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CrowdStrike CCSE-204 Exam Syllabus Topics:

Section	Objectives
Topic 1: Exam domains (official detailed syllabus not publicly disclosed)	- Dashboards, reporting, and alerting configuration - Operational use of CrowdStrike Falcon modules for SIEM engineering tasks - Threat detection and incident investigation workflows in CrowdStrike platform - CrowdStrike SIEM and log analysis fundamentals - Security event ingestion, normalization, and correlation concepts

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CrowdStrike Certified SIEM Engineer Sample Questions (Q66-Q71):

NEW QUESTION # 66
You notice a larger than expected ingest delay from one of your high-volume streaming log collectors.
Which setting should you increase on the log collector to improve performance?

A. Default memory queue size
B. Amount of available disk space
C. Number of concurrent requests a sink is using
D. Available source throughput

Answer: A

Explanation:
Increasing the memory queue size allows the log collector to buffer more events in memory before processing or forwarding, reducing ingest delays for high-volume streams and improving overall performance.

NEW QUESTION # 67
What dashboard presents a view of third-party data ingestion over the past 30 days?

A. Sensor Usage Dashboard
B. Next-Gen SIEM Connector Dashboard
C. Sensor Subscription Dashboard
D. Falcon Flex Dashboard

Answer: B

Explanation:
The Next-Gen SIEM Connector Dashboard provides visibility into third-party data ingestion, showing metrics such as volume, trends, and connector health over time, including the past 30 days.

NEW QUESTION # 68
You are creating an AI-generated parser to process and normalize log data from various sources.
How would you ensure the parser accurately interprets and categorizes the log data?

A. Ensure the parser has a minimum of 100 lines
B. Write the parser in a high-level programming language (Python or Java)
C. Create a set of log examples to match log patterns from different sources

Answer: C

Explanation:
Providing representative log examples allows an AI-generated parser to learn the structure and patterns of different log formats, ensuring accurate field extraction and normalization across diverse data sources.

NEW QUESTION # 69
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. timezone="Europe/Paris", field=ts)
kvParse() | findTimestamp(field=ts, timezone="Europe/London")
B. parseJson() | parseTimestamp("yyyy/MM/dd HH:mm:ss",
C. kvParse() | findTimestamp(timezone="America/New_York")
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 # 70
You are configuring third-party data for ingestion. Once a connection is established, you see the HTTP response code 413 as received by your data shipper.
What does this response code indicate?

A. This transient error might occur in rare cases. Wait and retry the request.
B. Bad request. Connection is accessing non-existent endpoints.
C. Bad request. Might indicate invalid data format or no data.
D. Bad request. The payload size exceeds the allowed limit.

Answer: D

Explanation:
HTTP 413 indicates that the request entity is too large. In the context of data ingestion, this means the payload sent by the data shipper exceeds the maximum size allowed by the receiving endpoint.

NEW QUESTION # 71
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