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| Section | Objectives |
|---|---|
| Exam domains (official detailed syllabus not publicly disclosed) | - CrowdStrike SIEM and log analysis fundamentals - Dashboards, reporting, and alerting configuration - Threat detection and incident investigation workflows in CrowdStrike platform - Security event ingestion, normalization, and correlation concepts - Operational use of CrowdStrike Falcon modules for SIEM engineering tasks |
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NEW QUESTION # 76
Which statement is accurate about how data ingest is measured and represented in Next-Gen SIEM?
Answer: B
Explanation:
Next-Gen SIEM measures data ingest based on the average gigabytes per day from all data sources, calculated before parsing, to accurately represent the volume of raw log data entering the system.
NEW QUESTION # 77
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 # 78
In the Next-Gen SIEM Connector Dashboard, what is the maximum retention period for which you can query third-party data ingestion metrics?
Answer: B
Explanation:
The Next-Gen SIEM Connector Dashboard allows querying third-party data ingestion metrics for up to 90 days, providing visibility into trends and connector performance over this period.
NEW QUESTION # 79
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: C
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 # 80
Review the log event below:
{"ts": "2018/11/01 14:31:10", "server": "web01", "message": "Out of memory"} Which parsing function is correct to add a missing timezone field?
Answer: B
Explanation:
The correct answer is D . CrowdStrike LogScale's timestamp parsing documentation gives this exact pattern as the example for a JSON event whose ts field contains 2018/11/01 14:31:10 with no timezone present. The documented solution is:
parseJson() | parseTimestamp("yyyy/MM/dd HH:mm:ss", timezone="Europe/Paris", field=ts) This works because the event is JSON, so parseJson() is the right first step, and the timestamp format matches the sample exactly. Since the timestamp string does not include timezone information, CrowdStrike documentation says you must provide a timezone parameter to parseTimestamp().
Why the other options are incorrect:
A is wrong because the format string does not match the timestamp. The event uses 2018/11/01 14:31:10, which is yyyy/MM/dd HH:mm:ss, not dd/MMM/yyyy:HH:mm:ss Z. Also, the sample timestamp does not include a Z timezone token in the raw string. B and C are wrong because kvParse() is for key-value logs, not JSON logs, and this event is clearly JSON. CrowdStrike's built-in parser documentation distinguishes JSON parsing from KV parsing, and the timestamp example for missing timezone specifically uses parseJson() with parseTimestamp().
NEW QUESTION # 81
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