monitoring concepts - SIEM architecture in Splunk |
| Splunk Enterprise Security Administration | 10-15% | - ES upgrade and maintenance - User management and authentication - ES content management - Backup and recovery procedures - Performance tuning and optimization |
| Splunk Enterprise Security (ES) Configuration | 20-25% | - ES dashboards and navigation - Configuring data inputs and normalization - Managing asset and identity correlation - Incident review and management - ES deployment and architecture |
| Threat Detection and Hunting | 25-30% | - Search and detection frameworks - Creating and modifying detections - Notable events and risk analysis - Adversarial tactics, techniques, and procedures (ATT&CK) - Proactive threat hunting methodologies - Using Splunk ES threat intelligence |
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NEW QUESTION # 24
Consider the following series of events:
4:00 GMT Detection runs for interval 3:30-4:00
4:30 GMT Detection runs for interval 4:00-4:30
4:35 GMT Event 1 occurs on an endpoint
4:45 GMT Event 1 is indexed
5:00 GMT Detection runs for interval 4:30-5:00
5:05 GMT Event 1 finding is added to ES with timestamp 4:35
5:24 GMT Event 2 occurs on an endpoint
5:30 GMT Detection runs for interval 5:00-5:30
5:35 GMT Event 2 is indexed
6:00 GMT Detection runs for interval 5:30-6:00
What is the problem with the detection schedule chosen and how can it be solved?
Answer: A
Explanation:
The issue is ingestion latency . Event 2 occurs at 5:24 but is not indexed until 5:35. The 5:30 scheduled search covers event time 5:00-5:30, but the event is unavailable when that search executes. By the next execution at 6:00, the search window has advanced to 5:30-6:00, so an event timestamped 5:24 can fall outside the new window even though it is now searchable.
The solution is to increase the detection ' s lookback window sufficiently to accommodate expected indexing delay. Operationally, scheduled detections frequently use overlapping windows-for example, executing every 30 minutes while looking back farther than 30 minutes-to prevent late-arriving events from being missed. Appropriate deduplication or throttling can then address repeated matches caused by overlap.
Decreasing the time window would make the latency problem worse. The scenario also does not indicate that an excessively large window is causing duplicate alerts; it demonstrates a late-arriving event that misses the search interval.
Study Guide topics: detection scheduling, ingestion latency, event time versus index time, lookback windows, late-arriving telemetry.
NEW QUESTION # 25
An effective method for building automation workflows is to follow the OODA (Observe, Orient, Decide, Act) loop stages. When transitioning between the Decide and Act stages, what additional step should be included before automating the Act stage?
Answer: B
Explanation:
Before moving from Decide into an automated Act operation, the workflow should determine whether the targeted asset, identity, account, or service is subject to an exemption . This represents an essential safety control for security automation.
The OODA model progresses from observation of an event, through contextual orientation and decision- making, toward action. The final transition can produce consequential operations such as disabling an account, isolating an endpoint, blocking an address, revoking credentials, or modifying infrastructure.
Performing those actions without checking exemptions can create significant business impact.
For example, automatically disabling a service account responsible for production authentication could cause an outage even though the detection itself was technically accurate. Exemption logic allows security engineers to establish guardrails for privileged systems, break-glass accounts, infrastructure services, executive assets, approved scanners, and other protected entities.
Creating a playbook or response template defines how automation is implemented, but neither substitutes for the decision guardrail immediately before execution. Effective security automation therefore combines machine-speed response with explicit environmental constraints.
The OODA automation question is presented on page 2 of the supplied certification material.
Study Guide topics: OODA, SOAR automation, automation guardrails, asset/identity exemptions, response safety, automated remediation.
NEW QUESTION # 26
When developing security metrics, why would a Key Performance Indicator (KPI) that focuses on total perimeter firewall blocks be an ineffective metric?
Answer: B
Explanation:
A total count of blocked firewall connections describes an outcome generated by the control , rather than measuring how effectively the firewall itself performs against an established operational objective. It is therefore better characterized as a Key Result Indicator (KRI) in the terminology used by the course material.
For example, observing five million blocked connections does not demonstrate that a firewall is performing better than one that blocks one million. The total is heavily influenced by external conditions such as Internet scanning, bot activity, exposure of public services, and changing adversary traffic. The number can increase even when no improvement has occurred in firewall configuration, reliability, policy quality, or security effectiveness.
A meaningful KPI should connect directly to measurable performance-for example control availability, policy deployment accuracy, remediation time, rule-review compliance, or another defined operational objective. Firewall-block volume may remain useful as contextual or trend information, but treating it as a performance metric can produce misleading conclusions about security-program effectiveness.
The supplied study material contains this firewall KPI/KRI distinction.
Study Guide topics: security metrics, KPI versus KRI, control effectiveness, perimeter security, security- program reporting.
NEW QUESTION # 27
When setting Common Information Model (CIM) accelerations, which parameter should be defined to set how far back in time (specified as a relative time string) the Splunk platform creates its column stores?
Answer: B
Explanation:
The relevant parameter is the Backfill range . It determines how far backward Splunk should build accelerated summary information when establishing or updating the accelerated CIM data model. The value is expressed using a relative-time specification, allowing administrators to define how much historical data should be represented in the accelerated column-store structures.
Backfilling is important because enabling acceleration today does not automatically imply that unlimited historical data should be summarized. A cyber defense team may require enough historical coverage to support detection baselines, investigations, dashboards, and retrospective searches while also controlling computational and storage cost.
For example, increasing the backfill interval gives tstats-based searches access to a larger historical period of summarized data but requires Splunk to perform more summarization work. An interval that is too short may prevent analysts from efficiently reviewing older events through accelerated searches.
Max summarization search time controls summarization execution behavior rather than the historical horizon. "Accelerate until maximum time" is not the requested setting. Although summary-range terminology is conceptually related to accelerated coverage, the question specifically describes the relative-time parameter controlling how far backward the column stores are initially generated: Backfill range .
Study Guide topics: CIM acceleration, backfill range, accelerated summaries, column stores, tstats, data- model performance.
NEW QUESTION # 28
One of the goals of a detection engineer is to facilitate the triage process by providing the analyst as much context as possible. One way of accomplishing this is to provide context options through the use of which of the following settings?
Answer: B
Explanation:
A drill-down search is specifically designed to give analysts additional investigative context directly from a detection or finding. It allows an engineer to define a follow-on search that pivots from the current result into relevant supporting telemetry using fields such as user, src, dest, process name, host, or other contextual attributes.
For example, a finding concerning a suspicious user could provide a drill-down that automatically searches recent authentication activity for that same user. This reduces manual copying of values and significantly accelerates triage. The study material reinforces this workflow through its coverage of contextual dashboard values and drilldowns, where tokens can pass selected information into a new search.
A Correlation Search Name identifies the analytic but does not provide investigative context. A Risk Object Name identifies the entity receiving risk but does not define a contextual pivot. The Risk Analysis Adaptive Response Action creates or contributes risk events; it does not itself provide the analyst-facing navigation mechanism described.
Study Guide topics: drilldown searches, analyst triage, contextual fields, correlation searches, investigation workflow, detection actionability.
NEW QUESTION # 29
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