Important Splunk SPLK-5002 Exam Questions

Answer: A, C

Explanation:

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

Answer: B

Explanation:

When a correlation search in Splunk Enterprise Security (ES) generates excessive notable events due to test accounts, the best approach is to filter out test accounts while keeping legitimate detections active.

1. Apply Filtering to Exclude Test Accounts (B)

Modifies the correlation search to exclude known test accounts.

Reduces false positives while keeping real threats visible.

Example:

Update the search to exclude test accounts:

index=auth_logs NOT user IN ('test_user1', 'test_user2')

Incorrect Answers:

A . Disable the correlation search for test accounts This removes visibility into all failed logins, including those that may indicate real threats.

C . Lower the search threshold for failed logins Would increase false positives, making it harder for SOC teams to focus on real attacks.

D . Suppress all notable events temporarily Suppression hides all alerts, potentially missing real security incidents.

Additional Resources:

Splunk ES: Managing Correlation Searches

Reducing False Positives in SIEM

Answer: A, B, E

Explanation:

Correlation searches are a key component of Splunk Enterprise Security (ES) that help detect and alert on security threats by analyzing machine data across various sources. Proper tuning of these searches is essential to reduce false positives, improve performance, and enhance the accuracy of security detections in a Security Operations Center (SOC).

Crucial Features for Tuning Correlation Searches

1. Using Thresholds and Conditions (A)

Thresholds help control the sensitivity of correlation searches by defining when a condition is met.

Setting appropriate conditions ensures that only relevant events trigger notable events or alerts, reducing noise.

Example:

Instead of alerting on any failed login attempt, a threshold of 5 failed logins within 10 minutes can be set to identify actual brute-force attempts.

2. Reviewing Notable Event Outcomes (B)

Notable events are generated by correlation searches, and reviewing them is critical for fine-tuning.

Analysts in the SOC should frequently review false positives, duplicates, and low-priority alerts to refine rules.

Example:

If a correlation search is generating excessive alerts for normal user activity, analysts can modify it to exclude known safe behaviors.

3. Optimizing Search Queries (E)

Efficient Splunk Search Processing Language (SPL) queries are crucial to improving search performance.

Best practices include:

Using index-time fields instead of extracting fields at search time.

Avoiding wildcards and unnecessary joins in searches.

Using tstats instead of regular searches to improve efficiency.

Example:

Using:

| tstats count where index=firewall by src_ip

instead of:

index=firewall | stats count by src_ip

can significantly improve performance.

Incorrect Answers & Explanation

C. Enabling Event Sampling

Event sampling helps analyze a subset of events to improve testing but does not directly impact correlation search tuning in production.

In a SOC environment, tuning needs to be based on actual real-time event volumes, not just sampled data.

D. Disabling Field Extractions

Field extractions are essential for correlation searches because they help identify and analyze security-related fields (e.g., user, src_ip, dest_ip).

Disabling them would limit the visibility of important security event attributes, making detections less effective.

Additional Resources for Learning

Splunk Documentation & Learning Paths:

Splunk ES Correlation Search Documentation

Best Practices for Writing SPL

Splunk Security Essentials - Use Cases

SOC Analysts Guide for Correlation Search Tuning

Courses & Certifications:

Splunk Enterprise Security Certified Admin

Splunk Core Certified Power User

Splunk SOAR Certified Automation Specialist

Answer: B

Explanation:

Why Use Data Models in Dashboards?

Splunk Data Models allow dashboards to retrieve structured, normalized data quickly, improving search performance and accuracy.

How Data Models Help in Dashboards? (Answer B) Standardized Field Naming -- Ensures that queries always use consistent field names (e.g., src_ip instead of source_ip). Faster Searches -- Data models allow dashboards to run structured searches instead of raw log queries. Example: A SOC dashboard for user activity monitoring uses a CIM-compliant Authentication Data Model, ensuring that queries work across different log sources.

Why Not the Other Options?

A. To store raw data for compliance purposes -- Raw data is stored in indexes, not data models. C. To compress indexed data -- Data models structure data but do not perform compression. D. To reduce storage usage on Splunk instances -- Data models help with search performance, not storage reduction.

Reference & Learning Resources

Splunk Data Models for Dashboard Optimization: https://docs.splunk.com/Documentation/Splunk/latest/Knowledge/Aboutdatamodels Building Efficient Dashboards Using Data Models: https://splunkbase.splunk.com Using CIM-Compliant Data Models for Security Analytics: https://www.splunk.com/en_us/blog/tips-and-tricks

Answer: A

Explanation:

In Splunk Enterprise Security (ES), notable events are generated by correlation searches, which are predefined searches designed to detect security incidents by analyzing logs and alerts from multiple data sources. Adding additional context to these notable events enhances their value for analysts and improves the efficiency of incident response.

To incorporate additional context, you can:

Use lookup tables to enrich data with information such as asset details, threat intelligence, and user identity.

Leverage KV Store or external enrichment sources like CMDB (Configuration Management Database) and identity management solutions.

Apply Splunk macros or eval commands to transform and enhance event data dynamically.

Use Adaptive Response Actions in Splunk ES to pull additional information into a notable event.

The correct answer is A. By adding enriched fields during search execution, because enrichment occurs dynamically during search execution, ensuring that additional fields (such as geolocation, asset owner, and risk score) are included in the notable event.

Splunk ES Documentation on Notable Event Enrichment

Correlation Search Best Practices

Using Lookups for Data Enrichment