Security-Operations-Engineer Google Cloud Certified - Professional Security Operations Engineer (PSOE) Exam Questions and Answers

Comprehensive and Detailed Explanation

The correct solution is Option A. The key requirement is to "improve" the previous manual "watchlist" process.

In Google Security Operations, "data tables" (mentioned in options C and D) are the modern equivalent of watchlists or reference lists.1 Using a data table would replicate the old, static process and would not be an improvement.

The superior method in Google SecOps is to ingest this data as Entity Context. This is a core feature where context data (like user information from AD or asset data from a CMDB) is ingested via a feed or the Context API. Google SecOps then uses this data to automatically enrich all incoming security events (UDM) in real-time.

When a log for john.doe is ingested, it is automatically enriched with the context data from AD, such as "John Doe," "Marketing Department," "Manager: Jane Smith," etc. This enriched information is then available for detection, hunting, and investigation. This is a significant improvement because it provides continuous, automatic enrichment at ingestion, rather than requiring a manual update of a static table or only enriching after an alert is generated (Option B).

Exact Extract from Google Security Operations Documents:

UDM enrichment and aliasing overview: Google Security Operations (SecOps) supports aliasing and enrichment for assets and users.2 Aliasing enables enrichment.3 For example, using aliasing, you can find the job title and employment status associated with a user ID.4

How aliasing works: User aliasing uses the USER_CONTEXT event type for aliasing.5 This contextual data is stored as entities in the Entity Graph.6 When new Unified Data Model (UDM) events are ingested, enrichment uses this aliasing data to add context to the UDM event.7 For example, a UDM event might include principal.user.userid = "jdoe". 8The enrichment process populates the principal.user noun with the entity data, such as user.user_display_name = "John Doe" and user.department = "Marketing".

This is the recommended method for ingesting organizational context from sources like Microsoft Windows Active Directory, as it makes the contextual data available for all subsequent detection, search, and investigation activities.

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

To import findings specifically for Google SecOps SOAR actions (formerly Siemplify), you utilize the Marketplace Integrations.

The standard procedure for connecting external alerts to the SOAR platform is to install the specific integration (connector) from the Marketplace. The documentation states: "Google Security Operations SOAR includes a Marketplace where you can find and install integrations... The Google Cloud Security Command Center integration allows you to ingest findings as alerts."

The configuration involves enabling the integration instance and providing authentication credentials (often a Service Account Key or API Key depending on the specific integration version and endpoint). Option B correctly identifies the "Install the SCC integration from the Google SecOps Marketplace" step as the primary mechanism for SOAR ingestion.

Options C and D describe the architecture for ingesting logs into the SIEM (Detection/Chronicle) layer using Pub/Sub feeds, rather than the API-based polling or fetching used by SOAR integrations to create cases.

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.

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.

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.

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 solution is Option C. Google SecOps SOAR has a specific feature designed for this exact use case: Advanced Reports.

The standard "SOAR Reports" (Option A) are pre-canned dashboard-style reports (e.g., Management - SOC Status). However, the "Advanced Reports" feature (built on Looker) provides a powerful, flexible interface for building highly customized, tabular reports based on case data. This allows an administrator to specifically query for case resolutions and SLA metrics, and filter them by priority = High OR Critical.

Most importantly, the Advanced Reports feature has a built-in scheduler. This scheduler can be configured to run the report at a specific cadence (e.g., "Weekly on Monday at 9:00 AM"), send it to a list of email recipients, and attach the data in the required format, including CSV and as a zipped file.

Option B is incorrect because detection rules create alerts, they don't report on case metrics. Option D is incorrect because it mixes the SIEM search function with a SOAR job, which is an overly complex and unnecessary way to query case data that is already structured within the SOAR module.

Exact Extract from Google Security Operations Documents:

Explore advanced SOAR reports: The default advanced SOAR reports are a set of dashboards and reports to help track SOC performance, case handling, analyst workload, and automation efficiency. These reports provide both high-level and detailed insights across your environments.1

SLA Monitoring: Use Triage Time and SLA Met flag to monitor SLA compliance and improve case handling.

Manage advanced reports: You can create, edit, duplicate, share, download, and delete advanced reports.

Schedule a report:

Select the report you want to schedule.

Select the Scheduler tab and click Add.

In the New Schedule dialog, click the Enable toggle to turn on scheduling and enter the required information (e.g., weekly, Monday, email recipients).

You can select the delivery format, including CSV and ZIP attachments.

This YARA-L rule is designed to correlate a real-time event (a DNS query, $dns) with known-bad indicators stored in the Google SecOps entity graph ($ioc). The code must correctly filter the entity graph to find the specific indicators from the custom MISP feed.

Two filters are required:

$ioc.graph.metadata.entity_type = "DOMAIN_NAME": This line is essential to filter the entity graph for IoCs that are domains. The rule is trying to match a DNS query ($dns_query) to a known C2 domain, so the entity type must be DOMAIN_NAME.

$ioc.graph.metadata.source_type = "ENTITY_CONTEXT": This is the key differentiator. The Google SecOps entity graph has multiple context sources. GLOBAL_CONTEXT (Option B) is for threat intelligence provided by Google (e.g., Google Threat Intelligence, Mandiant). DERIVED_CONTEXT (Option C) is for context inferred from UDM events. The prompt explicitly states the IoC feed is the organization's own "threat intelligence feed... ingested... with... MISP." This type of customer-provided, third-party intelligence is classified as ENTITY_CONTEXT. Adding this line ensures the rule only uses the custom MISP feed for its IoC data, as intended.

The other lines in the $ioc block, such as product_name = "MISP", further refine this ENTITY_CONTEXT search.

(Reference: Google Cloud documentation, "YARA-L 2.0 language syntax"; "Context-aware detections with entity graph"; "Populate the entity graph")

This is a standard Identity and Access Management (IAM) permission issue. When Google Security Operations (SecOps) exports data, it uses its own service account (often named service-@gcp-sa-bigquerydatatransfer.iam.gserviceaccount.com or a similar SecOps-specific principal) to perform the write operation. The user account that schedules the report (Option C) is only relevant for the scheduling action, not for the data transfer itself. For the export to succeed, the Google SecOps service account principal must have explicit permission to write data into the target BigQuery dataset.

The predefined IAM role roles/bigquery.dataEditor grants the necessary permissions to create, update, and delete tables and table data within a dataset. By granting this role to the Google SecOps service account on the specific dataset, you authorize the service to write the report results and populate the tables. Option A (serviceAccountUser) is incorrect as it's used for service account impersonation, not for granting data access. Option B (retention period) is a data lifecycle setting and has no impact on the ability to write new data. The most common cause for this exact scenario—a successful job run with no data appearing—is that the service account lacks the required bigquery.dataEditor permissions on the destination dataset.

(Reference: Google Cloud documentation, "Troubleshoot transfer configurations"; "Control access to resources with IAM"; "BigQuery predefined IAM roles")

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.

Comprehensive and Detailed Explanation

The correct configuration is Option A. This answer addresses two key requirements from the question: the identity mechanism (Cloud Identity) and the required permission level (read-only access including detection rules).

Identity Mechanism (Google Group vs. Workforce Pool):

The prompt explicitly states the organization uses Cloud Identity as its identity provider (IdP). When Cloud Identity or Google Workspace is the IdP, the standard practice is to manage access using Google Groups. Users are added to a group, and IAM roles are granted to that group. Workforce identity federation (which uses workforce pools) is the mechanism used when integrating with a third-party IdP, such as Okta or Azure AD. Since the IdP is Cloud Identity, creating a Google Group is the correct approach. This eliminates options C and D.

Permission Level (roles/chronicle.viewer vs. roles/chronicle.limitedViewer):

The prompt requires "read-only access to all resources, including detection engine rules." The predefined Google SecOps IAM roles are specific about this distinction:

roles/chronicle.viewer (Chronicle API Viewer): Provides "Read-only access to Google SecOps application and API resources." This role includes permissions to view detection rules and retrohunts.

roles/chronicle.limitedViewer (Chronicle API Limited Viewer): Provides "Grants read-only access to Google SecOps application and API resources, excluding detection engine rules and retrohunts."

Therefore, roles/chronicle.limitedViewer (Option B) is incorrect because it excludes access to detection engine rules, which violates the prompt's requirement. The correct role is roles/chronicle.viewer (Option A), as it grants the necessary comprehensive read-only access.

Exact Extract from Google Security Operations Documents:

On the topic of IAM roles:

Google SecOps predefined roles in IAM

Predefined role in IAM

Title

Description

roles/chronicle.viewer1

Chronicle API Viewer2

Read-only access to Google SecOps application and API resources3

roles/chronicle.limitedViewer4

Chronicle API Limited Viewer5

Grants read-only access to Google SecOps application and API resources, excluding detection engine rules and retro6hunts.

On the topic of Identity Providers:

"You can use Cloud Identity, Google Workspace, or a third-party identity provider (such as Okta or Azure AD) to manage users, groups, and authentication. This page describes how to use Cloud Identity or Google Workspace."7

"8The following example grants the Chronicle API Viewer role to to a specific group:"

gcloud projects add-iam-policy-binding PROJECT_ID \

--role roles/chronicle.viewer \

--member "group:GROUP_EMAIL"

The key requirement is to *automate* the extraction of data to *minimize analyst effort*. This is a core function of Google Security Operations SOAR (formerly Siemplify). The **Siemplify integration** provides the foundational playbook actions for case management and entity manipulation.

The **`Create Entity`** action is designed to programmatically add new entities (like users, IPs, or domains) to the active case. To make this action automatic, the playbook developer must use the **Expression Builder**. The Expression Builder is the tool used to parse the JSON output from a previous action (the UDM query) and dynamically map the results (the list of usernames) into the parameters of a subsequent action.

By using the Expression Builder to configure the `Entities Identifier` parameter of the `Create Entity` action, the playbook automatically extracts all `principal.user.userid` fields from the UDM query results and adds them to the case. These new entities can then be automatically passed to the next playbook step, such as "Reset Password."

Options A and C are incorrect because they are **manual** actions. They require an analyst to intervene, which does *not* minimize effort. Option D is incorrect as it creates multiple, unnecessary cases, flooding the queue instead of enriching the single, original phishing case.

*(Reference: Google Cloud documentation, "Google SecOps SOAR Playbooks overview"; "Using the Expression Builder"; "Marketplace and Integrations")*

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

To achieve improved coverage and reduced false positives "as quickly as possible," the correct action is to enable curated detections. These are pre-built rules managed entirely by Google, removing the need for internal development time.2

According to Google Security Operations documentation, Curated Detections are "built by our Google Cloud Threat Intelligence (GCTI) team, and are actively maintained to reduce manual toil in your team."3 The documentation explicitly highlights their speed and fidelity: "Our detections provide security teams with high quality, actionable, out-of-the-box threat detection content...4 This release helps understaffed and overstressed security teams... quickly identify threats."5

Furthermore, Curated Detections are categorized into "Precise" and "Broad" types to directly address false positive concerns.6 The documentation states: "Precise rules: Find malicious behavior with a higher degree of confidence with fewer false positives due to the more specific nature of the rule."7 By enabling these, an organization immediately gains high-fidelity coverage without the lead time required to "Develop" or "Design" custom YARA-L rules (Options C and D) or the potential noise of raw TIP data (Option B).8

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")

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")