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Google Security Operations Engineer (Beta) Sample Questions:

1. You are responsible for selecting and prioritizing potential sources of data to integrate with Google Security Operations (SecOps). Your company has recently started using several Google Cloud services to increase security in its Google Cloud organization. You need to determine which logs should be ingested into Google SecOps to reduce the effort required to write detections. What should you do?

A) Integrate Security Command Center (SCC) into Google SecOps to ingest logs originating from the Google Cloud services.
B) Ingest Google Cloud Armor logs by using Cloud Logging.
C) Deploy a Bindplane agent to ingest event logs from Compute Engine VMs that provide endpoint visibility.
D) Use Google Threat Intelligence to gain insight about threat group behavior and support threat hunting activities.

2. You are a security operations engineer in an enterprise that uses Google Security Operations (SecOps). Your organization recently faced a cybersecurity breach. You need to increase the threat analytics as quickly as possible. What should you do?

A) Ingest data from a threat intelligence platform (TIP) into Google SecOps.
B) Develop YARA-L detection rules that focus on threat intelligence.
C) Enable curated detections to identify threats.
D) Design YARA-L detection rules based on Google SecOps Marketplace use cases.

3. You are investigating whether an advanced persistent threat (APT) actor has operated in your organization's environment undetected. You have received threat intelligence that includes:
- A SHA256 hash for a malicious DLL
- A known command and control (C2) domain
- A behavior pattern where rundll32.exe spawns powershell.exe with obfuscated arguments Your Google Security Operations (SecOps) instance includes logs from EDR, DNS, and Windows Sysmon. However, you have recently discovered that process hashes are not reliably captured across all endpoints due to an inconsistent Sysmon configuration. You need to use Google SecOps to develop a detection mechanism that identifies the associated activities. What should you do?

A) Build a reference list that contains the hash and domain, and link the list to a high-frequency rule for near real-time alerting.
B) Write a multi-event YARA-L detection rule that correlates the process relationship and hash, and run a retrohunt based on this rule.
C) Use Google SecOps search to identify recent uses of rundll32.exe, and tag affected assets for watchlisting.
D) Create a single-event YARA-L detection rule based on the file hash, and run the rule against historical and incoming telemetry to detect the DLL execution.

4. You are working with your company's analyst team to automate the investigation of phishing alerts ingested directly into Google Security Operations (SecOps) SOAR from an email inbox.
The analyst team currently uses a SIEM query to search for related information. You need to design a solution to automatically include the query results in the Google SecOps case without writing any new code. What should you do?

A) Add a widget to the Default Case View in Google SecOps SOAR that allows the analyst team to query directly from the widget.
B) Modify the detection rule in the SIEM to include the query results as part of the detection.
C) Create a custom action in Google SecOps IDE that runs the SIEM query from a playbook through an API call and returns the results.
D) Add an action to the playbook that runs the SIEM query and returns the results.

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) 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.
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) 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.
D) 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.

Solutions: