Google GCP-SOE-B : Security Operations Engineer (Beta)

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Google GCP-SOE-B Exam Syllabus Topics:

Google Security Operations Engineer (Beta) Sample Questions:

1. Your team has onboarded a new log source from a third-party DNS filtering solution. After ingestion, you observe that key UDM fields such as network.dns.questions.name and metadata.product_event_type are missing from the parsed events in Google Security Operations (SecOps). You suspect that the default parser does not fully align with the source format. You need to ensure these fields are available for downstream detection rules that rely on DNS query telemetry and event categorization. What should you do?

A) Create a parser extension that maps the missing source fields to the correct UDM fields and attach it to the existing parser.
B) Enable asset enrichment for the log source to infer missing fields based on correlated host activity.
C) Modify the ingestion source definition to remap raw fields directly to UDM by using the UDM sample output.
D) Use a custom parser that outputs all fields as raw JSON for detection.

2. You have a close relationship with a vendor who reveals to you privately that they have discovered a vulnerability in their web application that can be exploited in an XSS attack. This application is running on servers in the cloud and on- premises. Before the CVE is released, you want to look for signs of the vulnerability being exploited in your environment. What should you do?

A) Activate a new Web Security Scanner scan in Security Command Center (SCC), and look for findings related to XSS.
B) Create a YARA-L 2.0 rule to detect a time-ordered series of events where an external inbound connection to a server was followed by a process on the server that spawned subprocesses previously not seen in the environment.
C) Ask the Gemini Agent in Google Security Operations (SecOps) to search for the latest vulnerabilities in the environment.
D) Create a YARA-L 2.0 rule to detect high-prevalence binaries on your web server architecture communicating with known command and control (C2) nodes. Review inbound traffic from those C2 domains that have only started appearing recently.

3. Your organization uses Google Security Operations (SecOps) for security analysis and investigation. Your organization has decided that all security cases related to Data Loss Prevention (DLP) events must be categorized with a defined root cause specific to one of five DLP event types when the case is closed in Google SecOps. How should you achieve this?

A) Customize the Case Name format to include the DLP event type.
B) Customize the Close Case dialog and add the five DLP event types as root cause options.
C) Create a Google SecOps SOAR playbook that automatically assigns case tags where each tag contains the unique definition of one of the five DLP event types.
D) Create case tags in Google SecOps SOAR where each tag contains a unique definition of each of the five DLP event types, and have analysts assign them to cases manually.

4. Your organization uses the curated detection rule set in Google Security Operations (SecOps) for high priority network indicators. You are finding a vast number of false positives coming from your on-premises proxy servers. You need to reduce the number of alerts. What should you do?

A) Configure a rule exclusion for the target.domain field.
B) Configure a rule exclusion for the principal.ip field.
C) Configure a rule exclusion for the network.asset.ip field.
D) Configure a rule exclusion for the target.ip field.

5. 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 Google SecOps SOAR global context value to store a list of flagged users with their corresponding time to live values. 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 your detection rule.
C) Ingest the user flags as custom enrichment data using a feed. Use a multi-event detection rule to find logins from users flagged in the entity graph.
D) Store the identifiers of the flagged users in the detection rule logic. Actively monitor for newly flagged users, and add them to the detection rule logic.

Solutions: