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CrowdStrike CCSE-204 Exam Syllabus Topics:

Section	Weight	Objectives
Dashboards and Reporting	20%	- Visualization Techniques 1. Dashboard creation 2. Report scheduling
Log Management and Data Collection	25%	- Data Sources and Connectors 1. Third-party integrations 2. Cloud-native log sources - Data Normalization 1. Common Information Model (CIM) 2. Parsing rules
Administration and Maintenance	25%	- Access Control 1. Authentication methods 2. Role-based access - System Health Monitoring 1. Performance tuning 2. Storage management
Search and Investigation	30%	- Search Processing Language (SPL) 1. Basic search commands 2. Statistical functions - Incident Investigation 1. Timeline analysis 2. Evidence gathering

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CrowdStrike Certified SIEM Engineer Sample Questions (Q21-Q26):

NEW QUESTION # 21
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. kvParse() | findTimestamp(field=ts, timezone="Europe/London")
B. parseJson() | parseTimestamp("dd/MMM/yyyy:HH:mm:ss Z", timezone="Europe/Paris", field=ts)
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 # 22
Which CQL statement below includes correct placement of the AND statements and the pipe symbol?

A. #sourcefile="jobfilename" | stdout=/\[[\+]\]/ | groupBy([hostname],
function=collect([hostname,stdout])) | stdout != "" AND stdout != "* No artifacts *" AND select([hostname,stdout])
B. #sourcefile="jobfilename" AND stdout=/\[[\+]\]/ | groupBy([hostname],
function=collect([hostname,stdout])) | stdout != "" AND stdout != "* No artifacts *" | select([hostname,stdout])
C. #sourcefile="jobfilename" | stdout=/\[[\+]\]/ AND groupBy([hostname],
function=collect([hostname,stdout])) AND stdout ! = "" | stdout != "* No artifacts *" | select([hostname,stdout])
D. #sourcefile="jobfilename" AND stdout=/\[[\+]\]/ | groupBy([hostname],
function=collect([hostname,stdout])) AND stdout != "" AND stdout != "*
No artifacts *" | select([hostname,stdout])

Answer: B

Explanation:
In CQL, filters combined with AND are applied before the pipe (|) operator, which is used to chain functions like groupBy and select. This syntax correctly places the AND conditions for filtering and pipes for processing steps.

NEW QUESTION # 23
You notice that the format of incoming logs suddenly changes from JSON format to key-value pairs during log collection.
What action would you take to parse the data correctly?

A. Restart the log collector in debug mode
B. Use a multi-source configuration with different parsers per source
C. Switch to fleet mode and monitor the logs
D. Disable parsing entirely

Answer: B

Explanation:
The correct answer is A. Use a multi-source configuration with different parsers per source .
CrowdStrike's Falcon LogScale Collector documentation states that parsers can be set for each source . The collector configuration model also explains that the Sources section defines the source of the data, filters to be applied, and parsers . That means when different log formats are being collected, the correct design is to separate them by source and assign the appropriate parser to each source.
Why the other options are incorrect:
Switching to fleet mode or monitoring logs does not itself correct parsing logic. Restarting in debug mode may help troubleshoot, but it does not solve the format mismatch. Disabling parsing would make the data less useful, not more useful. The documented way to handle parser differences is to apply parsers at the source level.

NEW QUESTION # 24
Which command helps visualize in real time whether sources and sinks are working properly in the Log Collector?

A. logscale-collector monitor
B. logscale-collector --status
C. logscale-collector check
D. journalctl -u logscale-collector

Answer: A

Explanation:
The logscale-collector monitor command provides a real-time view of the Log Collector's operation, showing the status of sources and sinks to help ensure data is being ingested and processed correctly.

NEW QUESTION # 25
As a Next-Gen SIEM Engineer, you are responsible for managing and tuning correlation rules to improve the detection of potential security incidents. One of your correlation rules is designed to detect multiple failed login attempts that are followed by a successful login within a short time frame.
Which step would you take to tune this correlation rule to reduce false positives while maintaining its effectiveness?

A. Increase the time window for detecting multiple failed login attempts to capture more data
B. Remove the condition for a successful login to simplify the rule
C. Add a condition to exclude known trusted IP addresses from triggering the rule
D. Decrease the threshold for the number of failed login attempts required to trigger the rule

Answer: C

Explanation:
Excluding trusted IP addresses helps reduce false positives caused by legitimate user activity while keeping the rule effective at detecting suspicious login patterns from unknown or untrusted sources.

NEW QUESTION # 26
......

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