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CrowdStrike CCSE-204 Exam Syllabus Topics:

Section	Weight	Objectives
Content Creation	20%	- Correlation rules creation, tuning and management - Lookup file management and utilization - CQL query design, building and optimization - Dashboard creation and customization - First-party vs third-party detections - Content deployment and version control
Data Ingestion	20%	- Built-in and custom data connector configuration - Ingestion methods and integration strategies - Connector components and management - Fleet management and log collector deployment - First-party vs third-party data sources - Troubleshooting ingestion and connectivity issues
Parsing	20%	- Parser creation, modification and cloning - AI-generated parsers and advanced syntax - Parser testing and validation - CrowdStrike Parsing Standards and normalization - Log format identification and handling - Monitoring and resolving parsing errors
Automation and Integration	20%	- Falcon Fusion SOAR workflow design and automation - External system integration - API access and token management - Integration with FalconPy and other tools - Automated response and remediation
User Management	20%	- Audit log monitoring and usage - Multi-factor authentication (MFA) setup - Custom role creation and permission assignment - Role-based access control (RBAC) and built-in roles - Repository-level access control - SSO/SAML configuration and claim mapping

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CrowdStrike Certified SIEM Engineer Sample Questions (Q62-Q67):

NEW QUESTION # 62
What are the four required CPS-compliant Event parser tags?

A. event.category
event.dataset
event.kind
event.outcome
B. event.category
event.kind
event.module
event.outcome
C. event.dataset
event.kind
event.module
event.outcome

Answer: A

Explanation:
CrowdStrike Parsing Standard (CPS) requires these four tags to classify and normalize events consistently, ensuring compatibility across parsers and enabling accurate correlation and reporting in Next-Gen SIEM.

NEW QUESTION # 63
An attacker uses legitimate administrative tools like PowerShell and WMI to avoid detection while moving laterally within the network.

A. File-based malware detection
B. Living-off-the-land techniques
C. Phishing
D. DDoS

Answer: B

Explanation:
These techniques use legitimate tools to evade detection (LOLBins).

NEW QUESTION # 64
Review the log event below:
{"ts": "2018/11/01 14:31:10", "server": "web01", "message": "Out of memory"} Which parsing function is correct to add a missing timezone field?

A. parseJson() | parseTimestamp("dd/MMM/yyyy:HH:mm:ss Z", timezone="Europe/Paris", field=ts)
B. kvParse() | findTimestamp(field=ts, timezone="Europe/London")
C. kvParse() | findTimestamp(timezone="America/New_York")
D. parseJson() | parseTimestamp("yyyy/MM/dd HH:mm:ss", timezone="Europe/Paris", field=ts)

Answer: D

Explanation:
The correct answer is D . CrowdStrike LogScale's timestamp parsing documentation gives this exact pattern as the example for a JSON event whose ts field contains 2018/11/01 14:31:10 with no timezone present. The documented solution is:
parseJson() | parseTimestamp("yyyy/MM/dd HH:mm:ss", timezone="Europe/Paris", field=ts) This works because the event is JSON, so parseJson() is the right first step, and the timestamp format matches the sample exactly. Since the timestamp string does not include timezone information, CrowdStrike documentation says you must provide a timezone parameter to parseTimestamp().
Why the other options are incorrect:
A is wrong because the format string does not match the timestamp. The event uses 2018/11/01 14:31:10, which is yyyy/MM/dd HH:mm:ss, not dd/MMM/yyyy:HH:mm:ss Z. Also, the sample timestamp does not include a Z timezone token in the raw string. B and C are wrong because kvParse() is for key-value logs, not JSON logs, and this event is clearly JSON. CrowdStrike's built-in parser documentation distinguishes JSON parsing from KV parsing, and the timestamp example for missing timezone specifically uses parseJson() with parseTimestamp().

NEW QUESTION # 65
What is the primary benefit of utilizing Next-Gen SIEM's built-in dashboards?

A. Direct access to raw log data
B. Quick insights without manual setup
C. Custom queries for specific events
D. Capability to modify dashboard source code

Answer: B

Explanation:
The correct answer is C. Quick insights without manual setup .
CrowdStrike describes Falcon Next-Gen SIEM as providing pre-built dashboards and says teams can quickly understand security and system health with prebuilt dashboards for data collection health, SOAR workflow executions, security trends, and more. That directly supports the idea that the main benefit is getting fast visibility and insights without having to build everything manually first .
Why the other options are incorrect:
A is incorrect because dashboards are for visualization and insight, not primarily for raw log access. B is incorrect because custom queries are a separate search capability, not the main value proposition of built-in dashboards. D is incorrect because CrowdStrike emphasizes using pre-built and custom dashboards for visualization, not modifying dashboard source code as the primary benefit.

NEW QUESTION # 66
Review the log event below:
{"ts": "2018/11/01 14:31:10", "server": "webOl", "message": "Out of
memory"}
Which parsing function is correct to add a missing timezone field?
parseJson() | parseTimestamp("dd/MMM/yyyy:HH:mm:ss Z",

A. kvParse() | findTimestamp(timezone="America/New_York")
B. parseJson() | parseTimestamp("yyyy/MM/dd HH:mm:ss",
C. timezone="Europe/Paris", field=ts)
kvParse() | findTimestamp(field=ts, timezone="Europe/London")
D. timezone="Europe/Paris", field=ts)

Answer: D

Explanation:
The log is in JSON format, so parseJson() is needed to extract fields.
The timestamp format matches yyyy/MM/dd HH:mm:ss, and adding the timezone parameter assigns the missing timezone correctly.

NEW QUESTION # 67
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