Splunk SPLK-5002 Splunk Certified Cybersecurity Defense Engineer Exam Practice Test

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.

Aligning security processes with frameworks likeNIST Cybersecurity Framework (CSF)orMITRE ATT&CKprovides astructured approach to threat detection and response.

Benefits of Using Common Security Methodologies:

Enhancing Organizational Compliance (A)

Helps organizationsmeet regulatory requirements(e.g., NIST, ISO 27001, GDPR).

Ensuresconsistent security controlsare implemented.

Ensuring Standardized Threat Responses (C)

MITRE ATT&CK providesa common language for adversary techniques.

ImprovesSOC workflows by aligning detection and response strategies.

When organizations need to normalize event data from various sources, using Common Information Model (CIM) in Splunk is the best approach.

Why Use CIM for Normalized Event Data?

Standardizes Data Across Different Log Sources

CIM ensures consistent field names and formats across varied log types.

Makes searches, reports, and dashboards easier to manage.

Enables Faster and More Efficient Searches

Uses Data Models to accelerate search queries.

Reduces the need for custom field extractions.

If a user queries an index during a high-priority incident but sees incomplete results, it is likely that the indexers are overloaded, causing queue bottlenecks.

Why Indexer Queue Capacity Issues Cause Incomplete Results:

When indexing queues fill up, incoming data cannot be processed efficiently.

Search results may be incomplete or delayed if events are still in the indexing queue and not fully written to disk.

Heavy search loads during incidents can also increase pressure on indexers.

How to Fix It:

Monitor indexing queues via the Monitoring Console (indexing>indexing performance).

Checkmetrics.logon indexers formax_queue_size_exceededwarnings.

Increase indexer capacity or optimize search scheduling to reduce load.

Splunk prevents duplicate data from being indexed through event parsing, which occurs during the data ingestion process.

How Event Parsing Prevents Duplicate Data:

Splunk’s indexer parses incoming data and assigns unique timestamps, metadata, and event IDs to prevent reindexing duplicate logs.

CRC Checks (Cyclic Redundancy Checks) are applied to avoid duplicate event ingestion.

Index-time filtering and transformation rules help detect and drop repeated data before indexing.

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.