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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Ingestion | 20% | - Connector components and management - Fleet management and log collector deployment - Troubleshooting ingestion and connectivity issues - Ingestion methods and integration strategies - Built-in and custom data connector configuration - First-party vs third-party data sources |
| Topic 2: Parsing | 20% | - AI-generated parsers and advanced syntax - Log format identification and handling - Parser testing and validation - CrowdStrike Parsing Standards and normalization - Parser creation, modification and cloning - Monitoring and resolving parsing errors |
| Topic 3: Automation and Integration | 20% | - External system integration - API access and token management - Falcon Fusion SOAR workflow design and automation - Integration with FalconPy and other tools - Automated response and remediation |
| Topic 4: Content Creation | 20% | - Lookup file management and utilization - First-party vs third-party detections - Dashboard creation and customization - Content deployment and version control - CQL query design, building and optimization - Correlation rules creation, tuning and management |
| Topic 5: User Management | 20% | - SSO/SAML configuration and claim mapping - Multi-factor authentication (MFA) setup - Custom role creation and permission assignment - Audit log monitoring and usage - Repository-level access control - Role-based access control (RBAC) and built-in roles |
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NEW QUESTION # 11
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: C
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 # 12
An event has the following fields:
Which CQL query will output the frequency of a unique set of ComputerName, UserName, CommandLine?
Answer: D
Explanation:
CrowdStrike LogScale documentation states that groupBy() is used to group events by one or more specified fields, similar to SQL GROUP BY. The documentation also says the function parameter accepts aggregate functions, and its default is count(as=_count). That means the query that explicitly groups by ComputerName, UserName, and CommandLine and applies function=count() is the correct way to output the frequency of each unique combination of those three fields.
Why the other options are incorrect:
A is incorrect because table() formats output rows but does not aggregate unique combinations into frequencies the way groupBy() does. Adding count() after table() does not produce grouped counts for each unique triplet. B is incorrect because table() is not the aggregation function documented for grouped frequency counting; groupBy() is. D is close, but it relies on the default count behavior rather than explicitly specifying function=count(). Since the question asks which query will output the frequency of a unique set, C is the most correct and explicit choice.
NEW QUESTION # 13
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: C
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 # 14
A Falcon Log Collector has been configured with 4 sinks of type memory, each having a queue size of 2GB.
What is the minimum memory requirement produced by this configuration?
Answer: D
Explanation:
Each memory sink requires its queue size plus an overhead of 500 MB. With 4 sinks of 2 GB each:
Memory required = (2 GB + 0.5 GB) ร 4 = 2.5 GB ร 4 = 10 GB.
This accounts for the minimum memory needed for all configured sinks.
NEW QUESTION # 15
During threat hunting, an analyst searches for rare processes executed across endpoints that deviate from baseline behavior within the enterprise environment.
Answer: C
Explanation:
Rare or unusual activity compared to baseline indicates anomaly-based detection.
NEW QUESTION # 16
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