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| Section | Weight | Objectives |
|---|---|---|
| Automation and Efficiency | 20% | - Automation and orchestration for standard operating procedures - Response automation using SOAR playbooks - REST API usage and description - Integration and automation capability comparison between Enterprise Security and SOAR - Case management optimization |
| Building Effective Security Processes and Programs | 20% | - Documentation and standard operating procedures development - Threat intelligence research, integration and development - Risk and detection prioritization methodologies |
| 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 review and analysis - Performant data indexing creation and maintenance - Data normalization methods and application |
| Detection Engineering | 40% | - Detection lifecycle management - Risk-based modifiers and detections - Generating effective Notable Events and findings - Creation and tuning of detections and correlation searches - Incorporating context into detections |
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NEW QUESTION # 26
During a ransomware attack, an adversary might add a default user and password in registry, modify the wallpaper, and create bulk ransomware notes across multiple machines. What is Splunk's method for grouping these types of detections together?
Answer: B
Explanation:
Splunk uses Analytic Stories to group related detections together that align with a specific threat scenario, such as ransomware. These stories provide a collection of correlation searches, baselines, and contextual guidance to detect, investigate, and respond to adversary behaviors.
NEW QUESTION # 27
Which of the following traces specific stages of an attack lifecycle?
Answer: B
Explanation:
The Lockheed Martin Cyber Kill Chain traces specific stages of an attack lifecycle, from reconnaissance through actions on objectives. It is widely used to understand, detect, and disrupt adversary behavior at each stage of an intrusion.
NEW QUESTION # 28
What are the benefits of incorporating asset and identity information into correlation searches?(Choosetwo)
Answer: B,C
Explanation:
Why is Asset and Identity Information Important in Correlation Searches?
Correlation searches in Splunk Enterprise Security (ES) analyze security events to detect anomalies, threats, and suspicious behaviors. Adding asset and identity information significantly improves security detection and response by:
1##Enhancing the Context of Detections - (Answer A)
Helps analysts understand the impact of an event by associating security alerts with specific assets and users.
Example: If a failed login attempt happens on a critical server, it's more serious than one on a guest user account.
2##Prioritizing Incidents Based on Asset Value - (Answer C)
High-value assets (CEO's laptop, production databases) need higher priority investigations.
Example: If malware is detected on a critical finance server, the SOC team prioritizes it over a low-impact system.
Why Not the Other Options?
#B. Reducing the volume of raw data indexed - Asset and identity enrichment adds more metadata;it doesn't reduce indexed data.#D. Accelerating data ingestion rates - Adding asset identity doesn't speed up ingestion; it actually introduces more processing.
References & Learning Resources
#Splunk ES Asset & Identity Framework: https://docs.splunk.com/Documentation/ES/latest/Admin
/Assetsandidentitymanagement#Correlation Searches in Splunk ES: https://docs.splunk.com/Documentation
/ES/latest/Admin/Correlationsearches
NEW QUESTION # 29
What is the best method to operationalize the results of a threat hunt for daily use by SOC analysts?
Answer: D
Explanation:
The most effective way to operationalize a validated threat-hunting result is to create detections based on the documented findings . Threat hunting is exploratory: analysts search historical or current telemetry for behaviors that may not yet have reliable automated coverage. Once a repeatable malicious or suspicious pattern is identified and validated, detection engineering converts that knowledge into an analytic that operates continuously.
This establishes a mature feedback loop:
Threat hunt # validate behavior # document evidence # engineer detection # test # deploy # monitor and tune.
Reports and communication remain valuable, particularly for management, architecture, and lessons learned, but they do not provide continuous identification of recurrence. A detection allows the SOC to identify the behavior automatically during routine operations and present relevant results to analysts.
The detection should preserve the context learned during the hunt, including useful fields, appropriate time windows, entity information, exclusions, and potentially ATT & CK annotations or risk information.
Engineers should also assess whether the hunting evidence supports a sufficiently reliable analytic to avoid excessive false positives.
Study Guide topics: threat hunting, detection operationalization, detection lifecycle, hunt-to-detection workflow, continuous security monitoring.
NEW QUESTION # 30
For detections that leverage a CIM data model, which aspect of the configuration is responsible for determining which indexes are being searched?
Answer: C
Explanation:
For detections using a CIM data model, the data model's constraint macro defines which indexes are searched. This macro ensures that only relevant indexed data is pulled into the data model, controlling the search scope for detections.
NEW QUESTION # 31
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