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CrowdStrike CCSE-204 Exam Syllabus Topics:

Section	Objectives
Topic 1: Exam domains (official detailed syllabus not publicly disclosed)	- Security event ingestion, normalization, and correlation concepts - 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 - Dashboards, reporting, and alerting configuration

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CrowdStrike Certified SIEM Engineer Sample Questions (Q61-Q66):

NEW QUESTION # 61
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. Add a condition to exclude known trusted IP addresses from triggering the rule
C. Decrease the threshold for the number of failed login attempts required to trigger the rule
D. Remove the condition for a successful login to simplify the rule

Answer: B

Explanation:
The correct answer is B . The best tuning step is to exclude known trusted IP addresses so the rule still detects suspicious sequences while removing known-benign sources of repeated authentication activity.
CrowdStrike has publicly documented this tuning principle in detection content guidance, noting that to avoid false positives, organizations may want to exclude certain IP ranges, ASNs, or ISPs from a rule when those sources are expected or trusted. That directly supports the idea that adding a trusted-IP exclusion reduces noise while preserving the core detection logic.
Why the other options are incorrect:
A would usually increase noise because a larger time window captures more benign failed logins. C would also increase false positives because lowering the failed-attempt threshold makes the rule easier to trigger. D weakens the original attack logic by removing the "failed logins followed by success" sequence that makes the rule more specific and meaningful. Keeping the core sequence intact while adding exclusions for known benign sources is the most precise tuning approach.

NEW QUESTION # 62
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" | stdout=/\[[\+]\] / AND groupBy([hostname], function=collect([hostname, stdout] )) AND stdout ! = "" | stdout != "* No artifacts *" | select([hostname,stdout])
C. #sourcefile="jobfilename" AND stdout=/\[[\+]\] / | groupBy([hostname], function=collect([hostname, stdout] )) AND stdout != "" AND stdout != "* No artifacts *" | select([hostname,stdout])
D. #sourcefile="jobfilename" AND stdout=/\[[\+]\] / | groupBy([hostname], function=collect([hostname, stdout] )) | stdout != "" AND stdout != "* No artifacts *" | select([hostname,stdout])

Answer: D

Explanation:
The correct answer is C . In CQL, boolean conditions such as AND belong inside filter expressions, while pipeline functions like groupBy() and select() must be separated with the pipe (|) operator. CrowdStrike syntax guidance shows that functions are chained through the pipeline and should not be combined with AND. Option C correctly uses AND for the filter logic and uses pipes to separate the aggregation and projection steps.

NEW QUESTION # 63
A SIEM rule generates excessive false positives due to normal administrative activities triggering alerts similar to malicious behavior.

A. Disable logging
B. Increase alert severity
C. Block all admin accounts
D. Tune detection rules and add context

Answer: D

Explanation:
Rule tuning with contextual filters reduces false positives without losing detection capability.

NEW QUESTION # 64
Which CQL statement below includes correct placement of the AND statements and the pipe symbol?

A. #sourcefile="jobfilename" AND stdout=/\[[\+]\]/ | groupBy([hostname],
function=collect([hostname,stdout])) AND stdout != "" AND stdout != "*
No artifacts *" | 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=/\[[\+]\]/ | groupBy([hostname],
function=collect([hostname,stdout])) | stdout != "" AND stdout != "* No artifacts *" AND select([hostname,stdout])
D. #sourcefile="jobfilename" | stdout=/\[[\+]\]/ AND groupBy([hostname],
function=collect([hostname,stdout])) AND stdout ! = "" | 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 # 65
In the Next-Gen SIEM Connector Dashboard, what is the maximum retention period for which you can query third-party data ingestion metrics?

A. 60 days
B. 90 days
C. 180 days
D. 30 days

Answer: B

Explanation:
In the Next-Gen SIEM Connector Dashboard (specifically within the CrowdStrike Falcon ecosystem), the maximum retention period for which you can query third-party data ingestion metrics is 90 days .
Why 90 Days?
While the actual log data (telemetry) in a Next-Gen SIEM can often be retained for a year or longer depending on the subscription (e.g., 365 days), the health and ingestion metrics -which include data such as volume throughput, connector status, and ingestion rates-are typically stored for a shorter duration. This
90-day window is designed to provide enough historical context for:
* Troubleshooting: Identifying when a specific connector started failing.
* Trend Analysis: Monitoring changes in data volume over a fiscal quarter.
* Capacity Planning: Reviewing average ingestion rates to ensure they stay within licensed limits.

NEW QUESTION # 66
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