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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Detection Engineering | 40% | - Creation and tuning of detections and correlation searches - Risk-based modifiers and detections - Generating effective Notable Events and findings - Incorporating context into detections - Detection lifecycle management |
| Topic 2: Building Effective Security Processes and Programs | 20% | - Threat intelligence research, integration and development - Risk and detection prioritization methodologies - Documentation and standard operating procedures development |
| Topic 3: Data Engineering | 10% | - Data review and analysis - Data normalization methods and application - Performant data indexing creation and maintenance |
| Topic 4: Automation and Efficiency | 20% | - Integration and automation capability comparison between Enterprise Security and SOAR - Response automation using SOAR playbooks - Case management optimization - REST API usage and description - Automation and orchestration for standard operating procedures |
| Topic 5: Auditing and Reporting on Security Programs | 10% | - Security report creation and population - Security metrics development and optimization - Dashboard building for program analytics |
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NEW QUESTION # 28
What is Enterprise Security ' s default way of determining the urgency of a finding (notable event)?
Answer: C
Explanation:
Enterprise Security determines notable-event urgency by combining the severity associated with the finding with the priority of the affected asset or identity . This produces a more context-aware prioritization model than simply treating every detection result with the same severity as equally important.
For example, identical suspicious behavior occurring on an ordinary workstation and on a critical production server may warrant different analyst priorities. Asset and identity enrichment supplies organizational context, while the detection contributes the severity of the observed security condition. Enterprise Security uses those dimensions to derive urgency so analysts can focus first on events with the greatest operational importance.
Option A incorrectly mixes detection execution frequency with risk scoring. Scheduling priority in B controls search execution considerations rather than analyst-facing notable urgency. Option C describes risk accumulation concepts but is not the default notable-event urgency calculation.
The supplied study material reinforces this architecture through its questions on asset priority , Assets & Identities enrichment, critical-asset prioritization, and Enterprise Security risk handling.
Study Guide topics: notable urgency, severity, asset priority, identity priority, Assets & Identities Framework, finding prioritization.
NEW QUESTION # 29
An engineer needs to create a new report capturing the vendors and products that detect a particular CVE in their environment. How can they ensure that the search associated with the report only includes accelerated data?
Answer: B
Explanation:
The appropriate approach is to query the Vulnerabilities data model using tstats , constrain the results to the particular CVE, and group or aggregate the results by vendor_product. This satisfies both requirements in the question: selecting vulnerability information associated with a CVE and ensuring that the search operates against accelerated data.
tstats is specifically designed to query indexed fields and accelerated data-model summaries efficiently. In conceptual form, the analytic follows this structure:
| tstats ... from datamodel=Vulnerabilities... where ...cve= < value > by ...vendor_product The exact field prefix depends on the dataset being addressed, but the architectural principle is the same: use the accelerated Vulnerabilities data model rather than scanning raw vulnerability events.
The Updates data model is not the appropriate semantic domain for reporting products associated with identified vulnerabilities. Likewise, simply searching for vendor_product without constraining the requested CVE would not satisfy the report requirement.
This question reinforces a recurring Cybersecurity Defense Engineer theme: choose the correct CIM domain and use accelerated-search mechanisms when operational reporting must scale efficiently.
Study Guide topics: Vulnerabilities data model, tstats, accelerated data, CVE reporting, vendor_product, search optimization.
NEW QUESTION # 30
How can an engineer verify if results will return for a potential detection based on historical events within the organization?
Answer: A
Explanation:
To verify if a potential detection will return results, the engineer should run the detection against production data in the same Splunk instance. This ensures the query is tested against actual historical events from the organization's environment, confirming whether it generates meaningful results.
NEW QUESTION # 31
Based on a recent red team exercise, an organization is highly concerned about pass-the-hash attacks, especially including tools like Empire. Which EventCode associated with PowerShell Script Block Logging would be used to detect this activity?
Answer: A
Explanation:
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.
NEW QUESTION # 32
Which Splunk feature helps in tracking and documenting threat trends over time?
Answer: C
Explanation:
Why Use Risk-Based Dashboards for Tracking Threat Trends?
Risk-based dashboards in Splunk Enterprise Security (ES) provide a structured way to track threats over time.
#How Risk-Based Dashboards Help:#Aggregate security events into risk scores # Helps prioritize high-risk activities.#Show historical trends of threat activity.#Correlate multiple risk factors across different security events.
#Example in Splunk ES:#Scenario: A SOC team tracks insider threat activity over 6 months.#The Risk-Based Dashboard shows:
Users with rising risk scores over time.
Patterns of malicious behavior (e.g., repeated failed logins + data exfiltration).
Correlation between different security alerts (e.g., phishing clicks # malware execution).
Why Not the Other Options?
#A. Event sampling - Helps with performance optimization, not threat trend tracking.#C. Summary indexing
- Stores precomputed data but is not designed for tracking risk trends.#D. Data model acceleration - Improves search speed, but doesn't track security trends.
References & Learning Resources
#Splunk ES Risk-Based Alerting Guide: https://docs.splunk.com/Documentation/ES#Tracking Security Trends Using Risk-Based Dashboards: https://splunkbase.splunk.com#How to Build Risk-Based Analytics in Splunk: https://www.splunk.com/en_us/blog/security
NEW QUESTION # 33
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