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| Section | Weight | Objectives |
|---|---|---|
| Detection Engineering | 40% | - Generating effective Notable Events and findings - Creation and tuning of detections and correlation searches - Incorporating context into detections - Detection lifecycle management - Risk-based modifiers and detections |
| Building Effective Security Processes and Programs | 20% | - Risk and detection prioritization methodologies - Threat intelligence research, integration and development - Documentation and standard operating procedures development |
| Auditing and Reporting on Security Programs | 10% | - Security report creation and population - Security metrics development and optimization - Dashboard building for program analytics |
| Data Engineering | 10% | - Data normalization methods and application - Performant data indexing creation and maintenance - Data review and analysis |
| Automation and Efficiency | 20% | - Automation and orchestration for standard operating procedures - Integration and automation capability comparison between Enterprise Security and SOAR - Case management optimization - REST API usage and description - Response automation using SOAR playbooks |
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NEW QUESTION # 17
Which methodology prioritizes risks by evaluating both their likelihood and impact?
Answer: A
Explanation:
Understanding Risk-Based Prioritization
Risk-based prioritization is a methodology that evaluatesboth the likelihood and impact of risksto determine which threats require immediate action.
#Why Risk-Based Prioritization?
Focuses onhigh-impact and high-likelihoodrisks first.
HelpsSOC teams manage alerts effectivelyand avoid alert fatigue.
Used inSIEM solutions (Splunk ES) and Risk-Based Alerting (RBA).
Example in Splunk Enterprise Security (ES):
Afailed login attemptfrom aninternal employeemight below risk(low impact, low likelihood).
Multiple failed loginsfrom aforeign countrywith a knownbad reputationcould behigh risk(high impact, high likelihood).
#Incorrect Answers:
A: Threat modeling# Identifies potential threats but doesn'tprioritize risks dynamically.
C: Incident lifecycle management# Focuses on handling security incidents, notrisk evaluation.
D: Statistical anomaly detection# Detects unusual activity but doesn'tprioritize based on impact.
#Additional Resources:
Splunk Risk-Based Alerting (RBA) Guide
NIST Risk Assessment Framework
NEW QUESTION # 18
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: C
Explanation:
The Summary range parameter in CIM accelerations defines how far back in time (using a relative time string) the Splunk platform creates its column stores. This determines the historical coverage of accelerated data available for searches and dashboards.
NEW QUESTION # 19
Which Splunk feature enables integration with third-party tools for automated response actions?
Answer: C
Explanation:
Security teams use Splunk Enterprise Security (ES) and Splunk SOAR to integrate with firewalls, endpoint security, and SIEM tools for automated threat response.
Workflow Actions (B) - Key Integration Feature
Allows analysts to trigger automated actions directly from Splunk searches and dashboards.
Can integrate with SOAR playbooks, ticketing systems (e.g., ServiceNow), or firewalls to take action.
Example:
Block an IP on a firewall from a Splunk dashboard.
Trigger a SOAR playbook for automated threat containment.
NEW QUESTION # 20
An engineer has discovered that an acquired company uses a duplicate IP address space. Which feature of the asset and identity framework could be turned on that would allow for the separation of company IP address ranges within a lookup?
Answer: A
Explanation:
Entity Zones in the Assets & Identities framework allow separation of entities (like IP address ranges) into distinct zones. This feature is useful when dealing with duplicate IP spaces from different companies, ensuring that events are correctly associated with the proper organizational context.
NEW QUESTION # 21
An engineer has been asked to build a new dashboard after an increase in login failures across the organization ' s Microsoft Azure domain. They need to construct a search to only display failed logins for their Azure Active Directory users and create a visualization that will help quickly identify failed logins that originate outside of North America. Which search and visualization type combination will achieve this?
Answer: A
Explanation:
The required combination must satisfy two independent requirements : restrict the dataset to failed Azure Active Directory authentication activity and represent the origin of those events geographically. Option D provides that combination by selecting the failure-oriented Azure AD sign-in data and using geographic- coordinate information with a Cluster Map .
A geographic cluster visualization is appropriate when individual events contain point locations such as latitude and longitude. Multiple sign-in failures originating from the same geographic area can then be grouped visually, making concentrations outside the expected operating region-here, North America- immediately apparent. This is particularly useful for authentication monitoring because a raw table of IP addresses does not provide the same rapid geographic interpretation.
The search must also distinguish failures from successful authentication. Merely plotting all sign-ins would allow large quantities of legitimate activity to obscure the specific anomalous behavior being investigated.
Conversely, choosing an inappropriate geographic visualization for point-coordinate data would not provide the intended presentation.
Study Guide topics: Azure AD authentication data, failed-login filtering, geospatial enrichment, geographic dashboards, Cluster Map visualization, security analytics.
NEW QUESTION # 22
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