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Google Security-Operations-Engineer Exam Syllabus Topics:

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

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最新的 Google Cloud Certified Security-Operations-Engineer 免費考試真題 (Q96-Q101):

問題 #96
You are responsible for identifying suspicious activity and security events in your organization's environment.
You discover that some detection rules are generating false positives when the principal.ip field contains one or more IP addresses in the 192.168.2.0/24 subnet. You want to improve these detection rules using the principal.ip repeated field. What should you add to the YARA-L detection rules?

  • A. net.ip_in_range_cidr(any $e.principal.ip, "192.168.2.0/24")
  • B. net.ip_in_range_cidr(all $e.principal.ip, "192.168.2.0/24")
  • C. not net.ip_in_range_cidr(any $e.principal.ip, "192.168.2.0/24")
  • D. not net.ip_in_range_cidr(all $e.principal.ip, "192.168.2.0/24")

答案：C

解題說明：
Comprehensive and Detailed Explanation
The correct solution is Option D. The goal is to exclude events (i.e., stop false positives) when the principal.
ip field contains any IP from the trusted 192.168.2.0/24 subnet.
The principal.ip field in UDM is a repeated field, meaning it can hold an array of values (e.g., ["1.2.3.4",
"192.168.2.5"]). YARA-L provides the any and all quantifiers to handle repeated fields.9
* any $e.principal.ip: This checks if at least one IP in the array meets the condition.
* all $e.principal.ip: This checks if every IP in the array meets the condition.
The function net.ip_in_range_cidr(...) returns true if an IP is in the specified range.
Therefore, the logic we need is: "do not trigger this rule if any of the IPs in the principal.ip field are in the
192.168.2.0/24 range."
This translates directly to the YARA-L syntax: not net.ip_in_range_cidr(any $e.principal.ip, "192.168.2.0/24")
* Option B would only find events from that subnet.
* Option A would only find events where all associated IPs are in that subnet.
* Option C is the logical inverse of A and would incorrectly filter out events that might be malicious (e.
g., ["1.2.3.4", "192.168.2.5"] would not be excluded because all IPs are not in the range).
Exact Extract from Google Security Operations Documents:
YARA-L 2.0 language syntax > Repeated fields and boolean expressions: When a boolean expression, such as a function call, is applied to a repeated field, you can use the any or all keywords to specify how the expression should be evaluated.10
* any <repeated_field>: The expression evaluates to true if it is true for at least one of the values in the repeated field.
* all <repeated_field>: The expression evaluates to true only if it is true for all of the values in the repeated field.
Functions > net.ip_in_range_cidr: The net.ip_in_range_cidr function is useful to bind rules to specific parts of the network.11 To exclude all private netblocks as defined in RFC1918, you can add a not to the start of the criteria:
and not (net.ip_in_range_cidr(any $e.principal.ip, "10.0.0.0/8") or net.ip_in_range_cidr(any $e.principal.ip,
"172.16.0.0/12") or net.ip_in_range_cidr(any $e.principal.ip, "192.168.0.0/16")) References:
Google Cloud Documentation: Google Security Operations > Documentation > Detections > YARA-L 2.0 language syntax Google Cloud Documentation: Google Security Operations > Documentation > Detections > YARA-L 2.0 functions > net.ip_in_range_cidr


問題 #97
You are responsible for evaluating the level of effort required to integrate a new third-party endpoint detection tool with Google Security Operations (SecOps). Your organization's leadership wants to minimize customization for the new tool for faster deployment. You need to verify that the Google SecOps SOAR and SIEM support the expected workflows for the new third-party tool. You must recommend a tool to your leadership team as quickly as possible. What should you do?
Choose 2 answers

  • A. Identify the tool in the Google SecOps Marketplace, and verify support for the necessary actions in the workflow.
  • B. Configure a Pub/Sub topic to ingest raw logs from the third-party tool, and build custom YARA-L rules in Google SecOps to extract relevant security events.
  • C. Review the architecture of the tool to identify the cloud provider that hosts the tool.
  • D. Review the documentation to identify if default parsers exist for the tool, and determine whether the logs are supported and able to be ingested.
  • E. Develop a custom integration that uses Python scripts and Cloud Run functions to forward logs and orchestrate actions between the third-party tool and Google SecOps.

答案：A,D

解題說明：
Comprehensive and Detailed Explanation
The core task is to evaluate a new tool for fast, low-customization deployment across the entire Google SecOps platform (SIEM and SOAR). This requires checking the two main integration points: data ingestion (SIEM) and automated response (SOAR).
* SIEM Ingestion (Option B): To minimize customization for the SIEM, you must verify that Google SecOps can ingest and understand the tool's logs out-of-the-box. This is achieved by checking the Google SecOps documentation for a default parser for that specific tool. If a default parser exists, the logs will be automatically normalized into the Unified Data Model (UDM) upon ingestion, requiring zero custom development.
* SOAR Orchestration (Option C): To minimize customization for SOAR, you must verify that pre- built automated actions exist. The Google SecOps Marketplace contains all pre-built SOAR integrations (connectors). By finding the tool in the Marketplace, you can verify which actions (e.g.,
"Quarantine Host," "Get Process List") are supported, confirming that response playbooks can be built quickly without custom scripting.
Options D and E describe high-effort, custom integration paths, which are the exact opposite of the "minimize customization for faster deployment" requirement.
Exact Extract from Google Security Operations Documents:
Default parsers: Google Security Operations (SecOps) provides a set of default parsers that support many common security products. When logs are ingested from a supported product, SecOps automatically applies the correct parser to normalize the raw log data into the structured Unified Data Model (UDM) format. This is the fastest method to begin ingesting and analyzing new data sources.
Google SecOps Marketplace: The SOAR component of Google SecOps includes a Marketplace that contains a large library of pre-built integrations for common third-party security tools, including EDR, firewalls, and identity providers. Before purchasing a new tool, an engineer should verify its presence in the Marketplace and review the list of supported actions to ensure it meets the organization's automation and orchestration workflow requirements.
References:
Google Cloud Documentation: Google Security Operations > Documentation > Ingestion > Default parsers > Supported default parsers Google Cloud Documentation: Google Security Operations > Documentation > SOAR > Marketplace integrations


問題 #98
Your company's analyst team uses a playbook to make necessary changes to external systems that are integrated with the Google Security Operations (SecOps) platform. You need to automate the task to run once every day at a specific time. You want your solution to minimize maintenance overhead. What should you do?

  • A. Use a VM to host a script that runs a playbook via an API call.
  • B. Write a custom Google SecOps SOAR job in the IDE using the code from the existing playbook actions.
  • C. Create a Google SecOps SOAR request and a playbook trigger to match the request from the user to start the playbook with the relevant actions.
  • D. Create a Cron Scheduled Connector for this use case Configure a playbook trigger to match the cases created by the connector that runs the playbook with the relevant actions.

答案：D

解題說明：
The best solution is to create a Cron Scheduled Connector in Google SecOps and configure a playbook trigger to execute based on the cases generated by the connector. This allows the playbook to run automatically at a specific daily time with minimal maintenance overhead, leveraging built-in scheduling and orchestration rather than requiring custom jobs or external scripts.


問題 #99
You are using Google Security Operations (SecOps) to investigate suspicious activity linked to a specific user. You want to identify all assets the user has interacted with over the past seven days to assess potential impact. Your need to understand the user's relationships to endpoints, service accounts, and cloud resources. How should you identify user-to-asset relationships in Google SecOps?

  • A. Run a retrohunt to find rule matches triggered by the user.
  • B. Generate an ingestion report to identify sources where the user appeared in the last seven days.
  • C. Query for hostnames in UDM Search and filter the results by user.
  • D. Use the Raw Log Scan view to group events by asset ID.

答案：C

解題說明：
The correct approach is to query UDM Search for hostnames (or other asset identifiers) and filter results by the specific user. UDM normalizes logs into a common schema, allowing you to trace the user's interactions across endpoints, service accounts, and cloud resources within the seven- day window. This provides a comprehensive view of user-to-asset relationships for impact assessment.


問題 #100
You have a custom-built YARA-L rule in Google Security Operations (SecOps) correlating observed IP addresses in network and EDR logs against threat intelligence findings ingested from a Malware Information Sharing Platform (MISP) over a 2-minute time window. Your company's SOC reported that the rule generates too many false positives. You want to reduce the number of false positives generated by the rule while continuing to use threat intelligence.
What should you do?

  • A. Convert the rule to a dashboard, and use a match window of 24 hours to visualize entities in a bar chart.
  • B. Adjust the match window in the rule to 24 hours to aggregate IP addresses by asset once a day.
  • C. Modify the rule to trigger only when the ICCs graph.risk_score.risk_score field exceeds 500.
  • D. Modify the rule to alert only when the graph.metadata.threat.severity value is critical or high.

答案：D

解題說明：
Comprehensive and Detailed 150 to 250 words of Explanation From Exact Extract Google Security Operations Engineer documents:
To reduce false positives in Threat Intelligence (TI) matching rules, the standard practice is to filter based on the confidence or severity of the threat indicator. Threat feeds often contain "noisy" indicators (like IP addresses associated with low-risk spam or scanning).
In Google Security Operations, entity context (such as TI data) is accessed in YARA-L using the graph variable. The UDM (Unified Data Model) field structure for threat severity within an entity is metadata.threat.
severity.
By modifying the rule to include a condition checking graph.metadata.threat.severity, you ensure the rule only triggers on indicators that the intelligence source has deemed "High" or "Critical," thereby filtering out low- fidelity noise.
* Option B correctly applies this logic: $graph.metadata.threat.severity = "CRITICAL" or $graph.
metadata.threat.severity = "HIGH".
* Option D (Adjusting the match window) only changes the frequency of grouping, not the criteria for what constitutes a threat, so the false positive IP would still trigger an alert (just once per day).
* Option C refers to risk_score, which is often a calculated aggregate, whereas threat.severity is the direct attribute from the TI feed (MISP) needed for this specific tuning.
References: Google Security Operations Documentation > Detection > YARA-L 2.0 Language Syntax > Graph; Google Security Operations Documentation > Unified Data Model > Entity Fields


問題 #101
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