Last Updated: Aug 06, 2026
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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: User Management | 20% | - Custom role creation and permission assignment - Repository-level access control - Multi-factor authentication (MFA) setup - Role-based access control (RBAC) and built-in roles - Audit log monitoring and usage - SSO/SAML configuration and claim mapping |
| Topic 2: Automation and Integration | 20% | - Automated response and remediation - Falcon Fusion SOAR workflow design and automation - External system integration - API access and token management - Integration with FalconPy and other tools |
| Topic 3: Data Ingestion | 20% | - Connector components and management - Built-in and custom data connector configuration - Fleet management and log collector deployment - Troubleshooting ingestion and connectivity issues - Ingestion methods and integration strategies - First-party vs third-party data sources |
| Topic 4: Parsing | 20% | - Monitoring and resolving parsing errors - CrowdStrike Parsing Standards and normalization - AI-generated parsers and advanced syntax - Parser creation, modification and cloning - Log format identification and handling - Parser testing and validation |
| Topic 5: Content Creation | 20% | - Lookup file management and utilization - First-party vs third-party detections - Content deployment and version control - Dashboard creation and customization - Correlation rules creation, tuning and management - CQL query design, building and optimization |
1. You clone a default parser and modify only the parseTimestamp()function to accommodate custom time format in your logs.
What is the impact on queries that search for this data?
A) The #Cps.versionfield will need to be updated
B) The #typefield will need to be updated
C) The # character needs to be removed from tagged fields as cloning the parser removes all tagged fields
D) No changes are necessary because all fields will be the same in both parsers
2. 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
3. A detection rule identifies suspicious parent-child process relationships commonly associated with malware execution techniques.
A) Encryption
B) Process tree analysis
C) File integrity monitoring
D) Network segmentation
4. An event has the following fields:
Which CQL query will output the frequency of a unique set of ComputerName, UserName, CommandLine?
#event_simpleName = ProcessRollup2 FileName = ssh.exe CommandLine = /\s-
A) | FileName = ssh.exe
| CommandLine = /\s-R\s.+\s-p/
| table([ComputerName, UserName, CommandLine], function=count())
#event_simpleName = ProcessRollup2
B) R\s.+\s-p/ | groupBy([ComputerName, UserName, CommandLine])
C) R\s.+\s-p/ | table([ComputerName, UserName, CommandLine]) | count()
#event_simpleName = ProcessRollup2
D) | FileName = ssh.exe
| CommandLine = /\s-R\s.+\s-p/
| groupBy([ComputerName, UserName, CommandLine], function=count())
#event_simpleName = ProcessRollup2 FileName = ssh.exe CommandLine = /\s-
5. In the Next-Gen SIEM Connector Dashboard, what is the maximum retention period for which you can query third-party data ingestion metrics?
A) 90 days
B) 180 days
C) 60 days
D) 30 days
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: C | Question # 3 Answer: B | Question # 4 Answer: D | Question # 5 Answer: A |
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