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Unsere Prüfungsunterlage zu Google Security-Operations-Engineer（Google Cloud Certified - Professional Security Operations Engineer (PSOE) Exam）enthältet alle echten, originalen und richtigen Fragen und Antworten. Die Abdeckungsrate unserer Unterlage (Fragen und Antworten) zu Google Security-Operations-Engineer（Google Cloud Certified - Professional Security Operations Engineer (PSOE) Exam）ist normalerweise mehr als 98%.
| Section | Objectives |
|---|---|
| Topic 1: Identity and Access Security | - IAM security monitoring
|
| Topic 2: Security Operations in Google Cloud | - Security monitoring and logging (Cloud Logging / Cloud Monitoring)
|
| Topic 3: Threat Detection and Incident Response | - Detection engineering and threat hunting
|
| Topic 4: Cloud Security Posture and Compliance | - Security configuration assessment
|
| Topic 5: Google Security Operations Platform | - Chronicle / Google SecOps SIEM usage
|
| Topic 6: Automation and Response | - Security orchestration and response
|
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89. Frage
You work for an organization that uses Security Command Center (SCC) with Event Threat Detection (ETD) enabled. You need to enable ETD detections for data exfiltration attempts from designated sensitive Cloud Storage buckets and BigQuery datasets. You want to minimize Cloud Logging costs. What should you do?
Antwort: D
Begründung:
Comprehensive and Detailed 150 to 250 words of Explanation From Exact Extract Google Security Operations Engineer documents:
This question is a balance between enabling detection and managing cost. Event Threat Detection (ETD) identifies threats by analyzing logs, and the specific detection for data exfiltration requires Data Access audit logs.
Data Access audit logs are disabled by default because they are high-volume and can be expensive. The key requirement is to "minimize Cloud Logging costs" while still enabling the detection for specific sensitive resources.
Data exfiltration is a "data read" operation. Therefore, to meet the requirements, the organization only needs to enable "data read" audit logs. Enabling "data write" logs (Option B) is unnecessary for this detection and would add needless cost. Enabling logs for all resources (Option C) would be prohibitively expensive and violates the "minimize cost" constraint. While ETD does use VPC Flow Logs (Option D) for many network- based detections, they do not provide the resource-level detail (i.e., which bucket or dataset was accessed) required for this specific data exfiltration finding. Therefore, enabling "data read" logs only for the sensitive resources is the most precise, cost-effective solution.
(Reference: Google Cloud documentation, "Event Threat Detection overview"; "Enable Event Threat Detection"; "Cloud Logging - Data Access audit logs")
90. Frage
You are writing a detection rule in Google Security Operations (SecOps) SIEM that sends a risk score to the alert. You have access to Google Threat Intelligence (GTI) data through your Google SecOps subscription. You need to ensure that the threat score output in the detection logic informs the alert's risk score and is available for future detections. What should you do?
Antwort: D
Begründung:
The correct method is to use the outcomes section of the YARA-L detection logic to apply logic on UDM enrichment fields (including GTI data), calculate the total risk outcome, and store it in the risk_score variable. This ensures the risk score is attached to the alert and available for correlation in future detections.
91. Frage
A Google Security Operations (SecOps) detection rule is generating frequent false positive alerts. The rule was designed to detect suspicious Cloud Storage enumeration by triggering an alert whenever the storage.
objects.list API operation is called using the api.operation UDM field. However, a legitimate backup automation tool that uses the same API, causing the rule to fire unnecessarily. You need to reduce these false positives from this trusted backup tool while still detecting potentially malicious usage. How should you modify the rule to improve its accuracy?
Antwort: C
Begründung:
Comprehensive and Detailed Explanation
The correct solution is Option D. The problem is that a known, trusted principal (the backup tool's service account) is performing a legitimate action (storage.objects.list) that happens to look like the suspicious behavior the rule is designed to catch.
The most precise and effective way to reduce these false positives without weakening the rule's ability to catch malicious actors is to create an exception for the trusted principal.
By adding principal.user.email != "backup-bot@fcobaa.com" (or the equivalent principal.user.userid) to the events or condition section of the YARA-L rule, the rule will now only evaluate events where the actor is not the known-good backup bot.
* Option A is incorrect because it just lowers the priority of the false positive; it doesn't stop it from being generated.
* Option B is incorrect because the legitimate tool might also perform repeated calls, leading to the same false positive.
* Option C is incorrect because api.service_name = "storage.googleapis.com" is less specific than api.
operation = "storage.objects.list" and would likely increase the number of false positives by triggering on any storage API call.
Exact Extract from Google Security Operations Documents:
Reduce false positives: When a detection rule generates false positives due to known-benign activity (e.g., from an administrative script or automation tool), the best practice is to add a not condition to the rule to exclude the trusted entity.8 You can filter on UDM fields to create exceptions. For example, to prevent a rule from firing on activity from a specific service account, you can add a condition to the events section such as:
and $e.principal.user.userid != "trusted-service-account@project.iam.gserviceaccount.com" This technique, often called "allow-listing" or "suppression," improves the rule's accuracy by focusing only on unknown or untrusted principals.
References:
Google Cloud Documentation: Google Security Operations > Documentation > Detections > Overview of the YARA-L 2.0 language > Add not conditions to prevent false positives
92. Frage
You received an IOC from your threat intelligence feed that is identified as a suspicious domain used for command and control (C2). You want to use Google Security Operations (SecOps) to investigate whether this domain appeared in your environment. You want to search for this IOC using the most efficient approach.
What should you do?
Antwort: C
Begründung:
Comprehensive and Detailed 150 to 250 words of Explanation From Exact Extract Google Security Operations Engineer documents:
The most efficient and reliable method to proactively search for a specific indicator (like a domain) in Google Security Operations is to perform a Universal Data Model (UDM) search. All ingested telemetry, including DNS logs and proxy logs, is parsed and normalized into the UDM. This allows an analyst to run a single, high- performance query against a specific, indexed field.
To search for a domain, an analyst would query a field such as network.dns.question.name or network.http.
hostname. Option B correctly identifies this as querying the "DNS section of the network noun." This approach is vastly superior to a raw log search (Option C), which is slow, inefficient, and does not leverage the normalized UDM data.
Option D (IOC Search/Matches) is a passive feature that shows automatic matches between your logs and Google's integrated threat intelligence. While it's a good place to check, a UDM search is the active, analyst- driven process for hunting for a new IoC that may have come from an external feed. Option A is a UI feature for grouping search results and is not the search method itself.
(Reference: Google Cloud documentation, "Google SecOps UDM Search overview"; "Universal Data Model noun list - Network")
93. 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?
Antwort: C
Begründung:
Comprehensive and Detailed 150 to 250 words of Explanation From Exact Extract Google Security Operations Engineer documents:
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")
94. Frage
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