Security-Operations-Engineer Exam Dumps - Google Cloud Certified - Professional Security Operations Engineer (PSOE) Exam

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

To definitively determine "whether any actions were taken" by a specific identity, you must search the audit logs directly for that identity's activity. The scenario specifies two data repositories: a centralized Cloud Logging bucket (for recent/retention-period logs) and BigQuery (for historical logs).

According to Google Cloud Observability and Security Operations documentation, Cloud Audit Logs (specifically Admin Activity and Data Access logs) capture "Who did what, where, and when." The primary identifier for the actor in these logs is the protoPayload.authenticationInfo.principalEmail.

Option C is the only method that directly queries the activity logs for the specific actor.

Cloud Logging: You would use the Logging Query Language to filter: protoPayload.authenticationInfo.principalEmail="[IDENTITY_EMAIL]".

BigQuery: You would use SQL to query the exported tables: SELECT * FROM [DATASET.TABLE] WHERE protopayload_auditlog.authenticationInfo.principalEmail = "[IDENTITY_EMAIL]".

Options A and B focus on access potential (Recommender/Policy Analyzer) rather than historical actions. Option D (VPC Flow Logs) records network traffic 5-tuples and does not contain identity information (principal email), making it unsuitable for attributing API actions to a specific user.

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.

The most efficient and reliable method to proactively search for a specific indicator (like a domain) in Google Security Operations is to perform a Universal Data Model (UDM) search. All ingested telemetry, including DNS logs and proxy logs, is parsed and normalized into the UDM. This allows an analyst to run a single, high-performance query against a specific, indexed field.

To search for a domain, an analyst would query a field such as network.dns.question.name or network.http.hostname. Option B correctly identifies this as querying the "DNS section of the network noun." This approach is vastly superior to a raw log search (Option C), which is slow, inefficient, and does not leverage the normalized UDM data.

Option D (IOC Search/Matches) is a passive feature that shows automatic matches between your logs and Google's integrated threat intelligence. While it's a good place to check, a UDM search is the active, analyst-driven process for hunting for a new IoC that may have come from an external feed. Option A is a UI feature for grouping search results and is not the search method itself.

(Reference: Google Cloud documentation, "Google SecOps UDM Search overview"; "Universal Data Model noun list - Network")

The primary investigation tool for exploring relationships and historical activity in Google Security Operations is the UDM (Universal Data Model) search. The platform's curated views, such as the "User View," are built on top of this search capability.

To find all assets a user has interacted with, an analyst would perform a UDM search for the specific user (e.g., principal.user.userid = "suspicious_user") over the specified time range. The search results will include all UDM events associated with that user. Within these events, the analyst can examine all populated asset fields, such as principal.asset.hostname, principal.ip, target.resource.name, and target.user.userid (for interactions with service accounts).

This UDM search allows the analyst to pivot from the user entity to all related asset entities, directly answering the question of "what assets the user has interacted with." While the wording of Option A is slightly backward (it's more efficient to query for the user and find the hostnames), it is the only option that correctly identifies the UDM search as the tool used to find user-to-asset (hostname) relationships. Options B (Retrohunt), C (Raw Log Scan), and D (Ingestion Report) are incorrect tools for this investigative task.

(Reference: Google Cloud documentation, "Google SecOps UM Search overview"; "Investigate a user"; "Universal Data Model noun list")

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")