SPLK-5002 Exam Dumps

Explanation:

Risk-based detection in Splunk Enterprise Security (ES) relies on three core components:

Risk objects – Entities (e.g., users, IPs, hosts) being evaluated for risk.
Risk modifiers – Conditions or rules that adjust risk scores (e.g., "high-severity attack pattern detected").
Risk scores – Numeric values assigned to objects based on threat severity (e.g., 0–100).

These elements work together to prioritize threats dynamically.

Why Not the Others?

B. Summary indexing, tags, and event types → Useful for data organization but not specific to risk-based detection.
C. Alerts, notifications, and priority levels → Part of incident response, not risk scoring logic.
D. Source types, correlation searches, and asset groups → Supports data mapping and detection but isn’t the core of risk-based analytics.

Key Splunk ES Features for Risk-Based Detection:
Risk Analysis dashboard (aggregates risk scores).
Adaptive Response Actions (auto-trigger responses based on risk thresholds).
MITRE ATT&CK alignment (maps risks to tactics/techniques).

Explanation:

A notable event in Splunk Enterprise Security (ES) represents a significant security detection that requires investigation.

#Key Elements of a Good Notable Event:#Meaningful Descriptions (Answer A) Helps analysts understand the event at a glance.

Example: Instead of "Possible attack detected," use "Multiple failed admin logins from foreign IP address". #Proper Categorization (Answer C)
Ensures events are classified correctly (e.g., Brute Force, Insider Threat, Malware Activity).

Example: A malicious file download alert should be categorized as "Malware Infection", not just "General Alert".

#Relevant Field Extractions (Answer D)

Ensures that critical details (IP, user, timestamp) are present for SOC analysis.

Example: If an alert reports failed logins, extracted fields should include username, source IP, and login method.

Why Not the Other Options?
#B. Minimal use of contextual data – More context helps SOC analysts investigate faster.

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.

Explanation:

Explanation:

A SOAR (Security Orchestration, Automation, and Response) playbook is a set of automated actions designed to respond to security incidents. Before deploying it in a live environment, a security analyst must ensure that it operates correctly, minimizes false positives, and doesn’t disrupt business operations.

#Key Reasons for Using Simulated Incidents:
Ensures that the playbook executes correctly and follows the expected workflow. Identifies false positives or incorrect actions before deployment.
Tests integrations with other security tools (SIEM, firewalls, endpoint security). Provides a controlled testing environment without affecting production.

How to Test a Playbook in Splunk SOAR?
1##Use the "Test Connectivity" Feature – Ensures that APIs and integrations work.
2##Simulate an Incident – Manually trigger an alert similar to a real attack (e.g., phishing email or failed admin login).
3##Review the Execution Path – Check each step in the playbook debugger to verify correct actions.
4##Analyze Logs & Alerts – Validate that Splunk ES logs, security alerts, and remediation steps are correct.
5##Fine-tune Based on Results – Modify the playbook logic to reduce unnecessary alerts or excessive automation.

Why Not the Other Options?
#B. Monitor the playbook’s actions in real-time environments – Risky without prior validation. It can cause disruptions if the playbook mis fires.
#C. Automate all tasks immediately – Not best practice. Gradual deployment ensures better security control and monitoring.
#D. Compare with existing workflows – Good practice, but it does not validate the playbook’s real execution.

Explanation:

The GET method in the Splunk REST API is used to retrieve data from a Splunk index. It allows users and automated scripts to fetch logs, alerts, or query results programmatically.

Key Points About GET in Splunk API:
Used for searching and retrieving logs from indexes.

Can be used to get search results, job status, and Splunk configuration details. Common API endpoints include:
/services/search/jobs/{search_id}/results– Retrieves results of a completed search.
/services/search/jobs/export– Exports search results in real-time

Explanation:

When building a new security process, the top priority should be making sure it meets regulatory and compliance requirements (e.g., HIPAA, GDPR, PCI-DSS, etc.). Failing to do so can result in legal issues, fines, or data breaches.

Why the other options are less appropriate:

A. Integrating it with legacy systems
Important, but secondary to compliance. Integration challenges can be addressed after the process is validated as secure and compliant.

C. Automating all workflows within the process
Automation is valuable for efficiency, but shouldn’t be the first priority. You should first ensure the process is secure and compliant before automating it.

D. Reducing the overall number of employees required
While operational efficiency matters, the main goal of a security process is risk reduction and compliance, not workforce reduction.