Curious about Actual Splunk Certified Cybersecurity Defense Engineer (SPLK-5002) Exam Questions?

Correct : A, B

Configurations Required for Data Normalization in Splunk

Data normalization ensures consistent field naming and event structuring, especially for Splunk Common Information Model (CIM) compliance.

1. props.conf (A)

Defines how data is parsed and indexed.

Controls field extractions, event breaking, and timestamp recognition.

Example:

Assigns custom sourcetypes and defines regex-based field extraction.

2. transforms.conf (B)

Used for data transformation, lookup table mapping, and field aliasing.

Example:

Normalizes firewall logs by renaming src_ip src to align with CIM.

Incorrect Answers:

C . savedsearches.conf Defines scheduled searches, not data normalization.

D . authorize.conf Manages user permissions, not data normalization.

E . eventtypes.conf Groups events into categories but doesn't modify data structure.

Additional Resources:

Splunk Data Normalization Guide

Understanding props.conf and transforms.conf

Correct : A, C, D

Risk and detection prioritization in Splunk Enterprise Security (ES) helps SOC analysts focus on the most critical threats. By assigning risk scores, integrating business context, and automating detection tuning, organizations can prioritize security incidents efficiently.

Methods to Improve Risk and Detection Prioritization:

Assigning Risk Scores to Assets and Events (A)

Uses Risk-Based Alerting (RBA) to prioritize high-risk activities based on behavior and history.

Helps SOC teams focus on true threats instead of isolated events.

Incorporating Business Context into Decisions (C)

Adds context from asset criticality, user roles, and business impact.

Ensures alerts are ranked based on their potential business impact.

Automating Detection Tuning (D)

Uses machine learning and adaptive response actions to reduce false positives.

Dynamically adjusts alert thresholds based on evolving threat patterns.

Incorrect Answers: B. Using predefined alert templates -- Static templates don't dynamically prioritize risk. E. Enforcing strict search head resource limits -- This impacts system performance but does not directly improve detection prioritization.

Splunk Risk-Based Alerting (RBA) Documentation

Best Practices for Prioritizing Security Alerts

Using Machine Learning for Threat Detection

Correct : A, C, D

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.

Incorrect Answers: B. Visualization -- Happens later in dashboards, but not part of the data pipeline itself. E. Alerting -- Occurs after the data pipeline processes and analyzes events.

Splunk Data Processing Pipeline Overview

How Splunk Parses and Indexes Data

Correct : A, D

Risk-based detection in Splunk prioritizes alerts based on behavior, threat intelligence, and business impact. Enhancing risk scores and enriching contextual data ensures that SOC teams focus on the most critical threats.

Methods to Enhance Risk-Based Detection:

Defining Accurate Risk Modifiers (A)

Adjusts risk scores dynamically based on asset value, user behavior, and historical activity.

Ensures that low-priority noise doesn't overwhelm SOC analysts.

Enriching Risk Objects with Contextual Data (D)

Adds threat intelligence feeds, asset criticality, and user behavior data to alerts.

Improves incident triage and correlation of multiple low-level events into significant threats.

Incorrect Answers: B. Limiting the number of correlation searches -- Reducing correlation searches may lead to missed threats. C. Using summary indexing for raw events -- Summary indexing improves performance but does not enhance risk-based detection.

Splunk Risk-Based Alerting Guide

Threat Intelligence in Splunk ES

Correct : B, D

How to Improve Data Indexing Performance in Splunk?

Optimizing indexing performance is critical for ensuring faster search speeds, better storage efficiency, and reduced latency in a Splunk deployment.

Why is 'Configuring Index-Time Field Extractions' Important? (Answer B)

Extracting fields at index time reduces the need for search-time processing, making searches faster.

Example: If security logs contain IP addresses, usernames, or error codes, configuring index-time extraction ensures that these fields are already available during searches.

Why 'Increasing the Number of Indexers in a Distributed Environment' Helps? (Answer D)

Adding more indexers distributes the data load, improving overall indexing speed and search performance.

Example: In a large SOC environment, more indexers allow for faster log ingestion from multiple sources (firewalls, IDS, cloud services).

Why Not the Other Options?

A. Indexing data with detailed metadata -- Adding too much metadata increases indexing overhead and slows down performance. C. Using lightweight forwarders for data ingestion -- Lightweight forwarders only forward raw data and don't enhance indexing performance.

Reference & Learning Resources

Splunk Indexing Performance Guide: https://docs.splunk.com/Documentation/Splunk/latest/Indexer/Howindexingworks Best Practices for Splunk Indexing Optimization: https://splunkbase.splunk.com Distributed Splunk Architecture for Large-Scale Environments: https://www.splunk.com/en_us/blog/tips-and-tricks