Splunk SPLK-3001 Practice Test Questions, Exam Dumps

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SPLK-3001 Splunk Practice Test Questions and Exam Dumps

Question No 1:

The Add-On Builder in Splunk is used to create Splunk Apps that follow a specific naming convention. Which of the following prefixes is commonly used for Splunk Apps created by the Add-On Builder?

A. DA-
B. SA-
C. TA-
D. App-

Answer: C. TA-

Explanation:

In Splunk, various types of apps and add-ons are created to extend the platform’s functionality. These apps typically serve specific purposes such as integrating with external data sources, enhancing search capabilities, or providing additional visualizations. One of the tools used to build these apps is the Splunk Add-On Builder. This tool helps developers create custom Splunk Apps or add-ons that integrate data from various external systems or services, like databases, cloud providers, or other third-party software.

When it comes to naming conventions for these apps, "TA-" stands for Technology Add-on, and is a prefix used for Splunk apps created by the Add-On Builder. A Technology Add-on (TA) usually focuses on data collection, transformation, and normalization. It helps Splunk to ingest data from external sources in a standardized format, making it easier for Splunk to process and analyze the data.

In contrast:

DA- stands for Data Add-on, which is a less common prefix, and may be used for certain data-related configurations but is not standard for the Add-On Builder.
SA- refers to Splunk Apps, often used for applications that provide specific functionalities or visualizations, but these are typically not built with the Add-On Builder.
App- is a general term that could apply to any Splunk App and is not specifically associated with the Add-On Builder or the data integration process.

Therefore, the correct answer is C. TA-, as the Add-On Builder specifically generates apps with this prefix for the purpose of technology integrations and data normalization. This ensures consistency and clear identification of the app's purpose within Splunk’s ecosystem.

Question No 2:

Which of the following are typical sources of events for monitoring in endpoint security dashboards? Select all that apply.

A. REST API invocations
B. Investigation final results status
C. Workstations, notebooks, and point-of-sale systems
D. Lifecycle auditing of incidents, from assignment to resolution

Answer:

The correct answers are:

A. REST API invocations
C. Workstations, notebooks, and point-of-sale systems
D. Lifecycle auditing of incidents, from assignment to resolution

Explanation:

In the context of endpoint security, dashboards serve as a critical tool to monitor, analyze, and respond to security events from various sources. These sources feed information into the system, allowing security teams to detect and mitigate threats.

A. REST API invocations:
REST APIs are commonly used for exchanging data between systems, including security monitoring tools and endpoint security platforms. These invocations trigger event logs and status updates related to interactions with security services, application behavior, or automated processes. API calls often contain useful data regarding the success or failure of security integrations, system configurations, or the status of security alerts. Therefore, API invocations are an essential event source for endpoint security dashboards.

B. Investigation final results status:
While this is an important metric for incident management, the "final results status" of investigations is more of an outcome or a conclusion after an incident has been reviewed. It’s not a direct event source. Event data, in this case, would come from the monitoring systems during the course of an investigation rather than the status itself.

C. Workstations, notebooks, and point-of-sale systems:
These devices are typical endpoints within an organization's network and are rich sources of security events. Endpoints generate data related to potential threats, malicious activities, system anomalies, and user behaviors, all of which are crucial for monitoring. This data is constantly fed into security dashboards for real-time analysis.

D. Lifecycle auditing of incidents, from assignment to resolution:
This refers to tracking and logging the stages of an incident response, from initial detection to final resolution. Lifecycle auditing is critical in monitoring security events and ensuring comprehensive response efforts are documented, providing valuable insights into overall security effectiveness.

By collecting data from these sources, endpoint security dashboards help analysts stay informed and respond to emerging security risks effectively.

Question No 3:

When configuring custom correlation searches in a security monitoring system (such as Splunk),

Which format should be used to embed field values within the title, description, and drill-down fields of a notable event?

A. $fieldname$
B. €fieldname€
C. %fieldname%
D. _fieldname_

Correct Answer: A. $fieldname$

Explanation:

In many security information and event management (SIEM) platforms, such as Splunk, correlation searches help detect potential threats by analyzing events and generating notable events based on predefined rules. These notable events often include critical information such as event titles, descriptions, and drill-down details that help security analysts quickly understand the context of an incident.

When creating custom correlation searches, it is common to want to dynamically populate certain fields—like the title, description, or drill-down—based on the values extracted from the events. This makes the alerts more informative and personalized to the specific data in the logs.

To insert field values into these fields, the correct format to use is $fieldname$.

For example, if you want to include the IP address from an event within the title of the notable event, you would use $src_ip$ in the title field of the correlation search. When the search runs and an event matches the rule, it will replace $src_ip$ with the actual source IP from the event.

Here’s a breakdown of the different options:

A. $fieldname$
This is the correct format for embedding field values in the title, description, or drill-down fields of a notable event. The $ symbols denote the dynamic insertion of field values at runtime.
B. €fieldname€
This is not a valid format in SIEM systems for field embedding. The euro symbol is unrelated to field references in correlation searches.
C. %fieldname%
While the percent symbol is sometimes used in other contexts, such as for environment variables or regex patterns, it is not used for embedding fields in notable event fields.
D. _fieldname_
This format is also incorrect. While underscores are commonly used in field names (especially when configuring custom fields), this is not the correct way to embed field values dynamically into correlation search results.

By correctly using $fieldname$, you ensure that the notable events generated by correlation searches provide actionable, context-rich data to help security teams respond effectively to potential threats.

This explanation clarifies how to dynamically reference fields in custom correlation searches and provides a comprehensive look at why the $fieldname$ format is essential for SIEM systems. Let me know if you'd like further details or examples!

Question No 4:

Which component of Enterprise Security is responsible for downloading threat intelligence data from a web server?

Options:

A. Threat Service Manager
B. Threat Download Manager
C. Threat Intelligence Parser
D. Threat Intelligence Enforcement

Answer: B. Threat Download Manager

Explanation:

In modern cybersecurity frameworks, threat intelligence plays a critical role in proactively identifying and mitigating security risks. Enterprise Security platforms, which are designed to monitor and protect an organization’s IT infrastructure, often incorporate various components that manage and analyze incoming threat data. One such key component is the Threat Download Manager, which is specifically responsible for downloading threat intelligence data from web servers.

The Threat Download Manager functions by fetching threat intelligence feeds from external sources, such as web servers, APIs, or threat intelligence providers. These feeds contain up-to-date information about emerging threats, vulnerabilities, and indicators of compromise (IOCs). The purpose of this process is to ensure that the security system is equipped with the latest data to detect and respond to potential cyber threats. By leveraging real-time threat intelligence, the system can more effectively identify suspicious patterns and take preventative measures, such as blocking malicious IP addresses or domains.

Detailed Overview of the Other Options:

A. Threat Service Manager: This component typically oversees and coordinates various threat-related services within the security platform. While it plays a role in managing overall threat services, it is not responsible for downloading threat intelligence data.
C. Threat Intelligence Parser: After threat intelligence is downloaded, the Threat Intelligence Parser processes and parses the data, making it usable for the security system. This step involves transforming raw threat intelligence data into a structured format that can be used for analysis and decision-making.
D. Threat Intelligence Enforcement: This component is responsible for enforcing security policies based on the threat intelligence. Once the intelligence has been parsed and analyzed, it can trigger security actions such as blocking traffic or alerting administrators, but it does not download the intelligence data itself.

In summary, the Threat Download Manager plays a crucial role in ensuring that the system has the latest, most relevant threat data, which is vital for maintaining the security of the enterprise environment. It directly connects with external servers to obtain up-to-date intelligence, enabling the enterprise security system to remain effective against evolving threats.

Question No 5:

The Remote Access panel on the User Activity dashboard is not displaying data for the most recent hour. What data model should be examined to identify potential issues, such as skipped searches or missing information?

A. Web
B. Risk
C. Performance
D. Authentication

Answer: D. Authentication

Explanation:

The issue described, where the Remote Access panel on the User Activity dashboard is not updating with data from the most recent hour, suggests that there may be a delay or an error in data collection related to user access or login activity. In most cases, problems like this are linked to the Authentication data model. Here’s why:

Authentication Data Model: This model tracks login and access events of users. Any problem with the authentication process, such as missed or failed login attempts, could cause discrepancies in how recent activity is displayed on the dashboard. If authentication logs are skipped or delayed due to system errors, network issues, or improper data synchronization, it can result in incomplete or outdated information being displayed in the Remote Access panel.
Role of Authentication in User Activity: Since the Remote Access panel is focused on user activity related to access (remote logins, VPN access, and so on), the data required to populate this panel is closely tied to the authentication process. If there's a failure in capturing or recording these authentication events, the data in the dashboard will not update as expected.
Other Data Models:

Web: The Web data model would be more relevant for tracking web page activity, clicks, or user interactions on websites. It is unlikely to be the cause of issues with remote access or authentication data.
Risk: The Risk data model focuses on identifying and analyzing potential security threats or incidents. While it might provide insight into abnormal behavior or security-related issues, it wouldn't directly impact the real-time display of user login data.
Performance: This data model monitors system performance, including server load, response times, and other metrics. While system performance can influence the overall functioning of dashboards, it’s not the primary cause of missing authentication data.

Conclusion: To resolve the issue, administrators should focus on the Authentication data model. They should check for any errors in the authentication logs, such as failed login attempts, system delays, or misconfigured settings that might prevent real-time data from being captured.

Question No 6:

In the process of incorporating an event type into a data model node within a data model in Splunk, what is the correct next step after you have successfully extracted the relevant fields?

A. Save the settings.
B. Apply the correct tags.
C. Run the correct search.
D. Visit the CIM dashboard.

Answer: The correct answer is B. Apply the correct tags.

Explanation:

When working with Splunk's Data Model and Common Information Model (CIM), it is important to structure your data effectively so that it can be used for accurate searches and analysis. Here’s a breakdown of the steps:

Extracting Fields: The first step involves identifying and extracting the relevant fields from your raw event data. This step ensures that Splunk can understand and index the data appropriately.
Apply the Correct Tags: After extracting the correct fields, the next step is to apply the correct tags to the data model node. Tags in Splunk are used to label event types, making it easier for users to categorize and identify specific data types. Tags help the system understand the relationship between different data points, making it simpler to work with data in searches, reports, and dashboards. Without appropriate tagging, the data may not be categorized correctly, making it difficult to analyze or correlate events across datasets.
Why Other Options Are Incorrect:

A. Save the settings: While saving settings is an essential part of managing configurations in Splunk, it is not the immediate next step after field extraction. The key action after field extraction is to ensure proper tagging to allow accurate classification and correlation of the data.
C. Run the correct search: Running a search may be done later to verify that the tags and fields are correctly applied, but applying the tags comes before running searches. You must ensure the data is properly tagged before making search queries to ensure that the correct information is retrieved.
D. Visit the CIM dashboard: The CIM dashboard is useful for viewing and managing the CIM compatibility of your data model. However, it is not directly related to the step after field extraction. The CIM dashboard is generally used to monitor and adjust settings related to the CIM, but you must apply the tags before reviewing them through the CIM dashboard.

By correctly tagging the data, you ensure that it is compatible with the event type and can be efficiently utilized in Splunk searches and reporting. Proper tagging also allows the system to apply the CIM knowledge objects, ensuring data is standardized across various Splunk apps and environments.

Question No 7:

What is the appropriate role for a security team member who will be responsible for taking ownership of notable events in the incident review dashboard?

A. ess_user
B. ess_admin
C. ess_analyst
D. ess_reviewer

Correct Answer: C. ess_analyst

Explanation:

In a security information and event management (SIEM) system, the "incident review dashboard" is a critical tool that helps security teams monitor and assess notable security events, such as potential breaches or suspicious activities. These events need to be reviewed and acted upon promptly to ensure the integrity and security of the system or network.

Each role in a SIEM system has specific responsibilities tied to the level of access and actions they are allowed to perform. Let's break down the roles to understand which one is most appropriate for a security team member who will take ownership of notable events.

A. ess_user: This is typically the role for general users who may have limited access to the system. They can view events or dashboards but are not granted permissions to actively manage or respond to incidents. A user with this role cannot take ownership of events or make changes in the system. Thus, this is not the correct choice.
B. ess_admin: The "ess_admin" role is generally assigned to administrators who have full access to configure the system, manage users, and oversee all security activities. While an admin could technically take ownership of events, their primary responsibility is system configuration and overall administration, not direct engagement with specific incidents. This role goes beyond what's necessary for taking ownership of individual events.
C. ess_analyst: The "ess_analyst" role is specifically designed for security analysts responsible for investigating, reviewing, and responding to security events. Analysts are the ones who actively take ownership of notable events in the dashboard, assess their significance, perform root cause analysis, and decide on remediation actions. This is the most appropriate role for someone responsible for managing notable security events.
D. ess_reviewer: The "ess_reviewer" role typically implies someone who reviews the outcome of investigations or actions taken by others. While this role may involve oversight, it does not involve taking ownership or actively managing events. A reviewer might evaluate incident responses but does not engage in the investigation or resolution process directly.

Therefore, the ess_analyst role is best suited for a team member tasked with taking ownership of notable events, as they are responsible for performing detailed analyses and driving the response to security incidents.

Question No 8:

In security monitoring and event management systems, which column in the Asset or Identity list is typically combined with event security data to determine the urgency of a notable event?

A. VIP
B. Priority
C. Importance
D. Criticality

Correct Answer: B. Priority

Explanation:

In event security systems, particularly in environments where security incidents are logged, assessed, and prioritized, it's essential to assess the severity or urgency of the events in question. These systems often rely on various factors such as the nature of the asset or identity involved, the criticality of the event, and other contextual data to evaluate and assign urgency to an incident.

The Priority column in the Asset or Identity list plays a crucial role in this process. It is a key variable in determining how quickly a response is needed for a particular event. This column indicates the level of importance or urgency associated with an asset or identity in the context of an event. When combined with other event security data, it helps to escalate or de-escalate the event’s urgency.

For instance, if a security event involves a high-priority asset (such as a server hosting sensitive data), it is more likely to be treated with urgency compared to an event involving a low-priority asset. This prioritization helps security teams allocate resources effectively, respond quickly to high-priority threats, and ensure that incidents are handled based on their potential impact.

VIP (A) might refer to assets that are designated as "Very Important People" or critical users, but it does not directly help in defining the urgency of an event in the same structured way as Priority does.
Importance (C), although relevant to the nature of the asset, is a less common term used in security event management compared to "Priority," which is standardized and understood as a specific level or categorization of urgency.
Criticality (D) typically refers to the degree of an asset's significance or impact on business operations but is often used in risk management rather than directly influencing event response urgency.

In conclusion, the Priority column is specifically designed to support the urgency determination process when assessing security events, guiding the rapid allocation of attention and resources to critical incidents.

Question No 9:

What does a risk management framework typically assign to an object (such as a user, server, or other type) to indicate that it has a higher level of risk?

A. Urgency
B. Risk profile
C. Aggregation
D. Numeric score

Correct Answer: D. Numeric score

Explanation:

In a risk management framework, objects like users, servers, or other system components are often evaluated to determine their level of risk. One common way to indicate the risk level is through the assignment of a numeric score. This score is typically based on various risk factors such as user behavior, system vulnerabilities, the potential impact of a security breach, and more.

A numeric score is an effective method for quantifying risk because it allows for a clear, standardized representation of the potential threat level. This score can be calculated based on several risk criteria, such as:

Threat likelihood: The probability that a particular threat (e.g., unauthorized access) will occur.
Impact severity: How severe the consequences would be if the threat were realized (e.g., data breach, loss of reputation).
Vulnerability: The susceptibility of the object (user, server, etc.) to the identified threat.

Once these factors are combined, the result is often expressed as a numeric score that reflects the overall risk level. This score makes it easier for security teams to prioritize resources and interventions, addressing the highest-risk items first.

While options like urgency, risk profiles, and aggregation are also relevant in risk management, they serve different purposes. Urgency typically refers to how quickly a risk needs to be addressed, risk profiles provide a broader understanding of risk for an individual or entity, and aggregation refers to the combining of multiple risks into a single risk assessment. However, none of these are as directly tied to quantifying the risk level of an object as a numeric score.

By using a numeric score, risk management becomes a more data-driven, actionable process, allowing organizations to make more informed decisions about how to mitigate potential threats.

Question No 10:

What are the default indexes searched for CIM (Common Information Model) data models in Splunk?

A. notable and default
B. summary and notable
C. _internal and summary
D. All indexes

Answer: B. summary and notable

Explanation:

In Splunk, the Common Information Model (CIM) is a standardized framework for organizing and structuring machine data, allowing for better searches, correlation, and reporting across various data sources. When you perform searches or queries related to CIM data models, Splunk looks at specific indexes by default to gather relevant information. The correct default indexes that are searched for CIM data models are summary and notable.

Summary Index: The summary index is a special type of index used to store summarized data, often created by scheduled searches or data transformation. When dealing with CIM data models, the summary index is typically used to store aggregated or pre-processed event data, which speeds up search performance. It’s beneficial for dealing with large volumes of data because searching through pre-summarized data is much faster than querying raw event data.
Notable Index: The notable index is commonly associated with notable events, often linked to security incidents or alerts in a security information and event management (SIEM) system like Splunk. The CIM data models can reference the notable index when dealing with events that require further investigation or action, such as suspicious or anomalous activity.

Together, these two indexes—summary and notable—are optimized for CIM searches to enhance performance and provide relevant results in situations such as security monitoring, incident response, and IT operations.

The other options, such as "notable and default," "internal and summary," or "all indexes," are not correct as they don’t accurately reflect the default behavior of CIM data models, which is focused specifically on the summary and notable indexes.