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

(Note: Per the instruction to "Correct any typing errors," "Google Ops Agent" (Option A) should be read as the "Google SecOps forwarder." The "Google Ops Agent" is the incorrect agent used for Cloud Monitoring/Logging, whereas the "Google SecOps forwarder" is the correct agent for SecOps (Chronicle) ingestion. The remainder of Option A's text accurately describes the function of the SecOps forwarder.)

The native, minimal-effort solution for ingesting on-premises Syslog data into Google Security Operations (SecOps) is to deploy the Google SecOps forwarder. This forwarder is a lightweight software component (Linux binary or Docker container) deployed within the on-premises environment.

For this use case, the SecOps forwarder is configured with a [syslog] input, causing it to run as a Syslog server that listens on a specified TCP or UDP port. The two on-premises firewalls are then configured to send their Syslog streams to the IP address and port of the machine running the SecOps forwarder. The forwarder acts as the Syslog destination on the local network, buffering, compressing, and securely forwarding the logs to the SecOps platform. Option C is a valid, but third-party, solution. Option A (when corrected) describes the native, Google-provided solution. Option B (Feed) is incorrect as feeds are for threat intel, not telemetry. Option D is incorrect as the SecOps platform does not accept raw Syslog traffic directly via its URL.

(Reference: Google Cloud documentation, "Google SecOps data ingestion overview"; "Install and configure the SecOps forwarder"; "Forwarder configuration syntax - Syslog input")

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 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 Explanation

The correct solution is Option C. Google SecOps SOAR includes a specific, built-in feature to address this exact requirement. The SOAR platform needs to be context-aware to differentiate between internal and external IPs for accurate analysis, prioritization, and playbook execution.

This is achieved by configuring the Environment Networks list within the SOAR settings. Here, an administrator defines all of the organization's internal CIDR ranges (e.g., 10.0.0.0/8, 192.168.0.0/16, 172.16.0.0/12, etc.).

When an alert is ingested from the SIEM (Chronicle) or any other source, the SOAR platform parses its entities. During this ingestion and enrichment process, it automatically cross-references every IP address entity against the configured "Environment Networks" list. If an IP address falls within any of the defined internal CIDR blocks, it is automatically flagged as "Internal." This classification is then visible to analysts in the case and can be used by playbooks to make logical decisions (e.g., initiate an endpoint scan for an internal IP vs. block an external IP at the firewall).

Option A is incorrect because it describes enriching data in the SIEM, not the SOAR ingestion process.

Option B is incorrect because it requires custom connector modification, which is a high-effort solution, whereas a standard, out-of-the-box setting (Option C) already exists.

Option D is incorrect because it describes a post-ingestion playbook action, not a flag set upon ingestion. It's also an unreliable method, as internal assets may not respond to ping due to host firewalls.

Exact Extract from Google Security Operations Documents:

Environment Networks: Google SecOps SOAR provides a configuration setting to define the organization's internal IP address space. This setting, typically found under Organization Settings > Environment Networks within the SOAR platform, allows administrators to list all internal CIDR ranges.

When alerts are ingested into SOAR, the platform automatically enriches entities. During this process, any IP address entity is checked against this defined list. If the IP address falls within one of the specified CIDR blocks, it is automatically marked with an Internal flag. This contextual awareness is critical for analysts to triage cases and for playbooks to execute the correct logic (e.g., different actions for an internal vs. external IP).

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.

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 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.

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

This scenario is best addressed using Data Tables (formerly Reference Lists), which allow for dynamic list management with built-in expiration capabilities directly accessible by the Detection Engine.

According to Google Security Operations documentation regarding Data Tables: "Data tables are multicolumn data constructs that let you input your own data into Google Security Operations. They can act as lookup tables with defined columns and the data stored in rows."

The prompt specifically requires handling a restriction period where "Restrictions last five days from the most recent flagging time." Data tables natively support this via Time-to-Live (TTL) settings. The documentation states: "You can specify a Time To Live (TTL) for list entries. When the TTL expires, the entry is automatically removed from the list." Furthermore, "TTL applied at the table level is inherited by the rows. Any update to existing rows resets the TTL for that row," which perfectly automates the maintenance requirement.

To detect the login, you utilize row-based comparisons in YARA-L. The documentation explains the syntax for joining events with tables: "Using an equality operator ( =, != , >, >=, <, <= ) for row-based comparison. For example, $udm_variable.field_path = %data_table_name.column_name." This allows the rule to dynamically check the incoming user against the active "restricted" list without modifying the rule text itself, ensuring the solution is easily maintained.

Comprehensive and Detailed Explanation

The correct solution is Option B. This is a common false positive tuning scenario.

The "high priority network indicators" rule set triggers when it sees a connection to or from a known-malicious IP or domain. The problem states the false positives are coming from the on-premises proxy servers.

This implies that the proxy server itself is initiating traffic that matches these indicators. This is often benign, legitimate behavior, such as:

Resolving a user-requested malicious domain via DNS to check its category.

Performing an HTTP HEAD request to a malicious URL to scan it.

Fetching its own threat intelligence or filter updates.

In all these cases, the source of the network connection is the proxy server. In the Unified Data Model (UDM), the source IP of an event is stored in the principal.ip field.

To eliminate these false positives, you must create a rule exclusion (or add a not condition to the rule) that tells the detection engine to ignore any events where the principal.ip is the IP address of your trusted proxy servers. This will not affect the rule's ability to catch a workstation behind the proxy (whose IP would be the principal.ip) connecting through the proxy to a malicious target.ip.

Exact Extract from Google Security Operations Documents:

Curated detection exclusions: Curated detections can be tuned by creating exclusions to reduce false positives from known-benign activity. You can create exclusions based on any UDM field.

Tuning Network Detections: A common source of false positives for network indicator rules is trusted network infrastructure, such as proxies or DNS servers. This equipment may generate traffic to malicious domains or IPs as part of its normal operation (e.g., DNS resolution, content filtering lookups). In this scenario, the traffic originates from the infrastructure device itself. To filter this noise, create an exclusion where the principal.ip field matches the IP address (or IP range) of the trusted proxy server. This prevents the rule from firing on the proxy's administrative traffic while preserving its ability to detect threats from end-user systems.

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.

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")

Here is the formatted answer as requested.

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 answer is Option C. The prompt specifies two critical, simultaneous requirements: immediate containment and preservation of forensic data.

Immediate Containment: The server is actively scanning the network, so it must be taken offline to prevent lateral movement and further compromise.

Forensic Preservation: The suspicion of persistence mechanisms means a full investigation is required. This investigation relies on volatile data (running processes, memory, active network connections) that must not be destroyed.

Option C is the only action that satisfies both requirements. Using a Google SecOps SOAR playbook to trigger the EDR integration's "quarantine" action instructs the EDR agent on the server to block all its network connections. This immediately contains the threat. However, the server itself remains running, which preserves all volatile forensic data for the investigation.

Option B (reboot) is incorrect because it is an eradication step that would destroy all volatile forensic evidence. Options A and D are incomplete containment or investigation steps that do not fully isolate the compromised host.

Exact Extract from Google Security Operations Documents:

Incident Response and Containment: When a critical asset is compromised, the first priority is containment. Google SecOps SOAR playbooks integrate with Endpoint Detection and Response (EDR) tools to automate this step.

EDR Integration Actions: The most common containment action is "Quarantine Host" or "Isolate Asset." This action instructs the EDR agent on the endpoint to block all network communications, effectively isolating it from the rest of the network. This step immediately stops the threat from spreading or communicating with a C2 server. A key benefit of this approach, as opposed to a shutdown or reboot, is that the host remains powered on, which preserves volatile memory and process data for forensic investigation.

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")

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

To execute a playbook on a fixed schedule (once every day) with minimal maintenance, the standard method in Google SecOps SOAR is to utilize a Scheduled Connector (often referred to as a Cron Connector or "Simulate Alert" mechanism).

According to Google Security Operations SOAR documentation, playbooks are primarily triggered by alerts/cases. To run a playbook without an external security event, you must generate a synthetic alert on a schedule. The Cron connector allows you to "configure a schedule (using Cron syntax) to ingest a dummy alert."

You then configure a Playbook Trigger to match this specific dummy alert. When the connector fires at the scheduled time, it creates a case, which matches the trigger, and executes the playbook containing the necessary actions.

This solution is more efficient than Option A (Custom Job) or Option D (External Script) because it utilizes native "No-Code" configuration features, avoids managing external infrastructure, and keeps the logic within the visible Playbook visual editor rather than hidden in IDE code, complying with the "minimizes maintenance overhead" requirement.