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| Section | Weight | Objectives |
|---|---|---|
| Detection Engineering | 40% | - Incorporating context into detections - Generating effective Notable Events and findings - Creation and tuning of detections and correlation searches - Detection lifecycle management - Risk-based modifiers and detections |
| Automation and Efficiency | 20% | - REST API usage and description - Case management optimization - Integration and automation capability comparison between Enterprise Security and SOAR - Response automation using SOAR playbooks - Automation and orchestration for standard operating procedures |
| Building Effective Security Processes and Programs | 20% | - Risk and detection prioritization methodologies - Documentation and standard operating procedures development - Threat intelligence research, integration and 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 review and analysis - Performant data indexing creation and maintenance - Data normalization methods and application |
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NEW QUESTION # 94
An engineer wants to track and report on all authentication to corporate assets, and wants to prioritize critical assets without significantly increasing the number of findings (notable events) generated. What process could be used to accomplish this goal?
Answer: D
Explanation:
By adding all access attempts to the Risk Index and then increasing the Criticality of critical assets, the engineer ensures all authentication activity is tracked while prioritizing findings involving high-value assets. This approach leverages risk-based alerting without flooding the SOC with unnecessary notable events.
NEW QUESTION # 95
For detections that leverage a CIM data model, which aspect of the configuration is responsible for determining which indexes are being searched?
Answer: A
Explanation:
A CIM data model commonly uses a constraint macro to control the indexes from which qualifying events are retrieved. Splunk CIM implementations use macros to abstract index-selection logic away from individual searches. This provides administrators with a centralized mechanism for defining which indexes contain data relevant to a particular CIM domain.
Conceptually, a data-model constraint can resolve to logic comparable to:
index=wineventlog OR index=endpoint
Rather than embedding those indexes separately into every detection, the associated macro can be maintained centrally. Consequently, changing the macro modifies the effective search scope for searches that depend on that CIM configuration.
The dataset hierarchy describes relationships between root datasets and child datasets but does not itself serve as the primary mechanism for enumerating indexes. Likewise, an " index list " is not the configuration construct being referenced by the CIM architecture in this question. Root dataset constraints define qualifying event characteristics, but the constraint macro is what enables deployment-specific index scoping.
This distinction matters operationally because a perfectly written detection can return incomplete results when its CIM constraint macro excludes an index containing relevant normalized data.
Study Guide topics: CIM data models, constraints, CIM macros, index scoping, data-model configuration, detection data availability.
NEW QUESTION # 96
Which features are crucial for validating integrations in Splunk SOAR? (Choose three)
Answer: B,C,E
Explanation:
Validating Integrations in Splunk SOAR
Splunk SOAR (Security Orchestration, Automation, and Response) integrates with various security tools to automate security workflows. Proper validation of integrations ensures that playbooks, threat intelligence feeds, and incident response actions function as expected.
#Key Features for Validating Integrations
1##Testing API Connectivity (A)
Ensures Splunk SOAR can communicate with external security tools (firewalls, EDR, SIEM, etc.).
Uses API testing tools like Postman or Splunk SOAR's built-in Test Connectivity feature.
2##Verifying Authentication Methods (C)
Confirms that integrations use the correct authentication type (OAuth, API Key, Username/Password, etc.).
Prevents failed automations due to expired or incorrect credentials.
3##Evaluating Automated Action Performance (D)
Monitors how well automated security actions (e.g., blocking IPs, isolating endpoints) perform.
Helps optimize playbook execution time and response accuracy.
#Incorrect Answers & Explanations
B: Monitoring data ingestion rates # Data ingestion is crucial for Splunk Enterprise, but not a core integration validation step for SOAR.
E: Increasing indexer capacity # This is related to Splunk Enterprise data indexing, not Splunk SOAR integration validation.
#Additional Resources:
Splunk SOAR Administration Guide
Splunk SOAR Playbook Validation
Splunk SOAR API Integrations
NEW QUESTION # 97
Which tool can help provide a baseline of the data sources in a given Splunk environment?
Answer: B
Explanation:
The Enterprise Security Data Library (ESDL) provides a baseline of the data sources available in a Splunk environment. It helps identify which data sources are present, how they map to security use cases, and whether they align with Enterprise Security requirements.
NEW QUESTION # 98
Consider the following series of events:
4:00 GMT Detection runs for interval 3:30-4:00
4:30 GMT Detection runs for interval 4:00-4:30
4:35 GMT Event 1 occurs on an endpoint
4:45 GMT Event 1 is indexed
5:00 GMT Detection runs for interval 4:30-5:00
5:05 GMT Event 1 finding is added to ES with timestamp 4:35
5:24 GMT Event 2 occurs on an endpoint
5:30 GMT Detection runs for interval 5:00-5:30
5:35 GMT Event 2 is indexed
6:00 GMT Detection runs for interval 5:30-6:00
What is the problem with the detection schedule chosen and how can it be solved?
Answer: B
Explanation:
In this scenario, events are indexed after the scheduled detection window has already executed, meaning detections miss relevant events. This happens due to log ingestion delay. The solution is to increase the detection time window (or use a delay offset) so that detections account for delayed logs, ensuring events like Event 1 and Event 2 are included in the proper detection run.
NEW QUESTION # 99
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