Splunk SPLK-5002 Exam Questions

The sourcetype in Splunk defines how incoming machine data is interpreted, structured, and stored. Proper sourcetype configurations ensure accurate event parsing, indexing, and searching.

1. Event Breaking Rules (A)

Determines how Splunk splits raw logs into individual events.

If misconfigured, a single event may be broken into multiple fragments or multiple log lines may be combined incorrectly.

Controlled using LINE_BREAKER and BREAK_ONLY_BEFORE settings.

2. Timestamp Extraction (B)

Extracts and assigns timestamps to events during ingestion.

Incorrect timestamp configuration leads to misplaced events in time-based searches.

Uses TIME_PREFIX, MAX_TIMESTAMP_LOOKAHEAD, and TIME_FORMAT settings.

3. Line Merging Rules (D)

Controls whether multiline events should be combined into a single event.

Useful for logs like stack traces or multi-line syslog messages.

Uses SHOULD_LINEMERGE and LINE_BREAKER settings.

Incorrect Answer:

C . Data Retention Policies

Affects storage and deletion, not data ingestion itself.

Additional Resources:

Splunk Sourcetype Configuration Guide

Event Breaking and Line Merging

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.

Incorrect Answers: A. Data deduplication -- While deduplication removes duplicates in searches, it does not prevent duplicate indexing. B. Metadata tagging -- Tags help with categorization but do not control duplication. C. Indexer clustering -- Clustering improves redundancy and availability but does not prevent duplicates.

Splunk Data Parsing Process

Splunk Indexing and Data Handling

Automating case management workflows in Splunk streamlines incident response and reduces manual overhead, allowing analysts to focus on higher-value tasks.

Main Benefits of Automating Case Management:

Reduces Response Times (C)

Automatically assigns cases to analysts based on predefined rules.

Triggers playbooks and workflows in Splunk SOAR to handle common incidents.

Improves Analyst Productivity (C)

Reduces time spent on manual case creation and updates.

Provides integrated case tracking across Splunk and ITSM tools (e.g., ServiceNow, Jira).

Incorrect Answers: A. Eliminating the need for manual alerts -- Alerts still require analyst verification and triage. B. Enabling dynamic storage allocation -- Case management does not impact Splunk storage. D. Minimizing the use of correlation searches -- Correlation searches remain essential for detection, even with automation.

Splunk Case Management Best Practices

Automating Incident Response with Splunk SOAR

If there is a delay in data being indexed from a remote location, even though the Universal Forwarder (UF) is correctly configured, the issue is likely a queue blockage or network latency.

Steps to Diagnose and Fix Forwarder Delays:

Check Forwarder Logs (splunkd.log) for Queue Issues (A)

Look for messages like TcpOutAutoLoadBalanced or Queue is full.

If queues are full, events are stuck at the forwarder and not reaching the indexer.

Monitor Forwarder Health Using metrics.log

Use index=_internal source=*metrics.log* group=queue to check queue performance.

Incorrect Answers: B. Increase the indexer memory allocation -- Memory allocation does not resolve forwarder delays. C. Optimize search head clustering -- Search heads manage search performance, not forwarder ingestion. D. Reconfigure the props.conf file -- props.conf affects event processing, not ingestion speed.

Splunk Forwarder Troubleshooting Guide

Monitoring Forwarder Queue Performance

Why Use Risk-Based Dashboards for Tracking Threat Trends?

Risk-based dashboards in Splunk Enterprise Security (ES) provide a structured way to track threats over time.

How Risk-Based Dashboards Help: Aggregate security events into risk scores Helps prioritize high-risk activities. Show historical trends of threat activity. Correlate multiple risk factors across different security events.

Example in Splunk ES: Scenario: A SOC team tracks insider threat activity over 6 months. The Risk-Based Dashboard shows:

Users with rising risk scores over time.

Patterns of malicious behavior (e.g., repeated failed logins + data exfiltration).

Correlation between different security alerts (e.g., phishing clicks malware execution).

Why Not the Other Options?

A. Event sampling -- Helps with performance optimization, not threat trend tracking. C. Summary indexing -- Stores precomputed data but is not designed for tracking risk trends. D. Data model acceleration -- Improves search speed, but doesn't track security trends.

Reference & Learning Resources

Splunk ES Risk-Based Alerting Guide: https://docs.splunk.com/Documentation/ES Tracking Security Trends Using Risk-Based Dashboards: https://splunkbase.splunk.com How to Build Risk-Based Analytics in Splunk: https://www.splunk.com/en_us/blog/security