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Google Security-Operations-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Observability	10%	- Analyze telemetry and metrics - Report security posture and risks - Design monitoring and alerting strategies - Improve security visibility
Detection engineering	22%	- Implement threat intelligence into detections - Manage detection lifecycle - Optimize detection logic and reduce false positives - Develop detection rules (YARA-L, Sigma)
Incident response	21%	- Develop and use response playbooks - Investigate security incidents - Automate response workflows - Contain and eradicate threats
Data management	14%	- Manage data retention and storage - Implement Unified Data Model (UDM) - Validate data quality and completeness - Ingest and normalize logs and data
Threat hunting	19%	- Design and execute threat hunts - Analyze anomalies and behaviors - Document and share findings - Use threat intelligence in hunting
Platform operations	14%	- Manage access and permissions - Monitor platform health and performance - Configure Security Command Center - Manage Google Security Operations platform

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Google Security-Operations-Engineer: Google Cloud Certified - Professional Security Operations Engineer (PSOE) Exam braindumps PDF & Testking echter Test

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Google Cloud Certified - Professional Security Operations Engineer (PSOE) Exam Security-Operations-Engineer Prüfungsfragen mit Lösungen (Q116-Q121):

116. Frage
Your team hunts for threats in a large multinational corporation. You have subscriptions to threat intelligence feeds from third-party sources. You want to implement a solution to continuously compare DNS calls on endpoints to your threat intelligence feeds. What should you do?

A. Create a YARA-L rule in Google Security Operations (SecOps) to track matches between the ingested EDR log entries and the entity graph.
B. Push endpoint logs to BigQuery and use scripts to compare entries to Google Threat intelligence by using a Google Threat Intelligence API key.
C. Create a YARA-L rule in Google Security Operations (SecOps) to track matches between the ingested EDR log entries and the VirusTotal table in the entity graph.
D. Use custom modules in Event Threat Detection in Security Command Center (SCC) to correlate feed data with Google Cloud logs.

Antwort: A

Begründung:
The best solution is to create a YARA-L rule in Google SecOps that correlates ingested EDR log entries (including DNS calls) with the entity graph populated by your threat intelligence feeds.
This enables continuous monitoring and automated detection of endpoint activity that matches known malicious domains or indicators, supporting proactive threat hunting at scale.

117. Frage
You are an incident responder at your organization using Google Security Operations (SecOps) for monitonng and investigation. You discover that a critical production server, which handles financial transactions, shows signs of unauthorized file changes and network scanning from a suspicious IP address. You suspect that persistence mechanisms may have been installed. You need to use Google SecOps to immediately contain the threat while ensuring that forensic data remains available for investigation. What should you do first?

A. Deploy emergency patches, and reboot the server to remove malicious persistence.
B. Use the EDR integration to quarantine the compromised asset.
C. Use VirusTotal to enrich the IP address and retrieve the domain. Add the domain to the proxy block list.
D. Use the firewall integration to submit the IP address to a network block list to inhibit internet access from that machine.

Antwort: B

Begründung:
The most effective first step in containment while preserving forensic data is to use the EDR integration to quarantine the compromised asset. Quarantine isolates the server from the network, preventing further malicious activity, but it does not wipe or reboot the system, ensuring that evidence such as persistence mechanisms, unauthorized file changes, and indicators of compromise remain intact for forensic investigation.

118. Frage
You are using a Google-managed image on a Compute Engine instance in Google Cloud to run an application. You need to ingest the application's log output into Google Security Operations (SecOps). The log output is standard and has a valid label and parser in Google SecOps. Your solution must minimize the cost and time required to move this data into Google SecOps. What should you do?

A. Deploy a Bindplane agent on the image to collect and send the logs to Google SecOps.
B. Use the Ops Agent embedded in the Compute Engine image to pull the logs into Cloud Logging.
Use the direct ingestion mechanism to ingest the logs from Google Cloud into Google SecOps.
C. Create a script on the workload that reads the logs and uses the Google SecOps Ingestion API to push them to Google SecOps.
D. Use the Ops Agent embedded in the Compute Engine image to pull the logs into a Cloud Storage bucket. Create a feed in Google SecOps to ingest the logs.

Antwort: B

Begründung:
The most efficient and cost-effective approach is to use the Ops Agent (already embedded in the Compute Engine image) to send logs to Cloud Logging, and then use the direct ingestion mechanism to forward those logs into Google SecOps. This avoids deploying additional agents or scripts, leverages Google-managed integrations, and minimizes both cost and time.

119. Frage
Your Google Security Operations (SecOps) instance is generating alerts for unusual login times from multiple user accounts. Your SOC analysts are reporting a high number of the alerts are false positives involving service accounts used by scheduled automation tasks. You want to refine the detection logic using entity-level context available in Google SecOps. You want to use the most effective approach. What should you do?

A. Update the rule to only alert when the principal.user.email and principal.user.userid fields match in the same event.
B. Modify the rule to include the principal.user.type != "service_account" condition.
C. Use asset tags to group known automation systems, and exclude them from the alert logic.
D. Add a reference list of all service accounts, and suppress alerts for any matches on the principal.user.email field.

Antwort: B

Begründung:
The most effective approach is to modify the rule to include the condition principal.user.type !=
"service_account". This directly uses entity-level context to exclude service accounts from triggering alerts for unusual login times, significantly reducing false positives without complex maintenance or manual list management.

120. Frage
You are developing a new detection rule in Google Security Operations (SecOps). You are defining the YARA-L logic that includes complex event, match, and condition sections. You need to develop and test the rule to ensure that the detections are accurate before the rule is migrated to production. You want to minimize impact to production processes. What should you do?

A. Use Gemini in Google SecOps to develop the rule by providing a description of the parameters and conditions, and transfer the rule into the Rules Editor.
B. Develop the rule in the Rules Editor, define the sections of the rule logic, and test the rule by setting it to live but not alerting. Run a YARA-L retrohunt from the rules dashboard.
C. Develop the rule logic in the UDM search, review the search output to inform changes to filters and logic, and copy the rule into the Rules Editor.
D. Develop the rule in the Rules Editor, define the sections of the rule logic, and test the rule using the test rule feature.

Antwort: D

Begründung:
The Google Security Operations (SecOps) platform provides an integrated, zero-impact workflow for developing and testing detections. The standard method is to use the "Test Rule" feature, which is built directly into the Rules Editor.
After the detection engineer has defined the complete YARA-L logic (including events, match, and condition sections), they can click the "Test Rule" button. This function performs a historical search (a retrohunt) against a specified time range of UDM data (e.g., last 24 hours, last 7 days). The platform then returns a list of all events that would have triggered the detection, without creating any live alerts, cases, or impacting production.
This allows the engineer to "ensure that the detections are accurate" by reviewing the historical matches, identifying potential false positives, and refining the rule's logic. This iterative "develop and test" cycle within the editor is the primary method for validating a rule before it is enabled. While UDM search (Option A) is useful for testing the events section logic, it cannot test the full match and condition logic of the rule. Setting a rule to "live but not alerting" (Option D) is a valid, later step, but the "Test Rule" feature is the correct initial development and testing tool.
(Reference: Google Cloud documentation, "Create and manage rules using the Rules Editor"; "Test a rule")

121. Frage
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