Practice Free Security-Operations-Engineer Google Cloud Certified - Professional Security Operations Engineer (PSOE) Exam Exam Questions Answers With Explanation

The Google Security Operations (SecOps) SOAR platform provides a native feature to enforce data collection at the end of an incident's lifecycle. The most effective and standard method to ensure analysts "must be categorized" is to customize the Close Case dialog.

This built-in feature allows an administrator to modify the pop-up window that appears when an analyst clicks the "Close Case" button in the UI. For this use case, the administrator would add a new custom field, such as a dropdown list titled "DLP Root Cause." This field would then be populated with the "five DLP event types" as the selectable options.

Crucially, this new field can be marked as mandatory. This configuration forces the analyst to select one of the five predefined root causes before the case can be successfully closed. This method ensures 100% compliance with the requirement, captures structured data for later reporting and metrics, and is the standard, low-maintenance solution. Using tags (Option B) is not mandatory and is prone to human error. Customizing the case name (Option A) is not a structured data field and is not enforceable.

(Reference: Google Cloud documentation, "Google SecOps SOAR overview"; "Customize case closure reasons"; "Case and Alert Customizations")

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")

Comprehensive and Detailed 150 to 250 words of Explanation From Exact Extract Google Security Operations Engineer documents:

The requirement is to identify internal entities and label them with a network name across alerts from "multiple connectors." This is a global environment configuration task, not a per-playbook task.

In Google SecOps SOAR, you achieve this by configuring the Networks (or Environments) settings. The documentation states: "You can define your internal network ranges... When an entity is ingested, the system checks if the entity value falls within any of the defined ranges. If it does, the entity is marked as internal."

Furthermore, you can assign a Network Name to these ranges. When an entity matches the range, it is automatically enriched with that network context. This allows you to set up Playbook Triggers based on the "Network Name" field, satisfying the requirement. Option D (Enrichment step) is inefficient because it would require adding the step to every single playbook, whereas Option A solves it globally for the platform.

Comprehensive and Detailed Explanation

The correct solution is Option C. The primary constraints are to "streamline" the process, create a "new, functional playbook," get it "as soon as possible," and "use available tools in Google Security Operations."

Google Security Operations integrates Gemini directly into the SOAR platform to accelerate security operations. One of its key capabilities is generative playbook creation. This feature allows an analyst to describe their intended objectives in natural language (e.g., "Create a playbook to investigate and respond to a remote shell alert"). Gemini then generates a complete, logical playbook flow, including investigation, enrichment, containment, and eradication steps.

This generated playbook serves as a high-quality draft. The analyst can then add the necessary customizations (like specific tools, notification endpoints, or contacts for the e-commerce platform) and, most importantly, test the playbook to ensure it is functional and reliable for junior analysts to execute. This workflow directly meets all the prompt's requirements, especially "streamline" and "as soon as possible."

Option D (creating a custom playbook from scratch and using a red team) is the exact opposite of streamlined and fast. Option B involves patching an "outdated" playbook, not creating a new one. Option A incorrectly bundles a specific remediation action (filtering traffic) with the playbook creation process.

Exact Extract from Google Security Operations Documents:

Gemini for Security Operations: Gemini in Google SecOps provides generative AI to assist analysts and engineers. Within the SOAR capability, Gemini can generate entire playbooks from natural language prompts.

Playbook Creation with Gemini: Instead of building a playbook manually, an engineer can describe the intended objectives of the response plan. Gemini will generate a new playbook with a logical structure, including relevant actions and conditional branches. This generated playbook serves as a strong foundation, which can then be refined. The engineer can add necessary customizations to tailor the playbook to the organization's specific environment, tools, and processes. Before deploying the playbook for use by the SOC, it is a best practice to test it against simulated alerts to validate its functionality and ensure it runs as expected.

Comprehensive and Detailed Explanation

The correct answer is Option A. This question is about entity context enrichment and aliasing.

Endpoint telemetry from EDR and Windows Event Logs (like 4624) identifies users by their Windows Security Identifier (SID) (e.g., S-1-5-21-12345...). However, detection rules are more effective when they match on a human-readable and consistent identifier, like an email address or username, which is stored in principal.user.userid.

To "connect the dots" between the SID found in endpoint events and the userid, Google SecOps must ingest an authoritative user context data source. In a modern Windows environment, this source is Microsoft Entra ID (formerly Azure AD) or on-premises Active Directory.

Ingesting Entra ID logs as a USER_CONTEXT feed populates the SecOps entity graph. This allows the platform to automatically alias the SID from an endpoint log to the corresponding userid (e.g., jsmith@company.com) at ingestion time. This ensures the principal.user.userid field is correctly populated, allowing the detection rules to match.

Options B, C, and D are all additional event sources (like EDR) and would provide more SIDs, but they do not provide the central directory data needed to perform the aliasing.

Exact Extract from Google Security Operations Documents:

UDM enrichment and aliasing overview: Google Security Operations (SecOps) supports aliasing and enrichment for assets and users. Aliasing enables enrichment. For example, using aliasing, you can find the job title and employment status associated with a user ID.

How aliasing works: User aliasing uses the USER_CONTEXT event type for aliasing. This contextual data is stored as entities in the Entity Graph. When new Unified Data Model (UDM) events are ingested, enrichment uses this aliasing data to add context to the UDM event. For example, an EDR log might contain a principal.windows_sid. The enrichment process queries the entity graph (populated by your Active Directory or Entra ID feed) and populates the principal.user.userid and other fields in the principal.user noun.

Google Security Operations (SecOps) SOAR is designed to natively measure and report on key SOC performance metrics, including MTTR. This calculation is automatically derived from playbook case stages.

As a case is ingested and processed by a SOAR playbook, it moves through distinct, customizable stages (e.g., "Triage," "Investigation," "Remediation," "Closed"). The SOAR platform automatically records a timestamp for each of these stage transitions. The time deltas between these stages (e.g., the time from when a case entered "Triage" to when it entered "Remediation") are the raw data used to calculate MTTR and other KPIs.

This data is then aggregated and visualized in the built-in SecOps SOAR reporting and dashboarding features. This is the standard, out-of-the-box method for capturing these metrics. Option C describes a manual, redundant process of what case stages do automatically. Option D describes where the data might be viewed (Looker), but Option B describes the underlying mechanism for how the MTTR data is captured in the first place, which is the core of the question.

(Reference: Google Cloud documentation, "Google SecOps SOAR overview"; "Manage playbooks"; "Get insights from dashboards and reports")

Comprehensive and Detailed Explanation

The correct solution is Option B. This question tests the advanced detection capabilities of YARA-L when using the Applied Threat Intelligence (ATI) Fusion Feed.

The key requirement is to find an IP that not only matches but has a documented relationship to APT41. The ATI Fusion Feed is not just a flat list of IOCs; it is a context-rich graph of indicators, malware, threat actors, and their relationships, managed by Google's threat intelligence teams.10

Option A is incorrect because it describes a manual, static list (data table) and cannot query the relationships in the live feed.

Option C is incorrect because it is too generic ("high confidence score," "any feed"). The requirement is specific to the ATI Fusion Feed and APT41.

Option D is incorrect because it describes a post-detection SOAR action. The question explicitly asks how to configure the YARA-L detection rule itself to perform this correlation.

Option B is the only one that describes the correct YARA-L 2.0 methodology. The rule must first define the live event (network connection). Then, it must define the context source (the ATI Fusion Feed). In the events section of the rule, a join is established between the event's external IP field and the IP indicator in the Fusion Feed. Finally, the rule filters the joined context data, looking for attributes such as threat.threat_actor.name = "APT41" or other related_indicators that link back to the specified threat group.

Exact Extract from Google Security Operations Documents:

Applied Threat Intelligence Fusion Feed overview: The Applied Threat Intelligence (ATI) Fusion Feed is a collection of Indicators of Compromise (IoCs), including hashes, IPs, domains, and URLs, that are associated with known threat actors, malware strains, active campaigns, and finished intelligence reporti11ng.12

Write YARA-L rules with the ATI Fusion Feed: Writing YARA-L rules that use the ATI Fusion Feed follows a similar process to writing YARA-L rules that use other context entity sources.13 To write a rule, you filter the selected context entity graph (in this case, Fusion Feed).14

You can join a field from the context entity and UDM event field. In the following example, the placeholder variable ioc is used to do a transitive join between the context entity and the event.

Because this rule can match a large number of events, it is recommended that you refine the rule to match on context entities that have specific intelligence. This allows you to filter for explicit associations, such as a specific threat group or an indicator's presence in a compromised environment.

The most efficient and native solution is to use the Google Cloud operations suite. Google Security Operations (SecOps) automatically exports its own ingestion health metrics to Cloud Monitoring. These metrics provide detailed information about the logs being ingested, including log counts, parser errors, and event counts, and can be filtered by dimensions such as hostname.

To solve this, an engineer would navigate to Cloud Monitoring and create a new alert policy. This policy would be configured to monitor the chronicle.googleapis.com/ingestion/log_entry_count metric, filtering it for the specific hostname of the critical Windows server.

Crucially, Cloud Monitoring alerting policies have a built-in condition type for "metric absence." The engineer would configure this condition to trigger if no data points are received for the specified metric (logs from that server) for a duration of 30 minutes. When this condition is met, the policy will automatically send a notification to the desired channels (e.g., email, PagerDuty). This is the standard, out-of-the-box method for monitoring log pipeline health and requires no custom rules (Option B) or custom heartbeat configurations (Option C).

(Reference: Google Cloud documentation, "Google SecOps ingestion metrics and monitoring"; "Cloud Monitoring - Alerting on metric absence")

The IOC Matches page is the central location in Google Security Operations (SecOps) for reviewing all IoCs that have been automatically correlated against your organization's UDM data. This page is populated by the Applied Threat Intelligence service, which includes feeds from Google, Mandiant, and VirusTotal.

When security exercises (like red teaming or penetration testing) are conducted, they often use known malicious tools or infrastructure that will correctly trigger IoC matches, creating "noise" and contributing to alert fatigue. The platform provides a specific function to manage this: muting.

An analyst can navigate to the IOC Matches page, use filters (such as time, as mentioned in Option B) to identify the specific IoCs associated with the red team exercise, and then select the Mute action for those IoCs. Muting is the correct operational procedure for suppressing known-benign or exercise-related IoCs. This action prevents them from appearing in the main view and contributing to noise, while preserving the historical record of the match. Option D is a prioritization technique, not a suppression one.

(Reference: Google Cloud documentation, "View IoCs using Applied Threat Intelligence"; "View alerts and IoCs"; "Mute or unmute IoC")

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Comprehensive and Detailed Explanation

The correct answers are B and D, as they accurately describe the two primary functions of a modern SecOps platform: SIEM (Detection) and SOAR (Response).

Option B: (Detection Strategy) A SIEM's fundamental purpose is to perform detection. To do this, it must first ingest telemetry (logs) as events. This is the foundational step for any detection and response strategy. Logs from all sources—on-premises (e.g., firewalls, Active Directory) and multi-cloud (e.g., AWS CloudTrail, Azure Activity Logs)—are ingested into Google SecOps, normalized into the Unified Data Model (UDM), and stored as events. This is what allows detection rules to run. (Option C is incorrect as logs are events, not entities).

Option D: (Response Strategy) A SOAR's fundamental purpose is to orchestrate and automate the response to a detection. A key part of this response is event enrichment (or more specifically, observable enrichment). When an alert is ingested by the SOAR, a playbook runs. This playbook uses integrations (e.g., with Mandiant or VirusTotal, which are part of GTI) to query for real-time context on the observables (IPs, hashes, domains) in the alert. This enrichment helps an analyst make a decision or allows the playbook to automate a containment action.

Option A is incorrect because GTI is ingested as context (in the entity graph and Fusion Feed), not as events. Option E is incorrect because "entity enrichment" (e.g., adding user data from AD) happens at the SIEM ingestion level, whereas SOAR integrations perform on-demand enrichment for alerts/events.

Exact Extract from Google Security Operations Documents:

Google SecOps data ingestion: Google Security Operations ingests customer logs, normalizes the data, and detects security alerts. Google SecOps ingests data using... Forwarders, Bindplane agent, Ingestion APIs, Google Cloud. Parsers convert logs from customer systems into a Unified Data Model (UDM) events.

Integrate Mandiant Threat Intelligence with Google SecOps: This document provides guidance on how to integrate Mandiant Threat Intelligence with Google Security Operations (Google SecOps). After you configure an integration instance, you can use it in playbooks.

Actions:

Enrich Entities: Use the Enrich Entities action to enrich entities using the information from Mandiant Threat Intelligence. This action runs on the following Google SecOps entities: Hostname, IP Address, URL, File Hash.

Enrich IOCs: Use this action to enrich indicators of compromise.

Comprehensive and Detailed Explanation

The correct answer is Option C. The question asks for the immediate action to remediate the existing compliance drift, which is the VM that already has an external IP address.

Option C (Remediate): Reconfiguring the VM's network interface to remove the external IP directly fixes the identified misconfiguration. This action brings the resource back into compliance, which will cause the Security Command Center finding to be automatically set to INACTIVE on its next scan.2

Option A (Prevent): Applying the organization policy constraints/compute.vmExternalIpAccess is a preventative control.3 It will stop new VMs from being created with external IPs, but it is not retroactive and does not remove the external IP from the already existing VM. Therefore, it does not remediate the current finding.

Option B (Mask): Removing the tag simply hides the resource from the posture scan. This is a violation of compliance auditing; it masks the problem instead of fixing it.

Option D (Ignore): Marking a finding as fixed without actually fixing the underlying issue is incorrect and will not resolve the compliance drift. The finding will reappear as ACTIVE on the next scan.

Exact Extract from Google Security Operations Documents:

Finding deactivation after remediation: After you remediate a vulnerability or misconfiguration finding, the Security Command Center service that detected the finding automatically sets the state of the finding to INACTIVE the next time the detection service scans for the finding.4 How long Security Command Center takes to set a remediated finding to INACTIVE depends on the schedule of the scan that detects the findin5g.

Organization policy constraints: If enforced, the constraint constraints/compute.vmExternalIpAccess will deny the creation or update of VM instances with IPv4 external IP addresses.6 This constraint is not retroactive and will not restrict the usage of external IPs on existing VM instances. To remediate an existing VM, you must modify the instance's network interface settings and remove the external IP.

This scenario describes an automated external approval, which is a key feature of Google Security Operations (SecOps) SOAR. The solution that "minimizes the effort required by the SOC analyst" is one that is fully automated and does not require the analyst to wait for an email and then manually resume the playbook.

The correct method (Option D) is to use the platform's built-in capabilities (often part of the "Flow" or "Siemplify" integration) to generate a unique approval link (or "Approve" / "Deny" links). These links are tokenized and tied to the specific playbook's execution. This link is then inserted as a placeholder into the email that is sent to the non-SecOps user via the "Send Email" (Gmail integration) action.

The playbook is then configured with conditional logic (e.g., a "Wait for Condition") to pause execution until one of the links is clicked. When the external user clicks the "Approve" or "Deny" link in their email, it sends a secure signal back to the SOAR platform. The playbook automatically detects this response and continues down the appropriate conditional path (e.g., "if approved, execute endpoint containment"). This process is fully automated and requires zero analyst intervention, perfectly meeting the requirements.

Options A, B, and C all require manual analyst action, which violates the core requirement of minimizing analyst effort.

(Reference: Google Cloud documentation, "Google SecOps SOAR Playbooks overview"; "Gmail integration documentation"; "Flow integration - Wait for Approval")

Comprehensive and Detailed Explanation

The correct answer is Option C. The incident description makes it clear that endpoint containment (by EDR) was insufficient, as the attacker successfully pivoted to privileged service accounts and began post-compromise activities (credential dumping, scheduled tasks).

The goal is to automate containment and minimize dwell time.

Option A is an enrichment/investigation action, not a containment action.

Option B is the opposite of automation; adding a manual approval step increases dwell time and response time.

Option D is a detection engineering task (creating a YARA-L rule), not a SOAR playbook (response) action.

Option C is the only true automated containment action that directly addresses the new threat. The anomalous behavior of the privileged accounts would raise their Entity Risk Score within Google SecOps. A modern SOAR playbook can be configured to automatically trigger on this high-risk score and execute an identity-based containment action. Revoking tokens and suspending sessions for the compromised high-privilege accounts is the most effective way to immediately stop the attacker's lateral movement and malicious activity, thereby accelerating containment and minimizing dwell time.

Exact Extract from Google Security Operations Documents:

SOAR Playbooks and Automation: Google Security Operations (SecOps) SOAR enables the orchestration and automation of security responses. Playbooks are designed to execute a series of automated steps to respond to an alert.

Identity and Access Management Integrations: SOAR playbooks can integrate directly with Identity Providers (IdPs) like Google Workspace, Okta, and Microsoft Entra ID. A critical automated containment action for compromised accounts is to revoke active OAuth tokens, suspend user sessions, or disable the account entirely. This action immediately logs the attacker out of all active sessions and prevents them from re-authenticating.

Entity Risk: Detections and anomalous activities contribute to an entity's (e.g., a user or asset) risk score. Playbooks can be configured to use this risk score as a trigger. For example, if a high-privilege account's risk score crosses a critical threshold, the playbook can automatically execute identity containment actions.