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| Section | Weight | Objectives |
|---|---|---|
| Data Ingestion | 20% | - Troubleshooting ingestion and connectivity issues - Ingestion methods and integration strategies - Connector components and management - First-party vs third-party data sources - Fleet management and log collector deployment - Built-in and custom data connector configuration |
| Content Creation | 20% | - First-party vs third-party detections - Content deployment and version control - Correlation rules creation, tuning and management - CQL query design, building and optimization - Dashboard creation and customization - Lookup file management and utilization |
| User Management | 20% | - Multi-factor authentication (MFA) setup - Repository-level access control - Custom role creation and permission assignment - SSO/SAML configuration and claim mapping - Role-based access control (RBAC) and built-in roles - Audit log monitoring and usage |
| Parsing | 20% | - CrowdStrike Parsing Standards and normalization - Parser testing and validation - AI-generated parsers and advanced syntax - Log format identification and handling - Monitoring and resolving parsing errors - Parser creation, modification and cloning |
| Automation and Integration | 20% | - Automated response and remediation - API access and token management - Integration with FalconPy and other tools - Falcon Fusion SOAR workflow design and automation - External system integration |
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NEW QUESTION # 58
An organization wants to detect command-and-control beaconing behavior characterized by periodic outbound connections to suspicious domains at regular intervals.
Answer: A
Explanation:
Beaconing patterns require behavioral detection, not just static IOC matching.
NEW QUESTION # 59
An internal security team identified a small number of high-risk users. They ask you to create an app that will monitor these users and trigger an alert when specific suspicious behavior is detected.
Which Falcon feature should you use to develop this app?
Answer: B
Explanation:
Falcon Foundry allows you to develop custom applications and detection logic within the Falcon platform, enabling monitoring of specific users and triggering alerts based on defined suspicious behaviors.
NEW QUESTION # 60
You have been tasked with parsing the following space-delimited log:
2025-06-03 12:13:07 johndoe 192.168.5.15 login
The log source data is guaranteed to always be in the same order.
Which function can parse this log?
Answer: D
Explanation:
The correct answer is C. parseCsv() .
CrowdStrike LogScale documentation for parseCsv() states that the function supports a configurable delimiter parameter, and it is used to split a field into named columns. Because this log is space-delimited and the values are always in the same order, parseCsv() is the appropriate parser function by specifying a space as the delimiter and naming the columns in order.
Why the other options are incorrect:
* A. parseCEF() is for CEF-formatted logs, which this event is not.
* B. parseJson() is for JSON, and this event is plain text.
* D. parseFixedWidth() is meant for logs where each field occupies a strict character width.
CrowdStrike's docs describe it as valuable when data must maintain strict positional formatting and defined field lengths. This question only guarantees field order , not fixed character widths, so parseFixedWidth() is not the best match.
NEW QUESTION # 61
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?
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 # 62
Which metric best reflects how quickly a SIEM-enabled SOC can respond to detected threats from identification to remediation?
Answer: C
Explanation:
MTTR (Mean Time to Respond) measures response speed.
NEW QUESTION # 63
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