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CrowdStrike CCSE-204 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Log Management and Data Collection25%- Data Normalization
  • 1. Common Information Model (CIM)
  • 2. Parsing rules
- Data Sources and Connectors
  • 1. Third-party integrations
  • 2. Cloud-native log sources
Topic 2: Administration and Maintenance25%- System Health Monitoring
  • 1. Storage management
  • 2. Performance tuning
- Access Control
  • 1. Authentication methods
  • 2. Role-based access
Topic 3: Search and Investigation30%- Incident Investigation
  • 1. Timeline analysis
  • 2. Evidence gathering
- Search Processing Language (SPL)
  • 1. Basic search commands
  • 2. Statistical functions
Topic 4: Dashboards and Reporting20%- Visualization Techniques
  • 1. Report scheduling
  • 2. Dashboard creation

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CrowdStrike Certified SIEM Engineer Sample Questions (Q55-Q60):

NEW QUESTION # 55
A SIEM detects large volumes of outbound data transfers during non-business hours from a sensitive database server to an external IP address.

Answer: A

Explanation:
Unusual outbound data volume at odd times suggests exfiltration.


NEW QUESTION # 56
What is the maximum number of active correlation rules in a CID?

Answer: A

Explanation:
In a CrowdStrike instance (CID), the maximum number of active correlation rules that can be applied simultaneously is 500, ensuring system performance and manageability.


NEW QUESTION # 57
You have been tasked with parsing the following space-delimited log:
2025-06-03 12:13:07 johndoe 192.168.5.15 login
The log source data is guaranteed to always be in the same order.
Which function can parse this log?

Answer: B

Explanation:
The correct answer is C. parseCsv() .
CrowdStrike LogScale documentation for parseCsv() states that the function supports a configurable delimiter parameter, and it is used to split a field into named columns. Because this log is space-delimited and the values are always in the same order, parseCsv() is the appropriate parser function by specifying a space as the delimiter and naming the columns in order.
Why the other options are incorrect:
* A. parseCEF() is for CEF-formatted logs, which this event is not.
* B. parseJson() is for JSON, and this event is plain text.
* D. parseFixedWidth() is meant for logs where each field occupies a strict character width.
CrowdStrike's docs describe it as valuable when data must maintain strict positional formatting and defined field lengths. This question only guarantees field order , not fixed character widths, so parseFixedWidth() is not the best match.


NEW QUESTION # 58
When creating an API client for Falcon SIEM Connector, which permission is required for the connector to read Falcon event streams?

Answer: A

Explanation:
The Falcon SIEM Connector requires an API client with Read access to Event Streams . This permission allows the connector to authenticate to Falcon and receive streaming event data. Other permissions such as Hosts, Incidents, or Detection Management are not the required permission for establishing Falcon event- stream ingestion.
==========


NEW QUESTION # 59
You want a consistent view of events from various data sources.
Which ECS field type should you normalize?

Answer: A

Explanation:
Elastic's official ECS guidelines define Core fields as the fields most common across use cases and explicitly state that analysis content built on these fields should work properly on data from any relevant source. They also say to focus on populating these fields first . CrowdStrike's CPS builds on ECS and is intended to standardize field names and structures across different data sources for consistent searching and analysis.
Together, that makes Core fields the right answer when your goal is a consistent cross-source view.
Why the other options are incorrect:
* Extended fields are useful, but ECS defines them as anything not in the core set, so they are not the primary normalization target for broad consistency.
* Base fields and Detection fields are not the correct ECS field-type answer to this question as framed.


NEW QUESTION # 60
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