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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Automation and Efficiency | 20% | - Response automation using SOAR playbooks - REST API usage and description - Automation and orchestration for standard operating procedures - Case management optimization - Integration and automation capability comparison between Enterprise Security and SOAR |
| Topic 2: Auditing and Reporting on Security Programs | 10% | - Security report creation and population - Dashboard building for program analytics - Security metrics development and optimization |
| Topic 3: Data Engineering | 10% | - Performant data indexing creation and maintenance - Data review and analysis - Data normalization methods and application |
| Topic 4: Building Effective Security Processes and Programs | 20% | - Documentation and standard operating procedures development - Threat intelligence research, integration and development - Risk and detection prioritization methodologies |
| Topic 5: Detection Engineering | 40% | - Risk-based modifiers and detections - Generating effective Notable Events and findings - Detection lifecycle management - Incorporating context into detections - Creation and tuning of detections and correlation searches |
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NEW QUESTION # 12
A new playbook needs to be developed for automated phishing analysis and response.
Configured in SOAR are integrations with Splunk Enterprise Security and actions from assets that pull in user-reported emails, perform automated threat analysis, add blocks on the proxy, and an EDR vendor to take various actions. Which would be the best workflow for the new playbook?
Answer: C
Explanation:
The best workflow for automated phishing analysis and response is:
1. Ingest the email from the mail vendor - acquire the reported email for analysis.
2. Detonate the email in the automated threat analysis system and collect verdict - determine if the email is malicious and extract indicators.
3. Search the mail system for all users that received the email - identify impacted users.
4. Block any malicious URLs and processes with the proxy and EDR solutions - take targeted remediation based on verified malicious indicators.
NEW QUESTION # 13
Which configurations are required for data normalization in Splunk?(Choosetwo)
Answer: D,E
Explanation:
Configurations Required for Data Normalization in Splunk
Data normalization ensures consistent field naming and event structuring, especially for Splunk Common Information Model (CIM) compliance.
#1. props.conf (A)
Defines how data is parsed and indexed.
Controls field extractions, event breaking, and timestamp recognition.
Example:
Assigns custom sourcetypes and defines regex-based field extraction.
#2. transforms.conf (B)
Used for data transformation, lookup table mapping, and field aliasing.
Example:
Normalizes firewall logs by renaming src_ip # src to align with CIM.
#Incorrect Answers:
C: savedsearches.conf # Defines scheduled searches, not data normalization.
D: authorize.conf # Manages user permissions, not data normalization.
E: eventtypes.conf # Groups events into categories but doesn't modify data structure.
#Additional Resources:
Splunk Data Normalization Guide
Understanding props.conf and transforms.conf
NEW QUESTION # 14
The SOC manager has a desire to measure mean time to acknowledge findings (notable events) in order to meet a desired service level objective. Which two fields can be used to measure this metric?
Answer: C
Explanation:
Mean Time to Acknowledge (MTTA) can be measured using the Status and Owner fields. Status indicates when a notable event moves from a new or unacknowledged state, and Owner identifies which analyst acknowledged the event, allowing calculation of the time taken to respond.
NEW QUESTION # 15
An engineer notices that a detection is creating multiple findings (notables) for the same potential incident. Which setting can be adjusted to reduce the number of generated findings (notables)?
Answer: C
Explanation:
Correlation search throttling is used to prevent multiple notable events from being created for the same condition within a defined time window. Adjusting throttling reduces duplicate findings and ensures only meaningful notables are generated.
NEW QUESTION # 16
What can an engineer use to capture contextual values from a dashboard and create a drilldown to link to a new search?
Answer: B
Explanation:
In Splunk dashboards, tokens are used to capture contextual values such as field selections or time ranges. These tokens can then be passed into a drilldown to dynamically link to and populate a new search with the selected context.
NEW QUESTION # 17
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