Splunk SPLK-5002 Practice Test - Questions Answers, Page 2

What is the Splunk Common Information Model (CIM)?

Splunk's Common Information Model (CIM) is a standardized way to normalize and map event data from different sources to a common field format. It helps with:

Consistent searches across diverse log sources

Faster correlation of security events

Better compatibility with prebuilt dashboards, alerts, and reports

Why is Data Normalization Important?

Security teams analyze data from firewalls, IDS/IPS, endpoint logs, authentication logs, and cloud logs.

These sources have different field names (e.g., ''src_ip'' vs. ''source_address'').

CIM ensures a standardized format, so correlation searches work seamlessly across different log sources.

How CIM Works in Splunk?

Maps event fields to a standardized schema Supports prebuilt Splunk apps like Enterprise Security (ES) Helps SOC teams quickly detect security threats

Example Use Case:

A security analyst wants to detect failed admin logins across multiple authentication systems.

Without CIM, different logs might use:

user_login_failed

auth_failure

login_error

With CIM, all these fields map to the same normalized schema, enabling one unified search query.

Why Not the Other Options?

A. Extract fields from raw events -- CIM does not extract fields; it maps existing fields into a standardized format. C. Compress data during indexing -- CIM is about data normalization, not compression. D. Create accelerated reports -- While CIM supports acceleration, its main function is standardizing log formats.

Reference & Learning Resources

Splunk CIM Documentation: https://docs.splunk.com/Documentation/CIM How Splunk CIM Helps with Security Analytics: https://www.splunk.com/en_us/solutions/common-information-model.html Splunk Enterprise Security & CIM Integration: https://splunkbase.splunk.com/app/263

Why Use CIM for Field Normalization?

When processing logs from multiple sources with inconsistent field names, the best way to ensure uniformity is to use Splunk's Common Information Model (CIM).

Key Benefits of CIM for Normalization:

Ensures that different field names (e.g., src_ip, ip_src, source_address) are mapped to a common schema.

Allows security teams to run a single search query across multiple sources without manual mapping.

Enables correlation searches in Splunk Enterprise Security (ES) for better threat detection.

Example Scenario in a SOC:

Problem: The SOC team needs to correlate firewall logs, cloud logs, and endpoint logs for failed logins. Without CIM: Each log source uses a different field name for failed logins, requiring multiple search queries. With CIM: All failed login events map to the same standardized field (e.g., action='failure'), allowing one unified search query.

Why Not the Other Options?

A. Create field extraction rules at search time -- Helps with parsing data but doesn't standardize field names across sources. B. Use data model acceleration for real-time searches -- Accelerates searches but doesn't fix inconsistent field naming. D. Configure index-time data transformations -- Changes fields at indexing but is less flexible than CIM's search-time normalization.

Reference & Learning Resources

Splunk CIM for Normalization: https://docs.splunk.com/Documentation/CIM Splunk ES CIM Field Mappings: https://splunkbase.splunk.com/app/263 Best Practices for Log Normalization: https://www.splunk.com/en_us/blog/tips-and-tricks

Why Use Index-Time Transformations for One-Time Parsing & Indexing?

Splunk parses and indexes data once during ingestion to ensure efficient storage and search performance. Index-time transformations ensure that logs are:

Parsed, transformed, and stored efficiently before indexing. Normalized before indexing, so the SOC team doesn't need to clean up fields later. Processed once, ensuring optimal storage utilization.

Example of Index-Time Transformation in Splunk: Scenario: The SOC team needs to mask sensitive data in security logs before storing them in Splunk. Solution: Use an INDEXED_EXTRACTIONS rule to:

Redact confidential fields (e.g., obfuscate Social Security Numbers in logs).

Rename fields for consistency before indexing.

Effective documentation ensures that security teams can standardize response procedures, reduce incident response time, and improve compliance.

1. Visual Workflow Diagrams (B)

Helps map out security processes in an easy-to-understand format.

Useful for SOC analysts, engineers, and auditors to understand incident escalation procedures.

Example:

Incident flow diagrams showing escalation from Tier 1 SOC analysts Threat hunters Incident response teams.

2. Incident Response Playbooks (C)

Defines step-by-step response actions for security incidents.

Standardizes how teams should detect, analyze, contain, and remediate threats.

Example:

A SOAR playbook for handling phishing emails (e.g., extract indicators, check sandbox results, quarantine email).

Incorrect Answers:

A . Detailed event logs Logs are essential for investigations but do not constitute process documentation.

D . Customer satisfaction surveys Not relevant to security process documentation.

Additional Resources:

NIST Cybersecurity Framework - Incident Response

Splunk SOAR Playbook Documentation

Notable events in Splunk Enterprise Security (ES) are triggered by correlation searches, which generate alerts when suspicious activity is detected. However, if too many false positives occur, analysts waste time investigating non-issues, reducing SOC efficiency.

How to Improve Notable Events Effectiveness:

Apply suppression rules to filter out known false positives and reduce alert fatigue.

Refine correlation searches by adjusting thresholds and tuning event detection logic.

Leverage risk-based alerting (RBA) to prioritize high-risk events.

Use adaptive response actions to enrich events dynamically.

By suppressing false positives, SOC analysts focus on real threats, making notable events more actionable. Thus, the correct answer is A. Applying suppression rules for false positives.

Managing Notable Events in Splunk ES

Best Practices for Tuning Correlation Searches

Using Suppression in Splunk ES

Effective case management in Splunk Enterprise Security (ES) helps streamline incident tracking, investigation, and resolution.

How to Optimize Case Management:

Standardizing ticket creation workflows (A)

Ensures consistency in how incidents are reported and tracked.

Reduces manual errors and improves collaboration between SOC teams.

Integrating Splunk with ITSM tools (C)

Automates the process of creating and updating tickets in ServiceNow, Jira, or Remedy.

Enables better tracking of incidents and response actions.

Incorrect Answers: B. Increasing the indexing frequency -- This improves data availability but does not directly optimize case management. D. Reducing the number of search heads -- This might degrade search performance rather than optimize case handling.

Splunk ES Case Management

Integrating Splunk with ServiceNow

Automating Ticket Creation in Splunk

The Splunk REST API allows programmatic access to Splunk's features, helping automate security workflows in a Security Operations Center (SOC).

Key REST API Actions for Automation:

POST for creating new data entries (A)

Used to send logs, alerts, or notable events to Splunk.

Essential for integrating external security tools with Splunk.

GET for retrieving search results (C)

Fetches logs, alerts, and notable event details programmatically.

Helps automate security monitoring and incident response.

Incorrect Answers: B. DELETE for archiving historical data -- DELETE is rarely used in Splunk as it does not archive data; instead, retention policies handle old data. D. PUT for updating index configurations -- While PUT can modify configurations, it is not a core automation function in SOC workflows.

Splunk REST API Documentation

Using Splunk API for Automation

Best Practices for Automating Security Workflows

Splunk SOAR (Security Orchestration, Automation, and Response) playbooks help SOC teams automate, orchestrate, and respond to threats faster.

Key Benefits of SOAR Playbooks

Automates Repetitive Tasks

Reduces manual workload for SOC analysts.

Automates tasks like enriching alerts, blocking IPs, and generating reports.

Orchestrates Multiple Security Tools

Integrates with firewalls, EDR, SIEMs, threat intelligence feeds.

Example: A playbook can automatically enrich an IP address by querying VirusTotal, Splunk, and SIEM logs.

Accelerates Incident Response

Reduces Mean Time to Detect (MTTD) and Mean Time to Respond (MTTR).

Example: A playbook can automatically quarantine compromised endpoints in CrowdStrike after an alert.

Incorrect Answers:

A . Manually running searches across multiple indexes SOAR playbooks are about automation, not manual searches.

C . Improving dashboard visualization capabilities Dashboards are part of SIEM (Splunk ES), not SOAR playbooks.

D . Enhancing data retention policies Retention is a Splunk Indexing feature, not SOAR-related.

Additional Resources:

Splunk SOAR Playbook Guide

Automating Threat Response with SOAR

Key Elements of Meaningful Security Metrics

Security metrics should align with business goals, be validated regularly, and have standardized definitions to ensure reliability.

1. Relevance to Business Objectives (A)

Security metrics should tie directly to business risks and priorities.

Example:

A financial institution might track fraud detection rates instead of generic malware alerts.

2. Regular Data Validation (B)

Ensures data accuracy by removing false positives, duplicates, and errors.

Example:

Validating phishing alert effectiveness by cross-checking with user-reported emails.

3. Consistent Definitions for Key Terms (E)

Standardized definitions prevent misinterpretation of security metrics.

Example:

Clearly defining MTTD (Mean Time to Detect) vs. MTTR (Mean Time to Respond).

Incorrect Answers:

C . Visual representation through dashboards Dashboards help, but data quality matters more.

D f. Avoiding integration with third-party tools Integrations with SIEM, SOAR, EDR, and firewalls are crucial for effective metrics.

Additional Resources:

NIST Security Metrics Framework

Splunk