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| Section | Weight | Objectives |
|---|---|---|
| Data Engineering | 10% | - Data ingestion and onboarding - Indexing performance and management - Data parsing, normalization, and CIM alignment |
| Detection Engineering | 40% | - Notable event generation and lifecycle management - Detection enrichment with context and risk-based alerting - Creation and tuning of detections (Correlation Searches) |
| Security Automation (SOAR) | 30% | - Playbook design and automation workflows - Incident response automation and orchestration |
| Security Operations and Program Development | 20% | - SOC process design and operational workflows - Threat intelligence integration |
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NEW QUESTION # 18
In order to perform a complete data assessment, an engineer's role within Splunk must have which of the following?
Answer: C
Explanation:
To perform a complete data assessment in Splunk, an engineer must have access to applicable indexes. Without index access, the engineer cannot review ingested data, validate mappings, or evaluate coverage for detections and reporting.
NEW QUESTION # 19
An engineer observes a high volume of false positives generated by a correlation search.
Whatsteps should they take to reduce noise without missing critical detections?
Answer: D
Explanation:
How to Reduce False Positives in Correlation Searches?
High false positives can overwhelm SOC teams, causing alert fatigue and missed real threats. The best solution is to fine-tune suppression rules and refine thresholds.
#How Suppression Rules & Threshold Tuning Help:#Suppression Rules: Prevent repeated false positives from low-risk recurring events (e.g., normal system scans).#Threshold Refinement: Adjust sensitivity to focus on true threats (e.g., changing a login failure alert from 3 to 10 failed attempts).
#Example in Splunk ES:#Scenario: A correlation search generates too many alerts for failed logins.#Fix: SOC analysts refine detection thresholds:
Suppress alerts if failed logins occur within a short timeframe but are followed by a successful login.
Only trigger an alert if failed logins exceed 10 attempts within 5 minutes.
Why Not the Other Options?
#A. Increase the frequency of the correlation search - Increases search load without reducing false positives.
#C. Disable the correlation search temporarily - Leads to blind spots in detection.#D. Limit the search to a single index - May exclude critical security logs from detection.
References & Learning Resources
#Splunk ES Correlation Search Optimization Guide: https://docs.splunk.com/Documentation/ES#Reducing False Positives in SOC Workflows: https://splunkbase.splunk.com#Fine-Tuning Security Alerts in Splunk:
https://www.splunk.com/en_us/blog/security
NEW QUESTION # 20
What is the main benefit of automating case management workflows in Splunk?
Answer: C
Explanation:
Automating case management workflows in Splunk streamlines incident response and reduces manual overhead, allowing analysts to focus on higher-value tasks.
Main Benefits of Automating Case Management:
Reduces Response Times (C)
Automatically assigns cases to analysts based on predefined rules.
Triggers playbooks and workflows in Splunk SOAR to handle common incidents.
Improves Analyst Productivity (C)
Reduces time spent on manual case creation and updates.
Provides integrated case tracking across Splunk and ITSM tools (e.g., ServiceNow, Jira).
NEW QUESTION # 21
Which Splunk feature makes SPL searches shorter and reusable by inserting it into search strings?
Answer: A
Explanation:
Splunk macros make SPL shorter and reusable by encapsulating frequently used search fragments and allowing them to be inserted into other searches. A macro can contain filters, field calculations, constraints, or larger portions of SPL that would otherwise need to be duplicated across multiple detections, reports, and dashboards.
Macros are typically invoked using backticks, conceptually:
search index=security | security_filter``
The macro is resolved at search execution time into the SPL definition associated with it. Parameterized macros can also accept arguments, allowing common search logic to be reused with different values.
This is particularly important for detection engineering because centralized reusable logic reduces duplication.
If ten correlation searches rely on the same constraint, a macro allows the engineer to maintain that logic centrally rather than changing all ten searches independently.
Although macros are themselves Splunk knowledge objects, Knowledge objects is too broad for the question.
Lookups add external or maintained contextual data to events, while commands such as stats, eval, or tstats perform specific SPL operations rather than serving as reusable search substitutions.
Study Guide topics: SPL macros, reusable searches, search maintainability, knowledge objects, detection engineering efficiency.
NEW QUESTION # 22
Based on this example image, if it is detected that a member has been added to a security- enabled local group, how many risk events will be created?
Answer: A
Explanation:
In the example, there are two risk modifiers configured: one for the system (src) and one for the user. Each modifier creates a separate risk event with a score of 10. Therefore, the detection will generate 2 risk events in total.
NEW QUESTION # 23
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