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Google Cloud Certified - Professional Security Operations Engineer (PSOE) Exam Sample Questions (Q17-Q22):

NEW QUESTION # 17
You are helping a new Google Security Operations (SecOps) customer configure access for their SOC team.
The customer's Google SecOps administrators currently have access to the Google SecOps instance. The customer is reporting that the SOC team members are not getting authorized to access the instance, but they are able to authenticate to the third-party identity provider (IdP). How should you fix the issue?
Choose 2 answers

A. Grant the roles/chronicle.viewer role to the SOC team's IdP group in IAM.
B. Link Google SecOps to a Google Cloud project with the Chronicle API.
C. Connect Google SecOps with the third-party IdP using Workforce Identity Federation.
D. Grant the appropriate data access scope to the SOC team's IdP group in IAM.
E. Grant the Basic permission to the appropriate IdP groups in the Google SecOps SOAR Advanced Settings.

Answer: A,E

Explanation:
Comprehensive and Detailed Explanation
This scenario describes a common configuration task where authorization is failing despite successful authentication. The problem stems from the fact that Google SecOps uses a dual-authorization model: one for the main platform (SIEM/Chronicle) and a separate one for the SOAR module. The SOC team needs both.
The prompt states admins already have access, which confirms that prerequisite steps like linking the project (Option A) and configuring Workforce Identity Federation (Option B) are already complete. The problem is specific to the new SOC team's group.
* Fixing Instance Access (Option D):
The error "not getting authorized to access the instance" refers to the primary Google Cloud-level authorization. Access to the Google SecOps application itself is controlled by Google Cloud IAM roles on the linked project.1 The SOC team's group, which is federated from the third-party IdP, is represented as a principalSet in IAM. This principalSet must be granted an IAM role to allow sign-in. The roles/chronicle.
viewer role is the minimum predefined role required to grant this application access.
* Fixing SOAR Access (Option E):
Simply granting the IAM role (Option D) is not enough for the SOC team to perform its job. That role only gets them into the main SIEM interface. The SOAR module (for case management and playbooks) has its own internal role-based access control system. An administrator must also navigate within the SecOps platform to the SOAR Advanced Settings > Users & Groups and grant the SOC team's federated group a SOAR-specific permission, like "Basic" or "Analyst." Both steps are required to fully "fix the issue" and provide the SOC team with functional access to the platform.
Exact Extract from Google Security Operations Documents:
Identity and Access Management: Access to a Google SecOps instance using a third-party IdP relies on Workforce Identity Federation, but authorization is configured in two distinct locations.
* Google Cloud IAM: Authorization to the main SecOps instance (including the SIEM interface) is controlled by Google Cloud IAM.2 The federated identities (groups) from the third-party IdP are mapped to a principalSet. This principalSet must be granted an IAM role on the Google Cloud project linked to the SecOps instance. The roles/chronicle.viewer role is the minimum predefined role required to grant sign-in access.
* Google SecOps SOAR: Authorization for the SOAR module (for case management and playbooks) is managed independently.3 An administrator must navigate to the SOAR Advanced Settings > Users & Groups and assign a SOAR-specific role (e.g., 'Basic' or 'Analyst') to the same federated IdP group.
References:
Google Cloud Documentation: Google Security Operations > Documentation > Onboard > Configure a third-party identity provider Google Cloud Documentation: Google Security Operations > Documentation > SOAR > SOAR Administration > Users and Groups

NEW QUESTION # 18
Your organization's Google Security Operations (SecOps) tenant is ingesting a vendor's firewall logs in its default JSON format using the Google-provided parser for that log. The vendor recently released a patch that introduces a new field and renames an existing field in the logs. The parser does not recognize these two fields and they remain available only in the raw logs, while the rest of the log is parsed normally. You need to resolve this logging issue as soon as possible while minimizing the overall change management impact. What should you do?

A. Use the Extract Additional Fields tool in Google SecOps to convert the raw log entries to additional fields.
B. Deploy a third-party data pipeline management tool to ingest the logs, and transform the updated fields into fields supported by the default parser.
C. Use the web interface-based custom parser feature in Google SecOps to copy the parser, and modify it to map both fields to UDM.
D. Write a code snippet, and deploy it in a parser extension to map both fields to UDM.

Answer: D

Explanation:
Comprehensive and Detailed 150 to 250 words of Explanation From Exact Extract Google Security Operations Engineer documents:
The correct, low-impact solution for augmenting a Google-managed parser is to use a parser extension. The problem states that the base parser is still working, but needs to be supplemented to map two new fields.
Copying the entire parser (Option A) is a high-impact, high-maintenance solution ("Customer Specific Parser"). This action makes the organization responsible for all future updates and breaks the link to Google's managed updates, which is not a minimal-impact solution.
The intended, modern solution is the parser extension. This feature allows an engineer to write a small, targeted snippet of Code-Based Normalization (CBN) code that executes after the Google-managed base parser. This extension code can access the raw_log and perform the specific logic needed to extract the two unmapped fields and assign them to their proper Universal Data Model (UDM) fields.
This approach is the fastest to deploy and minimizes change management impact because the core parser remains managed and updated by Google, while the extension simply adds the custom logic on top. Option B,
"Extract Additional Fields," is a UI-driven feature, but the underlying mechanism that saves and deploys this logic is the parser extension. Option D is the more precise description of the technical solution.
(Reference: Google Cloud documentation, "Manage parsers"; "Parser extensions"; "Code-Based Normalization (CBN) syntax")

NEW QUESTION # 19
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. Replace api.operation with api.service_name = "storage.googleapis.com" to narrow the detection scope.
B. Adjust the rule severity to low to deprioritize alerts from automation tools.
C. Convert the rule into a multi-event rule that looks for repeated API calls across multiple buckets.
D. Add principal.user.email != "backup-bot@fcobaa.com" to the rule condition to exclude the automation account.

Answer: D

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 # 20
You recently joined a company that uses Google Security Operations (SecOps) with Applied Threat Intelligence enabled. You have alert fatigue from a recent red team exercise, and you want to reduce the amount of time spent sifting through noise. You need to filter out IoCs that you suspect were generated due to the exercise. What should you do?

A. Navigate to the IOC Matches page. Review IoCs with an Indicator Confidence Score (IC-Score) label
>= 80%.
B. Filter IoCs with an ingestion time that matches the time period of the red team exercise.
C. Ask Gemini to provide a list of IoCs from the red team exercise.
D. Navigate to the IOC Matches page. Identify and mute the IoCs from the red team exercise.

Answer: D

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

NEW QUESTION # 21
Your company uses Google Security Operations (SecOps) Enterprise and is ingesting various logs. You need to proactively identify potentially compromised user accounts. Specifically, you need to detect when a user account downloads an unusually large volume of data compared to the user's established baseline activity.
You want to detect this anomalous data access behavior using minimal effort. What should you do?

A. Inspect Security Command Center (SCC) default findings for data exfiltration in Google SecOps.
B. Develop a custom YARA-L detection rule in Google SecOps that counts download bytes per user per hour and triggers an alert if a threshold is exceeded.
C. Enable curated detection rules for User and Endpoint Behavioral Analytics (UEBA), and use the Risk Analytics dashboard in Google SecOps to identify metrics associated with the anomalous activity.
D. Create a log-based metric in Cloud Monitoring, and configure an alert to trigger if the data downloaded per user exceeds a predefined limit. Identify users who exceed the predefined limit in Google SecOps.

Answer: C

Explanation:
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")*

NEW QUESTION # 22
......

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