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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Automation and Efficiency | 20% | - Case management optimization - Response automation using SOAR playbooks - Automation and orchestration for standard operating procedures - REST API usage and description - Integration and automation capability comparison between Enterprise Security and SOAR |
| Topic 2: Auditing and Reporting on Security Programs | 10% | - Dashboard building for program analytics - Security metrics development and optimization - Security report creation and population |
| Topic 3: Building Effective Security Processes and Programs | 20% | - Threat intelligence research, integration and development - Risk and detection prioritization methodologies - Documentation and standard operating procedures development |
| Topic 4: Data Engineering | 10% | - Data review and analysis - Performant data indexing creation and maintenance - Data normalization methods and application |
| Topic 5: Detection Engineering | 40% | - Risk-based modifiers and detections - Creation and tuning of detections and correlation searches - Detection lifecycle management - Incorporating context into detections - Generating effective Notable Events and findings |
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NEW QUESTION # 33
During an incident, a correlation search generates several notable events related to failed logins. The engineer notices the events are from test accounts.
Whatshould be done to address this?
Answer: D
Explanation:
When a correlation search in Splunk Enterprise Security (ES) generates excessive notable events due to test accounts, the best approach is to filter out test accounts while keeping legitimate detections active.
#1. Apply Filtering to Exclude Test Accounts (B)
Modifies the correlation search to exclude known test accounts.
Reduces false positives while keeping real threats visible.
Example:
Update the search to exclude test accounts:
index=auth_logs NOT user IN ("test_user1", "test_user2")
#Incorrect Answers:
A: Disable the correlation search for test accounts # This removes visibility into all failed logins, including those that may indicate real threats.
C: Lower the search threshold for failed logins # Would increase false positives, making it harder for SOC teams to focus on real attacks.
D: Suppress all notable events temporarily # Suppression hides all alerts, potentially missing real security incidents.
#Additional Resources:
Splunk ES: Managing Correlation Searches
Reducing False Positives in SIEM
NEW QUESTION # 34
What is the purpose of using data models in building dashboards?
Answer: A
Explanation:
Why Use Data Models in Dashboards?
SplunkData Modelsallow dashboards toretrieve structured, normalized data quickly, improving search performance and accuracy.
#How Data Models Help in Dashboards?(AnswerB)#Standardized Field Naming- Ensures that queries always useconsistent field names(e.g.,src_ipinstead ofsource_ip).#Faster Searches- Data models allow dashboards torun structured searches instead of raw log queries.#Example:ASOC dashboard for user activity monitoringuses a CIM-compliantAuthentication Data Model, ensuring that querieswork across different log sources.
Why Not the Other Options?
#A. To store raw data for compliance purposes- Raw data is stored in indexes,not data models.#C. To compress indexed data- Data modelsstructuredata but donot perform compression.#D. To reduce storage usage on Splunk instances- Data modelshelp with search performance, not storage reduction.
References & Learning Resources
#Splunk Data Models for Dashboard Optimization: https://docs.splunk.com/Documentation/Splunk/latest
/Knowledge/Aboutdatamodels#Building Efficient Dashboards Using Data Models: https://splunkbase.splunk.
com#Using CIM-Compliant Data Models for Security Analytics: https://www.splunk.com/en_us/blog/tips- and-tricks
NEW QUESTION # 35
Which syntax is correct to create two new rows on an existing threat intelligence collection?
Answer: B
Explanation:
The intended answer is A because its item parameter contains a JSON array with two separate objects , meaning two new threat-intelligence records are supplied in a single request:
[
{ " src_user " : " user_new " , " subject " : " click this " },
{ " src_user " : " user2_new " , " subject " : " click this " }
]
The surrounding single quotes are also important in a shell because they preserve the JSON ' s internal double quotes. Option C attempts to surround the entire JSON value with double quotes while also using unescaped double quotes inside the JSON, which would break normal shell parsing. Options B and D contain only one object , so they cannot create two rows.
There is one transcription issue worth noting: as pasted, options A and D end with an incomplete -X, and -G changes how cURL submits -d data. In a complete working REST call, the creation request would ordinarily use POST , for example -X POST, or rely on -d to imply POST. Thus A is clearly the intended certification answer based on its two-record JSON payload , although the pasted command ' s trailing flags are incomplete.
The supplied study material covers REST parameters, HTTP POST semantics, and threat-intelligence collections, but not this exact question verbatim.
Study Guide topics: Splunk REST API, Threat Intelligence Framework, JSON arrays, REST payloads, cURL, HTTP POST.
NEW QUESTION # 36
An engineer has been working on building a new automation for the SOC. What Scope should be selected in the SOAR Playbook Debugger during the playbook development to ensure consistency?
Answer: A
Explanation:
During playbook development and debugging, All Events provides the most consistent test scope because the engineer can repeatedly execute and evaluate the playbook against available event/container data rather than depending exclusively on events created after the playbook was activated.
A scope such as New Events is more appropriate for operational execution when the automation should trigger only as new events arrive. During development, however, restricting execution to newly created objects can make testing inconsistent: an engineer may need to repeatedly create fresh events simply to reproduce the same test case.
The artifact-oriented choices apply when execution is specifically driven by artifact creation or artifact-level behavior. They are not the broad event-level scope requested for consistent playbook debugging.
Using a stable test event is particularly useful when validating branching logic, asset actions, enrichment results, API responses, and downstream automation because the same input can be replayed while the engineer changes individual portions of the playbook.
The supplied study set discusses SOAR playbooks, containers, automation scope, and development workflows, although this exact debugger-scope question is not included verbatim.
Study Guide topics: Splunk SOAR Playbook Debugger, playbook scope, event processing, repeatable testing, automation development.
NEW QUESTION # 37
In a contextualization playbook, a URL is transmitted to a sandbox for examination and disposition recommendation. What underlying HTTP method is used to transmit this data to the sandbox?
Answer: C
Explanation:
The appropriate HTTP method is POST . A contextualization playbook submitting a URL to a sandbox is creating a new analysis request and transmitting data to an application endpoint for processing. REST APIs conventionally use POST for this type of operation.
A typical workflow sends an HTTP request containing the URL or artifact information in the request body.
The sandbox accepts the submission, generates an analysis job or task identifier, processes the object, and later returns a verdict or detailed analysis. SOAR can subsequently poll for completion or retrieve the resulting disposition.
GET is conventionally used to retrieve an existing resource and normally should not be used to create an analysis job. PUT is generally associated with creating or replacing a resource at a known URI and is not the standard semantic operation for submitting a new sandbox-analysis request. STOR is associated with FTP rather than HTTP and therefore is not an HTTP method applicable to this workflow.
Understanding these API semantics is essential when troubleshooting SOAR integrations because incorrect HTTP methods can produce errors such as 405 Method Not Allowed or other integration failures.
Study Guide topics: SOAR contextualization, REST APIs, HTTP POST, sandbox integration, automated enrichment, artifact analysis.
NEW QUESTION # 38
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