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Google Security-Operations-Engineer Exam Overview:

Certification Vendor:	Google Cloud
Exam Name:	Professional Security Operations Engineer Exam
Exam Number:	Professional Security Operations Engineer (PSO Engineer)
Exam Price:	$200 USD (may vary by region)
Real Exam Qty:	Approximately 50–60 questions
Available Languages:	English, Japanese
Related Certifications:	Google Cloud Certified - Associate Cloud Engineer Google Cloud Certified - Professional Cloud Security Engineer
Exam Duration:	120 minutes
Certificate Validity Period:	2 years
Exam Format:	Multiple select, Multiple choice
Recommended Training:	Google Cloud Security Engineer Learning Path Google Cloud Security Operations Training
Exam Registration:	Kryterion Webassessor Exam Scheduling Google Cloud Certification Registration
Sample Questions:	Google Security-Operations-Engineer Sample Questions
Exam Way:	Online proctored or test center (Kryterion/Webassessor)
Pre Condition:	Recommended: 3+ years industry experience in security operations or SOC roles, and 1+ year experience with Google Cloud security technologies
Official Syllabus URL:	https://cloud.google.com/certification/security-operations-engineer

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Google Security-Operations-Engineer Exam Syllabus Topics:

Topic	Details
Topic 1	Data Management: This section of the exam measures the skills of Security Analysts and focuses on effective data ingestion, log management, and context enrichment for threat detection and response. It evaluates candidates on setting up ingestion pipelines, configuring parsers, managing data normalization, and handling costs associated with large-scale logging. Additionally, candidates demonstrate their ability to establish baselines for user, asset, and entity behavior by correlating event data and integrating relevant threat intelligence for more accurate monitoring.
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.

Google Cloud Certified - Professional Security Operations Engineer (PSOE) Exam Sample Questions (Q91-Q96):

NEW QUESTION # 91
Your company recently started pulling JSON logs from a third-party system into Google Security Operations (SecOps). You noticed that some fields are missing, and you want to parse them into UDM fields as quickly as possible. What should you do?

A. Submit a parser improvement request to Cloud Customer Care.
B. Create parser extensions using the code snippet approach.
C. Configure auto extraction to add the additional fields.
D. Create parser extensions using the no-code approach.

Answer: D

Explanation:
The fastest way to handle missing fields in JSON logs is to create parser extensions using the no- code approach in Google SecOps. This allows you to quickly map additional fields into UDM without writing code or waiting on support requests, ensuring rapid parsing and normalization of the third-party logs.

NEW QUESTION # 92
You are developing a playbook to respond to phishing reports from users at your company. You configured a UDM query action to identify all users who have connected to a malicious domain. You need to extract the users from the UDM query and add them as entities in an alert so the playbook can reset the password for those users. You want to minimize the effort required by the SOC analyst. What should you do?

A. Implement an Instruction action from the Flow integration that instructs the analyst to add the entities in the Google SecOps user interface.
B. Create a case for each identified user with the user designated as the entity.
C. Configure a manual Create Entity action from the Siemplify integration that instructs the analyst to input the Entities Identifier parameter based on the results of the action.
D. Use the Create Entity action from the Siemplify integration. Use the Expression Builder to create a placeholder with the usernames in the Entities Identifier parameter.

Answer: D

Explanation:
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")*
***

NEW QUESTION # 93
Your organization uses Cloud Identity as their identity provider (IdP) and is a Google Security Operations (SecOps) customer You need to grant a group of users access to the Google SecOps instance with read-only access to all resources, including detection engine rules. How should this be configured?

A. Create a workforce identity pool at the organization level. Grant the roles/chronicle.editor IAM role to the principalSet://iam.googleapis.com/locations/global/workforcePools/POOL_ID/group/GROUP_ID principal set on the project associated with your Google SecOps instance.
B. Create a Google Group and add the required users. Grant the roles/chronicle.Viewer IAM role to the group on the project associated with your Google SecOps Instance.
C. Create a Google Group and add the required users. Grant the roles/chronicle.limitedViewer IAM role to the group on the project associated with your Google SecOps instance.
D. Create a workforce identity pool at the organization level Grant the roles/chronicle.limitedViewer IAM role to the principalSet://iam.googleapis.com/locations/global/workforcePools/POOL_ID/group/GROUP_ID principal set on the project associated with your Google SecOps Instance.

Answer: B

Explanation:
To grant read-only access to all Google SecOps resources, including detection engine rules, you assign the roles/chronicle.Viewer IAM role. The correct method is to create a Google Group, add the required users, and grant this role to the group at the project level tied to your Google SecOps instance. This ensures consistent, least-privilege access management through Cloud Identity.

NEW QUESTION # 94
You are ingesting and parsing logs from an SSO provider and an on-premises appliance using Google Security Operations (SecOps). Users are tagged as "restricted" by an internal process. Restrictions last five days from the most recent flagging time. You need to create a rule to detect when restricted users log into the appliance. Your solution must be quickly implemented and easily maintained.
What should you do?

A. Use a SOAR job to dynamically build and deploy a new version of the detection rule with the updated list of flagged users.
B. Store the flagged users in a data table column with their corresponding time-to-live values in a second column. Use row-based comparisons in the detection rule.
C. Create a regex data table to store each user and the corresponding time-to-live value in a single row, pipe-delimited, and use an "in" keyword in your detection rule.
D. Use a Google SecOps SOAR global context value to store a list of flagged users with their corresponding time-to-live values.

Answer: B

Explanation:
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.
References: Google Security Operations Documentation > Investigation > Use data tables; Google Security Operations Documentation > Detection > YARA-L 2.0 Language Syntax

NEW QUESTION # 95
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 Sysmon.
C. Ingest logs from Windows PowerShell.
D. Ingest logs from Microsoft Entra ID.

Answer: B

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 # 96
......

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