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Google Security-Operations-Engineer - First-grade Google Cloud Certified - Professional Security Operations Engineer (PSOE) Exam Interactive Practice Exam

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Google Security-Operations-Engineer Exam Syllabus Topics:

Topic
Details

Topic 1

Incident Response: This section of the exam measures the skills of Incident Response Managers and assesses expertise in containing, investigating, and resolving security incidents. It includes evidence collection, forensic analysis, collaboration across engineering teams, and isolation of affected systems. Candidates are evaluated on their ability to design and execute automated playbooks, prioritize response steps, integrate orchestration tools, and manage case lifecycles efficiently to streamline escalation and resolution processes.

Topic 2

Monitoring and Reporting: This section of the exam measures the skills of Security Operations Center (SOC) Analysts and covers building dashboards, generating reports, and maintaining health monitoring systems. It focuses on identifying key performance indicators (KPIs), visualizing telemetry data, and configuring alerts using tools like Google SecOps, Cloud Monitoring, and Looker Studio. Candidates are assessed on their ability to centralize metrics, detect anomalies, and maintain continuous visibility of system health and operational performance.

Topic 3

Detection Engineering: This section of the exam measures the skills of Detection Engineers and focuses on developing and fine-tuning detection mechanisms for risk identification. It involves designing and implementing detection rules, assigning risk values, and leveraging tools like Google SecOps Risk Analytics and SCC for posture management. Candidates learn to utilize threat intelligence for alert scoring, reduce false positives, and improve rule accuracy by integrating contextual and entity-based data, ensuring strong coverage against potential threats.

Topic 4

Platform Operations: This section of the exam measures the skills of Cloud Security Engineers and covers the configuration and management of security platforms in enterprise environments. It focuses on integrating and optimizing tools such as Security Command Center (SCC), Google SecOps, GTI, and Cloud IDS to improve detection and response capabilities. Candidates are assessed on their ability to configure authentication, authorization, and API access, manage audit logs, and provision identities using Workforce Identity Federation to enhance access control and visibility across cloud systems.

Topic 5

Threat Hunting: This section of the exam measures the skills of Cyber Threat Hunters and emphasizes proactive identification of threats across cloud and hybrid environments. It tests the ability to create and execute advanced queries, analyze user and network behaviors, and develop hypotheses based on incident data and threat intelligence. Candidates are expected to leverage Google Cloud tools like BigQuery, Logs Explorer, and Google SecOps to discover indicators of compromise (IOCs) and collaborate with incident response teams to uncover hidden or ongoing attacks.

Google Cloud Certified - Professional Security Operations Engineer (PSOE) Exam Sample Questions (Q42-Q47):

NEW QUESTION # 42
Your organization plans to ingest logs from an on-premises MySQL database as a new log source into its Google Security Operations (SecOps) instance. You need to create a solution that minimizes effort. What should you do?

A. Configure direct ingestion from your Google Cloud organization.
B. Configure and deploy a Google SecOps forwarder.
C. Configure and deploy a Bindplane collection agent
D. Configure a third-party API feed in Google SecOps.

Answer: B

Explanation:
Comprehensive and Detailed 150 to 250 words of Explanation From Exact Extract Google Security Operations Engineer documents:
The standard, native, and minimal-effort solution for ingesting logs from on-premises sources into Google Security Operations (SecOps) is to use the Google SecOps forwarder. The forwarder is a lightweight software component (available as a Linux binary or Docker container) that is deployed within the customer's network. It is designed to collect logs from a variety of on-premises sources and securely forward them to the SecOps platform.
The forwarder can be configured to monitor log files directly (which is a common output for a MySQL database) or to receive logs via syslog. Once the forwarder is installed and its configuration file is set up to point to the MySQL log file or syslog stream, it handles the compression, batching, and secure transmission of those logs to Google SecOps. This is the intended and most direct ingestion path for on-premises telemetry.
Option C is incorrect because the log source is on-premises, not within the Google Cloud organization. Option B (API feed) is the wrong mechanism; feeds are used for structured data like threat intelligence or alerts, not for raw telemetry logs from a database. Option A (Bindplane) is a third-party partner solution, which may involve additional configuration or licensing, and is not the native, minimal-effort tool provided directly by Google SecOps for this task.
(Reference: Google Cloud documentation, "Google SecOps data ingestion overview"; "Install and configure the SecOps forwarder")

NEW QUESTION # 43
You are implementing Google Security Operations (SecOps) with multiple log sources. You want to closely monitor the health of the ingestion pipeline's forwarders and collection agents, and detect silent sources within five minutes. What should you do?

A. Create a notification in Cloud Monitoring using a metric-absence condition based on sample policy for each collector_id.
B. Create an ingestion notification for health metrics in Cloud Monitoring based on the total ingested log count for each collector_id.
C. Create a Looker dashboard that queries the BigQuery ingestion metrics schema for each log_type and collector_id.
D. Create a Google SecOps dashboard that shows the ingestion metrics for each iog_cype and collector_id.

Answer: A

Explanation:
Comprehensive and Detailed Explanation
The correct solution is Option B. This question requires a low-latency (5 minutes) notification for a silent source.
The other options are incorrect for two main reasons:
* Dashboards vs. Notifications: Options C and D are incorrect because dashboards (both in Looker and Google SecOps) are for visualization, not active, real-time alerting. They show you the status when you look at them but do not proactively notify you of a failure.
* Metric-Absence vs. Metric-Value: Google SecOps streams all its ingestion health metrics to Google Cloud Monitoring, which is the correct tool for real-time alerting. However, Option A is monitoring the "total ingested log count." This metric would require a threshold (e.g., count < 1), which can be problematic. The specific and most reliable method to detect a "silent source" (one that has stopped sending data entirely) is to use a metric-absence condition. This type of policy in Cloud Monitoring triggers only when the platform stops receiving data for a specific metric (grouped by collector_id) for a defined duration (e.g., five minutes).
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... You can integrate email notifications into existing workflows.
Set up a sample policy to detect silent Google SecOps collection agents:
* In the Google Cloud console, select Monitoring.
* Click Create Policy.
* Select a metric, such as chronicle.googleapis.com/ingestion/log_count.
* In the Transform data section, set the Time series group by to collector_id.
* Click Next.
* Select Metric absence and do the following:
* Set Alert trigger to Any time series violates.
* Set Trigger absence time to a time (e.g., 5 minutes).
* In the Notifications and name section, select a notification channel.
References:
Google Cloud Documentation: Google Security Operations > Documentation > Ingestion > Use Cloud Monitoring for ingestion insights

NEW QUESTION # 44
A Google Security Operations (SecOps) detection rule is generating frequent false positive alerts. The rule was designed to detect suspicious Cloud Storage enumeration by triggering an alert whenever the storage.
objects.list API operation is called using the api.operation UDM field. However, a legitimate backup automation tool that uses the same API, causing the rule to fire unnecessarily. You need to reduce these false positives from this trusted backup tool while still detecting potentially malicious usage. How should you modify the rule to improve its accuracy?

A. Convert the rule into a multi-event rule that looks for repeated API calls across multiple buckets.
B. Add principal.user.email != "backup-bot@fcobaa.com" to the rule condition to exclude the automation account.
C. Replace api.operation with api.service_name = "storage.googleapis.com" to narrow the detection scope.
D. Adjust the rule severity to low to deprioritize alerts from automation tools.

Answer: B

Explanation:
Comprehensive and Detailed Explanation
The correct solution is Option D. The problem is that a known, trusted principal (the backup tool's service account) is performing a legitimate action (storage.objects.list) that happens to look like the suspicious behavior the rule is designed to catch.
The most precise and effective way to reduce these false positives without weakening the rule's ability to catch malicious actors is to create an exception for the trusted principal.
By adding principal.user.email != "backup-bot@fcobaa.com" (or the equivalent principal.user.userid) to the events or condition section of the YARA-L rule, the rule will now only evaluate events where the actor is not the known-good backup bot.
* Option A is incorrect because it just lowers the priority of the false positive; it doesn't stop it from being generated.
* Option B is incorrect because the legitimate tool might also perform repeated calls, leading to the same false positive.
* Option C is incorrect because api.service_name = "storage.googleapis.com" is less specific than api.
operation = "storage.objects.list" and would likely increase the number of false positives by triggering on any storage API call.
Exact Extract from Google Security Operations Documents:
Reduce false positives: When a detection rule generates false positives due to known-benign activity (e.g., from an administrative script or automation tool), the best practice is to add a not condition to the rule to exclude the trusted entity.8 You can filter on UDM fields to create exceptions. For example, to prevent a rule from firing on activity from a specific service account, you can add a condition to the events section such as:
and $e.principal.user.userid != "trusted-service-account@project.iam.gserviceaccount.com" This technique, often called "allow-listing" or "suppression," improves the rule's accuracy by focusing only on unknown or untrusted principals.
References:
Google Cloud Documentation: Google Security Operations > Documentation > Detections > Overview of the YARA-L 2.0 language > Add not conditions to prevent false positives

NEW QUESTION # 45
You are responsible for monitoring the ingestion of critical Windows server logs to Google Security Operations (SecOps) by using the Bindplane agent. You want to receive an immediate notification when no logs have been ingested for over 30 minutes. You want to use the most efficient notification solution. What should you do?

A. Create a new YARA-L rule in Google SecOps SIEM to detect the absence of logs from the server within a 30-minute window.
B. Configure the Windows server to send an email notification if there is an error in the Bindplane process.
C. Configure a Bindplane agent to send a heartbeat signal to Google SecOps every 15 minutes, and create an alert if two heartbeats are missed.
D. Create a new alert policy in Cloud Monitoring that triggers a notification based on the absence of logs from the server's hostname.

Answer: D

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

NEW QUESTION # 46
Your organization uses Security Command Center Enterprise (SCCE). You are creating models to detect anomalous behavior. You want to programmatically build an entity data structure that can be used to query the connections between resources in your Google Cloud environment. What should you do?

A. Employ attack path simulation with high-value resource sets to simulate potential lateral movement.
B. Create a Bash script to iterate through various resource types using gcloud CLI commands, and export a CSV file. Load this data into BigQuery for analysis.
C. Use the Cloud Asset Inventory relationship table, and ingest the data into Spanner Graph.
D. Navigate to the Asset Query tab, and join resources from the Cloud Asset Inventory resource table.
Export the results to BigQuery for analysis.

Answer: C

Explanation:
Comprehensive and Detailed Explanation
The key requirement is to programmatically build a data structure to query the connections (i.e., a graph) between resources. Security Command Center (SCC) Enterprise is built upon the data provided by Cloud Asset Inventory (CAI).1 Cloud Asset Inventory provides two primary types of data: resources (the "nodes" of a graph) and relationships (the "edges" of a graph).2
* Option B is incorrect because it focuses on the resource table. While the resource table contains the assets themselves, it is the relationship table that specifically stores the connections between them (e.
g., a compute.googleapis.com/Instance is ATTACHED_TO a compute.googleapis.com/Network).
* Option A (attack path simulation) is a feature that consumes this graph data; it is not the method used to build the data structure for programmatic querying.
* Option C (Bash script) is a manual, inefficient, and incomplete method that would fail to capture the complex relationships that CAI tracks automatically.
* Option D is the correct solution. The Cloud Asset Inventory relationship table is the precise source for all resource connections. To effectively query these connections as an entity data structure (a graph), the ideal destination is a graph database. Spanner Graph is Google Cloud's managed graph database service, designed specifically for storing and querying highly interconnected data, making it the perfect tool for analyzing resource relationships and potential attack paths.3 Exact Extract from Google Security Operations Documents:
Relationships in Cloud Asset Inventory: Cloud Asset Inventory (CAI) provides relationship data, which allows you to understand the connections between your Google Cloud resources.4 CAI models relationships as a graph. You can export this relationship data for analysis. The relationship service stores information about the relationships between resources. For example, a Compute Engine instance might have a relationship with a persistent disk, or an IAM policy binding might have a relationship with a project.
Spanner Graph: Spanner Graph is a graph database built on Cloud Spanner that lets you store and query your graph data at scale.5 It is suitable for use cases that involve complex relationships, such as security analysis, fraud detection, and recommendation engines. By ingesting the Cloud Asset Inventory relationship table into Spanner Graph, you can programmatically execute graph queries to explore connections, identify high-risk assets, and model potential lateral movement paths.
References:
Google Cloud Documentation: Cloud Asset Inventory > Documentation > Analyzing asset relationships Google Cloud Documentation: Spanner > Documentation > Spanner Graph > Overview Google Cloud Documentation: Security Command Center > Documentation > Key concepts > Attack path simulation

NEW QUESTION # 47
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