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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: User Management | 20% | - Role-based access control (RBAC) and built-in roles - SSO/SAML configuration and claim mapping - Audit log monitoring and usage - Custom role creation and permission assignment - Repository-level access control - Multi-factor authentication (MFA) setup |
| Topic 2: Data Ingestion | 20% | - Ingestion methods and integration strategies - Troubleshooting ingestion and connectivity issues - Connector components and management - Built-in and custom data connector configuration - First-party vs third-party data sources - Fleet management and log collector deployment |
| Topic 3: Content Creation | 20% | - First-party vs third-party detections - Correlation rules creation, tuning and management - Content deployment and version control - Dashboard creation and customization - CQL query design, building and optimization - Lookup file management and utilization |
| Topic 4: Parsing | 20% | - Log format identification and handling - Parser testing and validation - Monitoring and resolving parsing errors - Parser creation, modification and cloning - CrowdStrike Parsing Standards and normalization - AI-generated parsers and advanced syntax |
| Topic 5: Automation and Integration | 20% | - API access and token management - Falcon Fusion SOAR workflow design and automation - External system integration - Automated response and remediation - Integration with FalconPy and other tools |
>> Exam CrowdStrike CCSE-204 Answers <<
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NEW QUESTION # 65
A detection rule identifies suspicious parent-child process relationships commonly associated with malware execution techniques.
Answer: C
Explanation:
Malware often creates unusual parent-child process chains.
NEW QUESTION # 66
You are onboarding a log source that includes a timestamp with a different timezone.
How should you address any time parsing errors that occur?
Answer: D
Explanation:
The correct answer is A . CrowdStrike documentation states that when a timestamp does not include timezone information, or when you need to control timezone interpretation, you should pass the timezone parameter to parseTimestamp() or findTimestamp(). Since parsers are where ingest-time transformations are defined, the correct engineering approach is to create or clone a custom parser for that log source and explicitly apply the needed timezone handling there. CrowdStrike's custom parser docs explain that parsers are used to control how incoming events are transformed during ingest, and the timestamp parsing docs explain that timezone can be set directly in the parser logic.
Why the other options are incorrect:
B is not the documented parser-side solution. While changing the source may work operationally in some environments, CrowdStrike's parsing guidance focuses on fixing time interpretation in the parser by using timezone or related timestamp parsing controls. C is incorrect because changing the timestamp field name does not solve timezone parsing. D is incorrect because dropping the source timestamp and relying on ingest time would lose the original event time, which is exactly what parsers are meant to preserve by converting source timestamps into @timestamp. CrowdStrike explicitly states that one of the most important jobs of a parser is assigning correct timestamps to events.
NEW QUESTION # 67
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?
Answer: D
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 # 68
Which approach is most effective for reducing alert fatigue in a mature SIEM deployment while maintaining high detection fidelity?
Answer: C
Explanation:
Tuning and prioritization improve efficiency without sacrificing visibility.
NEW QUESTION # 69
You are creating a dashboard in Next-Gen SIEM and want to change the visualization used by a widget.
What must be selected to make this change?
Answer: B
Explanation:
In Next-Gen SIEM dashboards, Styling options allow you to change the visualization type of a widget, including charts, tables, and graphs, without modifying the underlying query.
NEW QUESTION # 70
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