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| Section | Weight | Objectives |
|---|---|---|
| Security Operations Center (SOC) Fundamentals | 10-15% | - Alert triage workflow - SIEM architecture in Splunk - Security monitoring concepts - SOC roles and responsibilities |
| Splunk SOAR for Security Automation | 10-15% | - Automation workflows and integrations - SOAR and ES integration - Incident response automation - SOAR platform fundamentals - Creating and managing playbooks |
| Threat Detection and Hunting | 25-30% | - Using Splunk ES threat intelligence - Adversarial tactics, techniques, and procedures (ATT&CK) - Search and detection frameworks - Creating and modifying detections - Notable events and risk analysis - Proactive threat hunting methodologies |
| Incident Response and Investigation | 20-25% | - Timeline reconstruction - Malware analysis and forensics - Incident response workflows - Investigation best practices - Container and cloud environment investigation - Using correlation searches for investigation |
| Splunk Enterprise Security (ES) Configuration | 20-25% | - Managing asset and identity correlation - Configuring data inputs and normalization - ES deployment and architecture - Incident review and management - ES dashboards and navigation |
| Splunk Enterprise Security Administration | 10-15% | - ES upgrade and maintenance - Backup and recovery procedures - User management and authentication - Performance tuning and optimization - ES content management |
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NEW QUESTION # 15
An engineer receives a report that the "Traffic over time by action" dashboard is not populating. It has been confirmed that the relevant logs are being ingested properly and they are CIM compliant. What other configuration may be missing?
Answer: B
Explanation:
The "Traffic over time by action" dashboard relies on the Network Traffic data model. For it to populate correctly, the data model must be accelerated, ensuring that the dashboard can pull from the accelerated summaries instead of raw data.
NEW QUESTION # 16
What are the main steps of the Splunk data pipeline?(Choosethree)
Answer: B,C,E
Explanation:
The Splunk Data Pipeline consists of multiple stages that process incoming data from ingestion to visualization.
Main Steps of the Splunk Data Pipeline:
Input Phase (C)
Splunk collects raw data from logs, applications, network traffic, and endpoints.
Supports various data sources like syslog, APIs, cloud services, and agents (e.g., Universal Forwarders).
Parsing (D)
Splunk breaks incoming data into events and extracts metadata fields.
Removes duplicates, formats timestamps, and applies transformations.
Indexing (A)
Stores parsed events into indexes for efficient searching.
Supports data retention policies, compression, and search optimization.
NEW QUESTION # 17
Which of the following is a reason to utilize ES risk framework as a part of detection building?
Answer: A
Explanation:
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.
NEW QUESTION # 18
When building a metrics dashboard for the SOC manager, which metric would represent how long it takes to fully complete an investigation?
Answer: A
Explanation:
MTTR (Mean Time to Resolution/Recovery/Respond) measures how long it takes to fully complete an investigation or resolve an incident. This is the key metric for tracking investigation completion time in SOC performance dashboards.
NEW QUESTION # 19
A company's Splunk setup processes logs from multiple sources with inconsistent field naming conventions.
Howshould the engineer ensure uniformity across data for better analysis?
Answer: A
Explanation:
Why Use CIM for Field Normalization?
When processing logs from multiple sources with inconsistent field names, the best way to ensure uniformity is to use Splunk's Common Information Model (CIM).
#Key Benefits of CIM for Normalization:
Ensures that different field names (e.g., src_ip, ip_src, source_address) are mapped to a common schema.
Allows security teams to run a single search query across multiple sources without manual mapping.
Enables correlation searches in Splunk Enterprise Security (ES) for better threat detection.
Example Scenario in a SOC:
#Problem: The SOC team needs to correlate firewall logs, cloud logs, and endpoint logs for failed logins.
#Without CIM: Each log source uses a different field name for failed logins, requiring multiple search queries.
#With CIM: All failed login events map to the same standardized field (e.g., action="failure"), allowing one unified search query.
Why Not the Other Options?
#A. Create field extraction rules at search time - Helps with parsing data but doesn't standardize field names across sources.#B. Use data model acceleration for real-time searches - Accelerates searches but doesn't fix inconsistent field naming.#D. Configure index-time data transformations - Changes fields at indexing but is less flexible than CIM's search-time normalization.
References & Learning Resources
#Splunk CIM for Normalization: https://docs.splunk.com/Documentation/CIM#Splunk ES CIM Field Mappings: https://splunkbase.splunk.com/app/263#Best Practices for Log Normalization: https://www.splunk.
com/en_us/blog/tips-and-tricks
NEW QUESTION # 20
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