Free Splunk SPLK-5002 Practice Exam with Questions & Answers | Set: 3

EventCode 4104 represents PowerShell Script Block Logging , making it the relevant telemetry for inspecting PowerShell code executed in an environment. The question specifically ties the detection requirement to PowerShell Script Block Logging and therefore directly points to 4104.

Script Block Logging provides visibility into PowerShell script content after PowerShell has processed the command or script block. From a detection-engineering perspective, this can expose suspicious commands, modules, functions, credential-access behavior, encoded or obfuscated PowerShell content, and scripts associated with offensive frameworks. An engineer can search this telemetry for characteristics associated with the behavior discovered during the red-team exercise and subsequently operationalize reliable indicators into a detection.

EventCode 4624 , by comparison, represents a successful Windows logon and can be useful in authentication and lateral-movement analysis, but it is not the PowerShell Script Block Logging event explicitly requested. The remaining distractor event codes do not represent the logging mechanism identified in the question.

The distinction matters because endpoint detections depend on selecting telemetry that observes the relevant layer of attacker behavior. Here, PowerShell execution content—not merely authentication—is the required visibility source.

Study Guide topics: PowerShell logging, EventCode 4104, endpoint telemetry, behavioral detection, red-team findings, Windows security monitoring.

The appropriate HTTP method is POST . A contextualization playbook submitting a URL to a sandbox is creating a new analysis request and transmitting data to an application endpoint for processing. REST APIs conventionally use POST for this type of operation.

A typical workflow sends an HTTP request containing the URL or artifact information in the request body. The sandbox accepts the submission, generates an analysis job or task identifier, processes the object, and later returns a verdict or detailed analysis. SOAR can subsequently poll for completion or retrieve the resulting disposition.

GET is conventionally used to retrieve an existing resource and normally should not be used to create an analysis job. PUT is generally associated with creating or replacing a resource at a known URI and is not the standard semantic operation for submitting a new sandbox-analysis request. STOR is associated with FTP rather than HTTP and therefore is not an HTTP method applicable to this workflow.

Understanding these API semantics is essential when troubleshooting SOAR integrations because incorrect HTTP methods can produce errors such as 405 Method Not Allowed or other integration failures.

Study Guide topics: SOAR contextualization, REST APIs, HTTP POST, sandbox integration, automated enrichment, artifact analysis.

The preferred approach is tstats , because tstats operates against indexed metadata rather than requiring Splunk to retrieve and process every matching raw event. A conceptual implementation is:

| tstats values(sourcetype) WHERE index=* BY index

This produces one result per visible index and lists the sourcetypes associated with each index. It is significantly more efficient than executing:

index=* | stats ...

because the latter can require broad raw-event retrieval across every searchable index. In a large security deployment containing billions of events, that difference is operationally significant.

The tstats command is particularly useful when the required information can be derived from indexed fields or accelerated data structures. Here, both index and sourcetype information can be obtained without inspecting full _raw event payloads.

The phrase " most efficient " is therefore central to the question. Several approaches may theoretically obtain similar information, but a broad raw-event search is unnecessarily expensive when indexed metadata already contains what is required.

This question appears on page 2 of the supplied material.

Study Guide topics: SPL efficiency, tstats, indexed metadata, index discovery, sourcetype inventory, search optimization.

Triage generally provides the lowest-friction opportunity for replacing manual SOC work with automation because much of triage consists of deterministic, repetitive information-gathering and enrichment tasks.

A SOAR playbook can automatically retrieve reputation information, collect endpoint details, query identity context, check threat intelligence, detonate suspicious files or URLs, deduplicate findings, and assemble evidence before an analyst reviews the incident. These activities are generally reversible and low impact compared with active response operations. The supplied material demonstrates this principle through automation scenarios where SOAR handles repetitive Attack Analyzer submission and data-collection steps before presenting the information to an analyst.

Containment introduces greater friction because actions such as disabling an account or isolating a production endpoint can disrupt business operations. Remediation may involve deleting files, changing configurations, resetting credentials, or restoring systems and therefore generally requires stronger controls. Rendering a verdict frequently requires contextual human judgment, particularly where evidence is ambiguous.

Automating triage first therefore provides high efficiency gains while maintaining relatively low operational risk.

Study Guide topics: SOAR automation, triage, enrichment, incident-response lifecycle, automation guardrails, containment, remediation.

The MITRE ATT & CK matrix ' s industry heatmap in Splunk Security Essentials is designed to help security teams understand which ATT & CK techniques are particularly relevant to specific industries. For a financial organization, this capability provides an industry-oriented view that assists engineers in identifying adversary behaviors and prioritizing detection coverage accordingly.

MITRE ATT & CK organizes adversarial behavior into tactics and techniques, while Splunk Security Essentials provides security-content and coverage context that can be related to those behaviors. An industry heatmap enables engineers to move beyond treating every ATT & CK technique as equally significant and instead examine techniques associated with threats relevant to their business sector.

This can support detection-program planning by exposing coverage gaps, prioritizing new analytics, and aligning threat hunting with realistic adversarial behavior. The result is a threat-informed detection strategy grounded in likely TTPs rather than an undifferentiated list of techniques.

The Mission Control and Incident Review distractors describe interfaces used for handling findings rather than the industry-focused ATT & CK analysis capability. Splunk Threat Intelligence Management manages threat intelligence but does not represent the specific industry heatmap requested.

Study Guide topics: Splunk Security Essentials; MITRE ATT & CK; industry heatmaps; TTPs; detection coverage; threat-informed defense; security-program prioritization.

Validating a Splunk SOAR integration requires confirming that the integration can communicate, authenticate, and successfully execute its intended actions .

Testing API connectivity verifies that SOAR can reach the target service, resolve the endpoint, negotiate the required network/TLS connection, and receive valid API responses. Verifying authentication methods confirms that the asset is configured with the authentication mechanism expected by the API, such as username/password, API token, OAuth, or another supported credential model. The supplied study material reinforces this troubleshooting domain through questions involving HTTP response codes, asset permissions, REST communication, and SOAR assets.

Evaluating automated action performance confirms that configured actions actually execute correctly—for example, querying an indicator, isolating a device, blocking a hash, or submitting an artifact to an analysis service.

Monitoring ingestion rates is a Splunk data-pipeline concern rather than a core SOAR integration-validation requirement. Increasing indexer capacity likewise addresses Splunk platform scaling rather than validating an external automation integration.

The exact choose-three wording is not present in the supplied PDF; these selections synthesize the SOAR integration concepts that the guide tests.

Study Guide topics: SOAR assets, REST APIs, authentication, authorization, HTTP status codes, automated actions, integration troubleshooting.

The correct scheduling mode is Continuous . Continuous scheduling is designed for cases where the engineer wants the scheduled search to preserve coverage of every intended time interval, even if a particular execution cannot start exactly at its configured time.

With continuous scheduling, Splunk can run a delayed search later while still evaluating the originally intended time range. This helps prevent backfill gaps , where an interval would otherwise never be searched because the scheduler was busy or the search could not execute on time.

This differs from real-time-oriented scheduling behavior, which prioritizes execution close to the current scheduled time and may skip older scheduled instances when resources are constrained. For security detections, that can be undesirable if the requirement is complete historical coverage rather than lowest possible latency.

This same principle aligns with the supplied study material ' s treatment of detection scheduling and late-arriving data: engineers must design search windows and scheduling behavior so events are not missed simply because indexing or execution occurs later than expected.

Study Guide topics: correlation-search scheduling, continuous scheduling, backfill, scheduler delays, detection coverage, late-arriving data.

The Enterprise Security Risk Framework enables detection engineers to express suspicious observations as risk against meaningful entities—typically users, systems, or other risk objects—and then prioritize those observations according to their security significance. Option C therefore represents the principal value being tested: prioritizing findings based on potential business impact .

In a Risk-Based Alerting design, an individual behavior does not necessarily need to generate an analyst-facing finding immediately. Instead, detections can generate risk events containing fields such as the risk object, risk object type, risk score, and contextual annotations. Multiple risk events can accumulate until correlation logic determines that the combined evidence warrants escalation. Risk Factors and asset/identity context can further modify significance when an affected entity is particularly sensitive or critical.

Risk processing is therefore fundamentally about contextual prioritization and evidence aggregation , not search-performance acceleration. It does not inherently create a threat-intelligence feedback loop, nor is its primary purpose to simplify SOAR execution. Those capabilities can interact with risk-based detections but are separate functions.

Study Guide topics: Enterprise Security Risk Framework; Risk-Based Alerting; risk objects; risk scores; business impact; security finding prioritization.

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.

The key-value pairs appearing after the ? character in a URL are parameters , more specifically query parameters . The example structure shown in the supplied material follows the standard pattern:

?type=hash & data= < value >

Here, type=hash and data= < value > are query parameters. The question mark marks the beginning of the URL ' s query component, while an ampersand ( & ) separates multiple parameter pairs. Each parameter typically consists of a key followed by = and its corresponding value.

This must be distinguished from an HTTP payload , which is normally transmitted in the request body, particularly with operations such as POST or PUT. Headers are separate HTTP metadata elements containing information such as authorization credentials, content type, accepted response formats, and user-agent information. “KV Elements” is not the HTTP/REST terminology for the URL query component.

Understanding this distinction is important when configuring SOAR integrations because an API may require values in different locations. Supplying a required query parameter in the request body—or vice versa—can result in validation failures even when the correct data is present.

Study Guide topics: REST APIs; query parameters; HTTP requests; SOAR integrations; URL structure; API troubleshooting.