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Splunk SPLK-5001 Exam Syllabus Topics:

Topic	Details
Topic 1	Monitoring and Performance Tuning: The Monitoring and Performance Tuning section addresses strategies for overseeing and optimizing the performance of a Splunk deployment.
Topic 2	Troubleshooting and Maintenance: The Troubleshooting and Maintenance section focuses on diagnosing and resolving issues within a Splunk deployment. This involves using diagnostic tools and logs to troubleshoot common problems such as data ingestion issues, search performance, and system errors.
Topic 3	Data Integration and Apps: The Data Integration and Apps section explores how to integrate Splunk with other systems and utilize Splunk apps to extend its functionality. This includes integrating Splunk with external data sources and third-party applications, as well as configuring data inputs and outputs.
Topic 4	Data Management and Indexing: The Data Management and Indexing section explores how Splunk processes data ingestion and indexing. It details the data pipeline, covering the stages of data collection, parsing, and indexing. This section also includes configuring data inputs and indexing settings, as well as managing indexing performance and data retention policies.
Topic 5	User Management and Security: The User Management and Security section focuses on controlling user access and securing the Splunk environment. It covers how to set up roles and permissions to manage access to Splunk features and data. This includes user authentication methods, such as integrating with external systems and managing user accounts. The section also discusses security best practices to protect against unauthorized access and ensure data confidentiality and integrity.
Topic 6	Installation and Configuration: In the Installation and Configuration section, the focus is on the procedures for installing and setting up Splunk Enterprise. This includes the installation process across different operating systems and the configuration of necessary components to ensure proper functionality. Key topics include installing the Splunk software, setting up the Deployment Server, and configuring Data Inputs for data collection and indexing.

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Splunk Certified Cybersecurity Defense Analyst Sample Questions (Q118-Q123):

NEW QUESTION # 118
Which tool can a SOC analyst use to explore existing SPL searches that might be helpful during investigations?

A. Splunk SOAR
B. Splunk Security Essentials
C. MITRE ATT&CK
D. SPL Editor App

Answer: B

Explanation:
Splunk Security Essentials features a built-in Search Library that lets analysts browse and preview hundreds of vetted SPL searches - organized by use case and security domain - so they can quickly find queries relevant to their investigation.

NEW QUESTION # 119
What is the main difference between hypothesis-driven and data-driven Threat Hunting?

A. Data-driven hunts always require more data to search through than hypothesis-driven hunts.
B. Data-driven hunting tries to uncover activity within an existing data set, hypothesis-driven hunting begins with a potential activity that the hunter thinks may be happening.
C. Hypothesis-driven hunting tries to uncover activity within an existing data set, data-driven hunting begins with an activity that the hunter thinks may be happening.
D. Hypothesis-driven hunts are typically executed on newly ingested data sources, while data-driven hunts are not.

Answer: B

NEW QUESTION # 120
What feature of Splunk Security Essentials (SSE) allows an analyst to see a listing of current on-boarded data sources in Splunk so they can view content based on available data?

A. Data Source Onboarding Guides
B. Security Content
C. Security Data Journey
D. Data Inventory

Answer: D

NEW QUESTION # 121
According to David Bianco's Pyramid of Pain, which indicator type is least effective when used in continuous monitoring?

A. TTPs
B. NetworM-lost artifacts
C. Domain names
D. Hash values

Answer: D

NEW QUESTION # 122
After discovering some events that were missed in an initial investigation, an analyst determines this is because some events have an empty src field. Instead, the required data is often captured in another field called machine_name.
What SPL could they use to find all relevant events across either field until the field extraction is fixed?

A. | eval src = tostring(machine_name)
B. | eval src = coalesce(src,machine_name)
C. | eval src = src + machine_name
D. | eval src = src . machine_name

Answer: B

NEW QUESTION # 123
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