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

Comprehensive and Detailed Explanation

The correct solution is Option D. This approach correctly uses the integrated Google Cloud-native tools for both monitoring and alerting.

Google Security Operations (SecOps) automatically streams all ingestion metrics to Google Cloud Monitoring. This includes metrics for throughput (e.g., chronicle.googleapis.com/ingestion/event_count, chronicle.googleapis.com/ingestion/byte_count), parsing errors (e.g., chronicle.googleapis.com/ingestion/parse_error_count), and the health of collection agents (e.g., chronicle.googleapis.com/ingestion/last_seen_timestamp).

Receive a notification (15 minutes): The Data Ingestion and Health dashboard (Option A) is for visualization, and its "reports" are scheduled summaries, not real-time alerts. The only way to get a 15-minute notification is to use Cloud Monitoring. An alerting policy can be configured to trigger when a "metric absence" is detected for a specific collection agent's last_seen_timestamp, fulfilling the "silent source" requirement.

Visualize metrics: Cloud Monitoring also provides a powerful dashboarding service. A Cloud Monitoring dashboard can be built to graph all the necessary metrics—throughput, parsing errors, and agent status—in one place.

Option C is incorrect because it suggests using the Bindplane Observability Pipeline, which is a separate product. Option B is incorrect as Risk Analytics is for threat detection (UEBA), not platform health.

Exact Extract from Google Security Operations Documents:

Use Cloud Monitoring for ingestion insights: Google SecOps uses Cloud Monitoring to send the ingestion notifications. Use this feature for ingestion notifications and ingestion volume viewing.

Set up a sample policy to detect silent Google SecOps collection agents:

In the Google Cloud console, select Monitoring.

Click Create Policy.

On the Select a metric page, select Chronicle Collector > Ingestion > Total ingested log count.

In the Transform data section, set the Time series group by to collector_id.

Click Next.

Select Metric absence and set the Trigger absence time (e.g., 15 minutes).

In the Notifications and name section, select a notification channel.

You can also create custom dashboards in Cloud Monitoring to visualize any of the exported metrics, such as Total ingested log size or Total record count (for parsing).

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

The key requirements are to "proactively hunt," "prioritize investigative actions," and identify "lateral movement" paths before deep log analysis. This is the primary use case for Security Command Center (SCC) Enterprise. SCC aggregates all findings from Google Cloud services and correlates them with assets. By filtering on the GKE cluster, the analyst can see all associated findings (e.g., from Event Threat Detection) which may contain initial IoCs.

More importantly, SCC's attack path simulation feature is specifically designed to "prioritize investigative actions" by modeling how an attacker could move laterally. It visualizes the chain of exploits—such as a misconfigured GKE service account with excessive permissions, combined with a public-facing service—that an attacker could use to pivot from the development cluster to high-value production systems. Each path is given an attack exposure score, allowing the hunter to immediately focus on the most critical risks.

Option C is too narrow, as it only checks for malware on nodes, not the lateral movement path. Option B is a later step used to enrich IoCs after they are found. Option D is an automated response (SOAR), not a proactive hunting and prioritization step.

(Reference: Google Cloud documentation, "Security Command Center overview"; "Attack path simulation and attack exposure scores")

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

(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")

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 : The expression evaluates to true if it is true for at least one of the values in the repeated field.

all : 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"))

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")

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 actions are C and D, as they represent the standard, parallel process for incident response: technical investigation and procedural/communicative response.

Technical Investigation (Option D): The immediate priority is to understand the alert. An analyst must review the Container Threat Detection finding in Security Command Center (SCC) to understand what was detected. This is followed by investigating the affected pod, its container, the node it's running on, and any associated service accounts to determine the initial blast radius and gather forensic data. Researching the binary and related TTPs (Tactics, Techniques, and Procedures) helps contextualize the attack.

Procedural Response (Option C): Concurrently, the organizational response plan must be activated. This involves notifying the business-critical workload owner (stakeholder communication), initiating the formal, documented incident response playbook, and escalating to specialized teams, like threat hunting, for deeper root cause analysis that goes beyond the initial triage.

Option A is incorrect because deleting the pod immediately is a premature remediation step that destroys critical forensic evidence. Option B is incorrect because "keeping the cluster and pod running" without any containment is reckless and could allow an attacker to pivot. Option E is incorrect because an unauthorized binary execution in a critical workload is a high-severity event, not a low-severity finding to be silenced.

Exact Extract from Google Security Operations Documents:

Responding to Container Threat Detection findings: When a Container Threat Detection finding is generated, it indicates a potential security issue that requires investigation. The first step is to review the finding details in Security Command Center (SCC) to understand the nature of the threat, such as K8S_BINARY_EXECUTED.

The recommended workflow involves:

Investigate: Examine the affected Kubernetes resources, such as the Pod, Container, and Node. Use tools like kubectl to inspect the pod configuration, running processes, and network connections. Research the associated attack and response methods to understand the threat actor's TTPs.

Respond: Follow the organization's incident response playbook. This includes notifying the workload owner and relevant stakeholders. Contain the threat by isolating the pod or node, but avoid deleting resources immediately to preserve evidence for forensic analysis.

Escalate: For complex incidents, engage the threat hunting or forensics team to conduct a thorough investigation, identify the root cause, and determine the full scope of the compromise.

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

The requirement to detect activity that is *unusual* compared to a *user's established baseline* is the precise definition of **User and Endpoint Behavioral Analytics (UEBA)**. This is a core capability of Google Security Operations Enterprise designed to solve this exact problem with **minimal effort**.

Instead of requiring analysts to write and tune custom rules with static thresholds (like in Option A) or configure external metrics (Option B), the UEBA engine automatically models the behavior of every user and entity. By simply **enabling the curated UEBA detection rulesets**, the platform begins building these dynamic baselines from historical log data.

When a user's activity, such as data download volume, significantly deviates from their *own* normal, established baseline, a UEBA detection (e.g., `Anomalous Data Download`) is automatically generated. These anomalous findings and other risky behaviors are aggregated into a risk score for the user. Analysts can then use the **Risk Analytics dashboard** to proactively identify the highest-risk users and investigate the specific anomalous activities that contributed to their risk score. This built-in, automated approach is far superior and requires less effort than maintaining static, noisy thresholds.

*(Reference: Google Cloud documentation, "User and Endpoint Behavioral Analytics (UEBA) overview"; "UEBA curated detections list"; "Using the Risk Analytics dashboard")*

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

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")

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"