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