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| Section | Weight | Objectives |
|---|---|---|
| Automation and Integration | 20% | - API access and token management - External system integration - Integration with FalconPy and other tools - Automated response and remediation - Falcon Fusion SOAR workflow design and automation |
| User Management | 20% | - 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 - SSO/SAML configuration and claim mapping |
| Content Creation | 20% | - First-party vs third-party detections - CQL query design, building and optimization - Lookup file management and utilization - Dashboard creation and customization - Correlation rules creation, tuning and management - Content deployment and version control |
| Parsing | 20% | - AI-generated parsers and advanced syntax - CrowdStrike Parsing Standards and normalization - Parser creation, modification and cloning - Log format identification and handling - Parser testing and validation - Monitoring and resolving parsing errors |
| Data Ingestion | 20% | - Built-in and custom data connector configuration - Troubleshooting ingestion and connectivity issues - First-party vs third-party data sources - Connector components and management - Fleet management and log collector deployment - Ingestion methods and integration strategies |
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NEW QUESTION # 55
What are the two types of connectors used to integrate data between third-party systems and Falcon?
Answer: B
Explanation:
Falcon integrates with third-party systems using Push connectors, which send data to Falcon, and Pull connectors, which retrieve data from external sources. These two types enable flexible data ingestion and synchronization.
NEW QUESTION # 56
You need to ingest data from a custom internal application hosted on-prem. The application writes logs to a file on a syslog server.
Which data connector would you use?
Answer: A
Explanation:
The correct answer is B. HTTP Event Connector .
CrowdStrike describes the HTTP Event Connector (HEC) as the generic mechanism used to bring third- party data into Falcon Next-Gen SIEM when you need to onboard logs from sources that are not tied to a specific cloud-native connector. CrowdStrike's own Next-Gen SIEM materials highlight pre-built connectors and HTTP Event Collectors as the way to extend visibility to many different third-party sources.
Because this question describes a custom internal application hosted on-prem , the cloud-specific connectors in options A , C , and D do not fit. The broad, flexible connector option intended for custom or non-native sources is the HTTP Event Connector . Also, CrowdStrike's vCenter example shows an architecture where logs are first centralized and then onboarded to Falcon Next-Gen SIEM through an HTTP Event Connector , which aligns with this kind of custom-source pattern.
NEW QUESTION # 57
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: C
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 # 58
You are reviewing logs and find that the content appears as one large block of text within the @rawstring field for incoming firewall logs. The other expected structured fields are empty.
What is the cause of this issue?
Answer: C
Explanation:
The correct answer is A. The parser was incorrect .
CrowdStrike LogScale documentation explains that when data is ingested without an appropriate parser , the event still arrives in LogScale, but it is not automatically parsed into fields . In that case, the event remains as raw text in @rawstring, while the expected extracted fields stay empty. That matches the exact symptom described in the question.
Why the other options are incorrect:
B is incorrect because if the ingestion token were invalid, the data generally would not be ingested successfully in the first place. C is incorrect because an overloaded sink may delay or buffer delivery, but it does not explain why only @rawstring is populated while structured fields are missing. D is incorrect because a timestamp parsing problem may cause time-related errors, but it would not by itself explain why the entire firewall event remains unparsed as raw text. CrowdStrike's parser error docs show that parse failures are tracked separately and that @rawstring is what you inspect when events fail to parse correctly.
NEW QUESTION # 59
What is the recommended order of the three required activities to build an efficient CQL query?
Answer: B
Explanation:
The recommended order for building efficient CQL queries is to first filter the data to reduce volume, then aggregate it for analysis, and finally format the results for readability or reporting.
This order optimizes performance and clarity.
NEW QUESTION # 60
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