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Splunk SPLK-5001 Exam Syllabus Topics:

Topic	Details
Topic 1	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.
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	Splunk Architecture and Deployment: The Splunk Architecture and Deployment section offers a detailed understanding of Splunk’s structure and deployment methods. It covers the core components of Splunk Enterprise, such as the Indexer, Search Head, and Forwarder. This section involves examining the design of Splunk deployments, including how these components interact and their specific roles.
Topic 5	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.

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Splunk Certified Cybersecurity Defense Analyst Sample Questions (Q90-Q95):

NEW QUESTION # 90
In Splunk, what feature would an analyst leverage to drilldown on an IP address field to query third-party intelligence for that IP?

A. Workflow action
B. Adaptive Response action
C. Alert action
D. Notable drilldown

Answer: A

Explanation:
Workflow actions let you click on a field value, like an IP address, in search results or dashboards and invoke external lookups or queries (for example, sending the IP to a threat-intel service) directly from the Splunk UI.

NEW QUESTION # 91
An adversary uses "LoudWiner" to hijack resources for crypto mining. What does this represent in a TTP framework?

A. Tactic
B. Technique
C. Problem
D. Procedure

Answer: D

NEW QUESTION # 92
Which unit of a Security Operations team is focused on collaboration and the integration of defensive tactics and offensive results?

A. Purple team
B. Blue team
C. Orange team
D. Green team

Answer: A

Explanation:
The Purple team is responsible for integrating the efforts of both the Blue team (defensive operations) and the Red team (offensive operations). Their focus is on collaboration, ensuring that insights from offensive testing directly enhance defensive strategies and capabilities.

NEW QUESTION # 93
What is the main difference between hypothesis-driven and data-driven Threat Hunting?

A. 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.
B. 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.
C. Data-driven hunts always require more data to search through than hypothesis-driven hunts.
D. Hypothesis-driven hunts are typically executed on newly ingested data sources, while data-driven hunts are not.

Answer: A

NEW QUESTION # 94
As an analyst, tracking unique users is a common occurrence. The Security Operations Center (SOC) manager requested a search with results in a table format to track the cumulative downloads by distinct IP address. Which example calculates the running total of distinct users over time?
eventtype="download" | bin _time span=1d as day | table clientip day

A. user
eventtype="download" | bin _time span=1d as day | stats values(clientip)
B. as ips dc(clientip) by day
eventtype="download" | bin _time span=1d as day | stats values(clientip)
C. as ips dc(clientip) by day | streamstats dc(ips) as "Cumulative total"
D. as ips dc(clientip) by user | table day ips
eventtype="download" | bin _time span=1d as day | stats values(clientip)

Answer: C

Explanation:
This search first bins events by day and uses stats to list each day's distinct IPs (ips) and count them (dc(clientip)). The streamstats dc(ips) as "Cumulative total" then computes a running distinct count of all IPs seen up through each day, giving the cumulative total you need.

NEW QUESTION # 95
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