Test SPLK-5002 Collection - Exam SPLK-5002 Practice

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Splunk SPLK-5002 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Data Engineering: This section of the exam measures the skills of Security Analysts and Cybersecurity Engineers and covers foundational data management tasks. It includes performing data review and analysis, creating and maintaining efficient data indexing, and applying Splunk methods for data normalization to ensure structured and usable datasets for security operations.
Topic 2
  • Detection Engineering: This section evaluates the expertise of Threat Hunters and SOC Engineers in developing and refining security detections. Topics include creating and tuning correlation searches, integrating contextual data into detections, applying risk-based modifiers, generating actionable Notable Events, and managing the lifecycle of detection rules to adapt to evolving threats.
Topic 3
  • Automation and Efficiency: This section assesses Automation Engineers and SOAR Specialists in streamlining security operations. It covers developing automation for SOPs, optimizing case management workflows, utilizing REST APIs, designing SOAR playbooks for response automation, and evaluating integrations between Splunk Enterprise Security and SOAR tools.
Topic 4
  • Auditing and Reporting on Security Programs: This section tests Auditors and Security Architects on validating and communicating program effectiveness. It includes designing security metrics, generating compliance reports, and building dashboards to visualize program performance and vulnerabilities for stakeholders.
Topic 5
  • Building Effective Security Processes and Programs: This section targets Security Program Managers and Compliance Officers, focusing on operationalizing security workflows. It involves researching and integrating threat intelligence, applying risk and detection prioritization methodologies, and developing documentation or standard operating procedures (SOPs) to maintain robust security practices.

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Splunk Certified Cybersecurity Defense Engineer Sample Questions (Q86-Q91):

NEW QUESTION # 86
What is the purpose of using data models in building dashboards?

Answer: A

Explanation:
Why Use Data Models in Dashboards?
SplunkData Modelsallow dashboards toretrieve structured, normalized data quickly, improving search performance and accuracy.
#How Data Models Help in Dashboards?(AnswerB)#Standardized Field Naming- Ensures that queries always useconsistent field names(e.g.,src_ipinstead ofsource_ip).#Faster Searches- Data models allow dashboards torun structured searches instead of raw log queries.#Example:ASOC dashboard for user activity monitoringuses a CIM-compliantAuthentication Data Model, ensuring that querieswork 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 modelsstructuredata but donot perform compression.#D. To reduce storage usage on Splunk instances- Data modelshelp with search performance, not storage reduction.
References & 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


NEW QUESTION # 87
What does Splunk's term "bucket" refer to in data indexing?

Answer: B


NEW QUESTION # 88
Risk scores are associated with how many levels of risk in Enterprise Security by default?

Answer: A

Explanation:
By default, Splunk Enterprise Security associates risk scores with five levels: Info, Low, Medium, High, and Critical. These levels help prioritize security events and focus analyst attention on the most impactful risks.


NEW QUESTION # 89
Based on this example image, if it is detected that a member has been added to a security- enabled local group, how many risk events will be created?

Answer: A

Explanation:
In the example, there are two risk modifiers configured: one for the system (src) and one for the user. Each modifier creates a separate risk event with a score of 10. Therefore, the detection will generate 2 risk events in total.


NEW QUESTION # 90
When creating a detection that searches user activity across CIM-compliant data, which CIM field should be reviewed to ensure that data is aggregated appropriately?

Answer: B

Explanation:
The user field is the normalized CIM field for user activity across data sources. Reviewing and using this field ensures that data from different sources is properly aggregated, enabling consistent detection logic across CIM-compliant datasets.


NEW QUESTION # 91
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