SPLK-5001 Splunk Certified Cybersecurity Defense Analyst Free Practice Exam Questions (2025 Updated)

The log entry indicates aPOST /cgi-bin/shutdown/request, which suggests that a command was sent to shut down the server via a CGI script. This kind of activity is indicative of aDenial of Service (DoS) attackbecause it involves sending a specific command that causes the server to stop functioning or shut down. This is different from a Distributed Denial of Service (DDoS) attack, which typically involves overwhelming the server with traffic rather than exploiting a specific command.

The step-by-step description provided is an example of aTechniqueas defined in the MITRE ATT&CK framework. Techniques are the specific methods adversaries use to achieve their objectives during an attack, such as establishing command and control (C2) channels or delivering payloads via phishing emails. In this scenario, the attacker uses a non-default beacon profile in Cobalt Strike, sends a malicious document via email, and establishes a C2 channel once the victim interacts with the document, all of which are examples of adversary techniques.

Notable Events in Splunk Enterprise Security are configured as part of a correlation search, where an Adaptive Response Action can be set to create a Notable Event when certain conditions are met. These correlation searches are pre-defined or custom searches that look for specific patterns of interest, such as security incidents or anomalies. The use of Adaptive Response Actions within these searches allows for the automated creation of Notable Events, which can then be investigated by security analysts. This configuration is a crucial part of Splunk's security operations capabilities.

Mean Time to Respond (MTTR)is a critical SOC metric measuring the average time from detection of a security incident to the point where it is resolved or dispositioned. According to theSplunk Enterprise Security and SOC Operations documentation, the typical start time for MTTR calculations iswhen a Notable Event is triggeredin Splunk ES. Notable Events represent the initial alert generated by correlation searches or detection mechanisms, marking the point where the SOC becomes aware of a potential incident requiring analyst review.

Starting MTTR at the time the malicious event occurs (option A) is impractical since detection is rarely instantaneous.

SOC Manager notification (option B) is too late in the workflow, as response metrics are analyst-focused.

Notifying end users (option D) is a post-response activity and does not define the start of response time.

This approach standardizes MTTR calculation across security operations and provides actionable insights into detection-to-response performance.

In Splunk,default metadatarefers to automatically assigned attributes associated with each event at indexing time that help identify and organize data. These include:

Source: The origin of the data (file, network port, etc.)

Timestamps: The time the event occurred, extracted from the event or assigned at ingestion

Host name: The name of the host generating the event

Event descriptionis not a default metadata field in Splunk. It is typically user-defined or derived from the event content and is not assigned automatically by Splunk.

TheSplunk documentationon event metadata clarifies these standard fields, which are crucial for search filtering and data organization.

In Splunk, field extractions are essential for transforming raw log data into structured fields that are easier to work with during analysis. When the question refers to an analyst identifying helpful information in the raw logs that assists them in determining suspicious activity, the most effective way to streamline this process is throughfield extraction. This allows the Splunk system to automatically parse and tag the necessary data, making it more accessible for searches, dashboards, and alerts.

Let’s break down whyoption A: Create a field extraction for this informationis the best approach:

Field Extraction Overview:

Field extraction is a process within Splunk that takes unstructured log data and converts it into structured fields.

This makes it possible to directly query and display these fields, allowing analysts to quickly find and use relevant data in their investigations.

For example, if the logs contain IP addresses, user IDs, file names, or activity types, extracting these fields enables the analyst to filter and correlate data much more effectively without manually scanning the raw logs.

Why Field Extraction?

In this case, the question suggests that the raw logs contain information that helps determine whether activity is malicious. By creating field extractions for the relevant data points, analysts can use those structured fields to build queries and visualizations, drastically speeding up analysis time.

Analysts can write custom Splunk queries to isolate events that meet specific conditions, such as matching specific cloud sharing activities associated with risk notables.

Field extraction improves not only real-time analysis but also supports retrospective analysis and incident correlation across multiple events.

Comparison to Other Options:

Option B: Add this information to the risk message– While adding more context to a risk message could be useful for reviewing individual alerts, it doesn’t improve the efficiency of log analysis. The analyst still would need to go back and manually inspect raw logs for more detailed data.

Option C: Create another detection for this information– Creating additional detections adds more rules, but doesn't solve the fundamental issue of having raw logs that aren’t easily searchable. You can only build effective detections when you have structured data available.

Option D: Allowlist more events based on this information– Allowlisting is generally used to reduce noise or irrelevant logs, but it doesn't help extract the necessary details for analysis. It may reduce unnecessary alerts, but won’t help analyze the suspicious events that do arise.

Cybersecurity Defense Analyst Best Practices:

Field extractionsshould be created for any important log source or data point, especially when handling complex or multi-part log entries (e.g., cloud sharing logs). This ensures logs are searchable and actionable, allowing for faster identification of anomalies and malicious activity.

Analysts should collaborate with engineers to ensure these extractions are tuned and validated. The extraction should be tailored to isolate the fields most relevant for identifying suspicious activity.

Once fields are extracted, analysts can create dashboards, real-time alerts, or retrospective searches based on the structured data for more effective incident response.

The description outlines a process where similar data points (emails with similar attributes) are grouped based on their proximity in a multidimensional space (sender, recipient, subject, URLs, attachments), which is a classic definition ofclustering. Clustering algorithms group data points into clusters so that points in the same cluster are more similar to each other than to those in other clusters.

Clusteringis used extensively in threat hunting to identify campaigns or patterns that share common characteristics but may not be exactly the same, such as phishing emails with minor variations.

Least Frequency of Occurrence Analysisfocuses on rare events, which is opposite of the described method.

Time Series Analysisanalyzes data points in temporal order, not similarity grouping.

Most Frequency of Occurrence Analysiscounts most frequent items but does not spatially group them by similarity.

TheSplunk App for Data Science and Deep Learningsupports clustering techniques such as k-means, DBSCAN, and hierarchical clustering, which analysts can use to detect campaigns spreading across multiple users.

Splunk Enterprise Security (ES) provides various features to enhance security monitoring, analysis, and incident response. One of the powerful features in Splunk ES isAnnotations. This feature allows security analysts to map and categorize correlation search results according to well-known industry frameworks such as the CIS Critical Security Controls, MITRE ATT&CK, and the Lockheed Martin Cyber Kill Chain®.

Purpose of Annotations:

Annotations help analysts understand and categorize security events by aligning them with recognized security frameworks. This alignment provides context, making it easier to understand the nature of threats and how they fit within broader threat models or attack strategies.

How Annotations Work:

When a correlation search in Splunk ES triggers an alert, Annotations can automatically tag the alert with relevant tactics, techniques, and procedures (TTPs) from frameworks like MITRE ATT&CK. This helps in categorizing the event within the context of known attack patterns, offering insights into potential next steps by an attacker and recommended defensive actions.

Annotations can be manually added or configured to be applied automatically based on the nature of the search results.

Integration with Frameworks:

MITRE ATT&CK:Annotations can map alerts to specific techniques and tactics in the MITRE ATT&CK framework, which provides a detailed knowledge base of adversary behaviors, tactics, and techniques.

CIS Critical Security Controls:These controls can also be mapped through annotations, allowing the organization to measure and improve its security posture against these controls.

Lockheed Martin Cyber Kill Chain®:This model focuses on the stages of a cyberattack, and annotations can help identify where in the kill chain a particular alert fits, providing a clearer understanding of the attack’s progression.

Annotations in Splunk ES:Practical Example:Consider a correlation search that detects unusual behavior indicating potential lateral movement within a network. If this alert is annotated with a reference to the MITRE ATT&CK framework, it might map to techniques like "T1021 - Remote Services," which is associated with the lateral movement tactic. This mapping not only categorizes the event but also helps in planning the next steps for containment and investigation.

Efficiency in Response:By aligning alerts with industry frameworks, annotations help in quickly identifying the nature and potential impact of a threat.

Consistency in Analysis:Provides a standardized method for categorizing and responding to alerts, ensuring that all analysts interpret and react to threats in a consistent manner.

Improved Reporting:Allows for better visualization and reporting of threats according to established frameworks, making it easier to communicate risks and actions to stakeholders.