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Google Security-Operations-Engineer Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 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	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.

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Google Cloud Certified - Professional Security Operations Engineer (PSOE) Exam Sample Questions (Q105-Q110):

NEW QUESTION # 105
You use Google Security Operations (SecOps) curated detections and YARA-L rules to detect suspicious activity on Windows endpoints. Your source telemetry uses EDR and Windows Events logs. Your rules match on the principal.user.userid UDM field. You need to ingest an additional log source for this field to match all possible log entries from your EDR and Windows Event logs.
What should you do?

A. Ingest logs from Windows Procmon.
B. Ingest logs from Windows PowerShell.
C. Ingest logs from Microsoft Entra ID.
D. Ingest logs from Windows Sysmon.

Answer: D

Explanation:
To ensure the principal.user.userid field captures all relevant activity, you should ingest logs from Windows Sysmon. Sysmon provides detailed system activity, including process creation, network connections, and user context, which complements EDR and Windows Event logs, allowing YARA-L rules to match across all endpoint telemetry.

NEW QUESTION # 106
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. Use the Cloud Asset Inventory relationship table, and ingest the data into Spanner Graph.
B. Employ attack path simulation with high-value resource sets to simulate potential lateral movement.
C. Navigate to the Asset Query tab, and join resources from the Cloud Asset Inventory resource table.
Export the results to BigQuery for analysis.
D. 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.

Answer: A

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 # 107
You were recently hired as a SOC manager at an organization with an existing Google Security Operations (SecOps) implementation. You need to understand the current performance by calculating the mean time to respond or remediate (MTTR) for your cases. What should you do?

A. Create a multi-event detection rule to calculate the response metrics in the outcome section based on the entity graph. Create a dashboard based on these metrics.
B. Create a playbook block that can be reused in all alert playbooks to write timestamps in the case wall after each change to the case. Write a job to calculate the case metrics.
C. Use the playbooks' case stages to capture metrics for each stage change. Create a dashboard based on these metrics.
D. Create a Looker dashboard that displays case handling times by analyst, case priority, and environment using SecOps SOAR data.

Answer: C

Explanation:
Google Security Operations (SecOps) SOAR is designed to natively measure and report on key SOC performance metrics, including MTTR. This calculation is automatically derived from playbook case stages.
As a case is ingested and processed by a SOAR playbook, it moves through distinct, customizable stages (e.g.,
"Triage," "Investigation," "Remediation," "Closed"). The SOAR platform automatically records a timestamp for each of these stage transitions. The time deltas between these stages (e.g., the time from when a case entered "Triage" to when it entered "Remediation") are the raw data used to calculate MTTR and other KPIs.
This data is then aggregated and visualized in the built-in SecOps SOAR reporting and dashboarding features.
This is the standard, out-of-the-box method for capturing these metrics. Option C describes a manual, redundant process of what case stages do automatically. Option D describes where the data might be viewed (Looker), but Option B describes the underlying mechanism for how the MTTR data is captured in the first place, which is the core of the question.
(Reference: Google Cloud documentation, "Google SecOps SOAR overview"; "Manage playbooks"; "Get insights from dashboards and reports")

NEW QUESTION # 108
You are the SOC manager at a large enterprise that uses Google Security Operations (SecOps).
You need to create a report that shows the Return on Investment (ROI) attributed to analyst activities in Google SecOps SOAR for the previous month. The report should include the time saved and efficiency gains from using SOAR's features. You need to generate this report using the most efficient and accurate approach while providing the required level of detail. What should you do?

A. Create a custom Google SecOps SOAR search query that filters for all cases handled by specific analysts in the last month. Export the results to a spreadsheet for analysis and ROI calculation.
B. Use the filters and visualizations in the Management - SOC Status report in SOAR Reports to extract case-specific performance data.
C. Use the ROI - Analysts Benchmark report in SOAR Reports. Configure the report to display data for the desired time period, and filter by individual analysts.
D. Develop a Google SecOps SOAR playbook that automatically aggregates analyst performance metrics, incorporates custom weighted factors for different case types, calculates ROI based on predefined formulas, and generates a PDF report on a monthly schedule.

Answer: C

Explanation:
The most efficient and accurate method is to use the ROI - Analysts Benchmark report in SOAR Reports. This built-in report automatically calculates time saved and efficiency gains from SOAR features, allows filtering by analyst and time period, and avoids the need for manual queries or custom playbook development while delivering the required ROI insights.

NEW QUESTION # 109
You are using Google Security Operations (SecOps) to identify and report a repetitive sequence of brute force SSH login attempts on a Compute Engine image that did not result in a successful login. You need to gain visibility into this activity while minimizing impact on your ingestion quota.
Which log type should you ingest into Google SecOps?

A. Cloud Audit Logs
B. Security Command Center Premium (SCCP) findings
C. VPC Flow Logs
D. Cloud IDS logs

Answer: C

Explanation:
VPC Flow Logs provide network-level visibility into traffic such as repetitive SSH connection attempts, regardless of login success. Ingesting VPC Flow Logs lets you identify brute force patterns while minimizing ingestion volume, since you don't need full authentication logs or Cloud Audit Logs for unsuccessful login attempts. This approach gives you the necessary insight into SSH brute force activity without high log ingestion costs.

NEW QUESTION # 110
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