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>> Security-Operations-Engineer日本語問題集 <<
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質問 # 135
Your organization has recently onboarded to Google Cloud with Security Command Center Enterprise (SCCE) and is now integrating it with your organization's SOC. You want to automate the response process within SCCE and integrate with the existing SOC ticketing system. You want to use the most efficient solution. How should you implement this functionality?
正解：B
解説：
Comprehensive and Detailed Explanation
The correct answer is Option C. The prompt asks for the most efficient and automated solution for handling SCCE findings and integrating with a ticketing system. This is the primary use case for Google Security Operations SOAR.
The native workflow is as follows:
* SCCE detects a finding.
* The finding is automatically ingested into Google SecOps SIEM, which creates an alert.
* The alert is automatically sent to SecOps SOAR, which creates a case.
* The SOAR case automatically triggers a playbook.
Option C describes this process perfectly. An administrator would disable the default playbook and enable a specific playbook that uses a pre-built integration (from the Marketplace) for the organization's ticketing system (e.g., ServiceNow, Jira). This playbook would contain an automated step to generate a ticket, thus fulfilling the requirement efficiently.
Option B is a manual process. Options A and D describe complex, custom-built data engineering pipelines, which are far less efficient than using the built-in SOAR capabilities.
Exact Extract from Google Security Operations Documents:
SOAR Playbooks and Integrations: Google SecOps SOAR is designed to automate and orchestrate responses to alerts. When an alert from a source like Security Command Center (SCC) is ingested and creates a case, it can be configured to automatically trigger a playbook.
Ticketing Integration: A common playbook use case is integration with an external ticketing system. Using a pre-built integration from the SOAR Marketplace, an administrator can add a step to the playbook (e.g., Create Ticket). This action will automatically generate a ticket in the external system and populate it with details from the alert, such as the finding, the affected resources, and the recommended remediation steps.
This provides a seamless, automated workflow from detection to ticketing.
References:
Google Cloud Documentation: Google Security Operations > Documentation > SOAR > Use cases > Case Management Google Cloud Documentation: Google Security Operations > Documentation > SOAR > Marketplace integrations
質問 # 136
You are a senior SOC analyst in your organization. You are receiving alerts of traffic to a command and control (C2) IP address. You want to use Google Security Operations (SecOps) to investigate the IP address associated with the C2 IP address. What should you do?
正解：D
解説：
The most effective method is to conduct a Google SecOps SIEM Search using src.ip and target.ip to identify both outbound and inbound traffic associated with the C2 IP address. This approach gives you comprehensive visibility into all interactions with the suspicious IP, supporting a thorough investigation.
質問 # 137
You are part of a cybersecurity team at a large multinational corporation that uses Google Security Operations (SecOps). You have been tasked with identifying unknown command and control nodes (C2s) that are potentially active in your organization's environment. You need to generate a list of potential matches for the unknown C2s within the next 24 hours. What should you do?
正解：C
解説：
Comprehensive and Detailed 150 to 200 words of Explanation From Exact Extract Google Security Operations Engineer documents:
The key requirement is to hunt for unknown C2 nodes. This implies that the indicators will not exist in any current threat intelligence feed. Therefore, Option C is incorrect as it only hunts for known IoCs. Option A is also incorrect as Security Health Analytics (SHA) is a posture management tool, not a threat hunting tool.
Option D describes a classic and effective hypothesis-driven threat hunt. Attackers frequently use Newly Registered Domains (NRDs) for their C2 infrastructure, as these domains have no established reputation and are not yet on blocklists.
Google Security Operations (SecOps) allows an engineer to write a YARA-L rule that joins real-time event data (UDM network traffic) with contextual data (the entity graph or a custom lookup). An engineer can ingest WHOIS data or a feed of NRDs as context. The YARA-L rule would then compare outbound network connections against this context, looking for any communication with domains registered within the last 30-
90 days. By executing this rule as a retrohunt, the engineer can scan all historical data to "generate a list of potential matches" for this high-risk, anomalous behavior, which is a strong indicator of unknown C2 activity.
(Reference: Google Cloud documentation, "YARA-L 2.0 language syntax"; "Run a YARA-L retrohunt"; " Context-aware detections with entity graph")
質問 # 138
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. You 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?
正解：C
解説：
Comprehensive and Detailed 150 to 250 words of Explanation From Exact Extract Google Security Operations Engineer documents:
The primary investigation tool for exploring relationships and historical activity in Google Security Operations is the UDM (Universal Data Model) search. The platform's curated views, such as the "User View," are built on top of this search capability.
To find all assets a user has interacted with, an analyst would perform a UDM search for the specific user (e.
g., principal.user.userid = "suspicious_user") over the specified time range. The search results will include all UDM events associated with that user. Within these events, the analyst can examine all populated asset fields, such as principal.asset.hostname, principal.ip, target.resource.name, and target.user.userid (for interactions with service accounts).
This UDM search allows the analyst to pivot from the user entity to all related asset entities, directly answering the question of "what assets the user has interacted with." While the wording of Option A is slightly backward (it's more efficient to query for the user and find the hostnames), it is the only option that correctly identifies the UDM search as the tool used to find user-to-asset (hostname) relationships. Options B (Retrohunt), C (Raw Log Scan), and D (Ingestion Report) are incorrect tools for this investigative task.
(Reference: Google Cloud documentation, "Google SecOps UM Search overview"; "Investigate a user"; " Universal Data Model noun list")
質問 # 139
Your organization has a standard set of Google Security Operations (SecOps) playbooks that are applied to alerts in different circumstances. One playbook uses an "All" trigger that should always be applied if no other more specific playbooks have triggered. You need to ensure that the more specific playbook is attached and not the generic "All" playbook when multiple triggers match.
What should you do?
正解：D
解説：
Set the priority of the "All" playbook to a higher value than the priority of the specific playbook. In Google SecOps, playbook triggers are evaluated by priority. By assigning a higher numerical priority (which means lower precedence) to the "All" playbook, you ensure that more specific playbooks with lower numerical priorities (higher precedence) will be attached and executed first when multiple triggers match, and the generic "All" playbook will only be used if no specific playbook applies.
質問 # 140
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