Security Operations Engineer Online Practice Questions

Explanation:
Comprehensive and Detailed 150 to 250 words of Explanation From Exact Extract Google Security Operations Engineer documents:
This is a standard Identity and Access Management (IAM) permission issue. When Google Security Operations (SecOps) exports data, it uses its own service account (often named service-<project_number>@gcp-sa-bigquerydatatransfer.iam.gserviceaccount.com or a similar SecOps-specific principal) to perform the write operation. The user account that schedules the report (Option
C) is only relevant for the scheduling action, not for the data transfer itself. For the export to succeed, the Google SecOps service account principal must have explicit permission to write data into the target BigQuery dataset.
The predefined IAM role roles/bigquery.dataEditor grants the necessary permissions to create, update, and delete tables and table data within a dataset. By granting this role to the Google SecOps service account on the specific dataset, you authorize the service to write the report results and populate the tables.
Option A (serviceAccountUser) is incorrect as it's used for service account impersonation, not for granting data access.
Option B (retention period) is a data lifecycle setting and has no impact on the ability to write new data. The most common cause for this exact scenario―a successful job run with no data appearing―is that the service account lacks the required bigquery.dataEditor permissions on the destination dataset.
(Reference: Google Cloud documentation, "Troubleshoot transfer configurations"; "Control access to resources with IAM"; "BigQuery predefined IAM roles")

Explanation:
Comprehensive and Detailed 150 to 250 words of Explanation From Exact Extract Google Security Operations Engineer documents:
The most efficient and native solution is to use the Google Cloud operations suite. Google Security Operations (SecOps) automatically exports its own ingestion health metrics to Cloud Monitoring. These metrics provide detailed information about the logs being ingested, including log counts, parser errors, and event counts, and can be filtered by dimensions such as hostname.
To solve this, an engineer would navigate to Cloud Monitoring and create a new alert policy. This policy would be configured to monitor the chronicle.googleapis.com/ingestion/log_entry_count metric, filtering it for the specific hostname of the critical Windows server.
Crucially, Cloud Monitoring alerting policies have a built-in condition type for "metric absence." The engineer would configure this condition to trigger if no data points are received for the specified metric (logs from that server) for a duration of 30 minutes. When this condition is met, the policy will automatically send a notification to the desired channels (e.g., email, PagerDuty). This is the standard, out-of-the-box method for monitoring log pipeline health and requires no custom rules (Option B) or custom heartbeat configurations (Option C).
(Reference: Google Cloud documentation, "Google SecOps ingestion metrics and monitoring"; "Cloud Monitoring - Alerting on metric absence")

Explanation:
Comprehensive and Detailed Explanation
The correct solution is Option D. This approach correctly uses the integrated Google Cloud-native tools for both monitoring and alerting.
Google Security Operations (SecOps) automatically streams all ingestion metrics to Google Cloud
Monitoring. This includes metrics for throughput (e.g., chronicle.googleapis.com/ingestion/event_count, chronicle.googleapis.com/ingestion/byte_count), parsing errors (e.g., chronicle.googleapis.com/ingestion/parse_error_count), and the health of collection agents (e.g., chronicle.googleapis.com/ingestion/last_seen_timestamp).
Receive a notification (15 minutes): The Data Ingestion and Health dashboard (Option A) is for visualization, and its "reports" are scheduled summaries, not real-time alerts. The only way to get a 15-minute notification is to use Cloud Monitoring. An alerting policy can be configured to trigger when a "metric absence" is detected for a specific collection agent's last_seen_timestamp, fulfilling the "silent source" requirement.
Visualize metrics: Cloud Monitoring also provides a powerful dashboarding service. A Cloud Monitoring dashboard can be built to graph all the necessary metrics―throughput, parsing errors, and agent status―in one place.
Option C is incorrect because it suggests using the Bindplane Observability Pipeline, which is a separate product.
Option B is incorrect as Risk Analytics is for threat detection (UEBA), not platform health.
Exact Extract from Google Security Operations Documents:
Use Cloud Monitoring for ingestion insights: Google SecOps uses Cloud Monitoring to send the ingestion notifications. Use this feature for ingestion notifications and ingestion volume viewing.
Set up a sample policy to detect silent Google SecOps collection agents:
In the Google Cloud console, select Monitoring.
Click Create Policy.
On the Select a metric page, select Chronicle Collector > Ingestion > Total ingested log count.
In the Transform data section, set the Time series group by to collector_id.
Click Next.
Select Metric absence and set the Trigger absence time (e.g., 15 minutes).
In the Notifications and name section, select a notification channel.
You can also create custom dashboards in Cloud Monitoring to visualize any of the exported metrics, such as Total ingested log size or Total record count (for parsing).
Reference: Google Cloud Documentation: Google Security Operations > Documentation > Ingestion > Use Cloud Monitoring for ingestion insights
Google Cloud Documentation: Google Security Operations > Documentation > Ingestion > Silent-host monitoring > Use Google Cloud Monitoring with ingestion labels for SHM

Explanation:
Comprehensive and Detailed Explanation
The correct solution is Option
A. The key requirement is to "improve" the previous manual "watchlist" process.
In Google Security Operations, "data tables" (mentioned in options C and D) are the modern equivalent of watchlists or reference lists.1 Using a data table would replicate the old, static process and would not be an improvement.
The superior method in Google SecOps is to ingest this data as Entity Context. This is a core feature where context data (like user information from AD or asset data from a CMDB) is ingested via a feed or the Context API. Google SecOps then uses this data to automatically enrich all incoming security events (UDM) in real-time.
When a log for john.doe is ingested, it is automatically enriched with the context data from AD, such as "John Doe," "Marketing Department," "Manager: Jane Smith," etc. This enriched information is then available for detection, hunting, and investigation. This is a significant improvement because it provides continuous, automatic enrichment at ingestion, rather than requiring a manual update of a static table or only enriching after an alert is generated (Option B).
Exact Extract from Google Security Operations Documents:
UDM enrichment and aliasing overview: Google Security Operations (SecOps) supports aliasing and enrichment for assets and users.2 Aliasing enables enrichment.3 For example, using aliasing, you can find the job title and employment status associated with a user ID.4
How aliasing works: User aliasing uses the USER_CONTEXT event type for aliasing.5 This contextual data is stored as entities in the Entity Graph.6 When new Unified Data Model (UDM) events are ingested, enrichment uses this aliasing data to add context to the UDM event.7 For example, a UDM event might include principal.user.userid = "jdoe". 8The enrichment process populates the principal.user noun with the entity data, such as user.user_display_name = "John Doe" and user.department = "Marketing".
This is the recommended method for ingesting organizational context from sources like Microsoft Windows Active Directory, as it makes the contextual data available for all subsequent detection,
search, and investigation activities.
Reference: Google Cloud Documentation: Google Security Operations > Documentation > Event processing > UDM enrichment and aliasing overview
Google Cloud Documentation: Google Security Operations > Documentation > Ingestion > Collect Microsoft Windows AD logs (This document explicitly mentions collecting USER_CONTEXT and ASSET_CONTEXT).9

Explanation:
Comprehensive and Detailed Explanation
The correct answer is Option C. The incident description makes it clear that endpoint containment (by EDR) was insufficient, as the attacker successfully pivoted to privileged service accounts and began post-compromise activities (credential dumping, scheduled tasks).
The goal is to automate containment and minimize dwell time.
Option A is an enrichment/investigation action, not a containment action.
Option B is the opposite of automation; adding a manual approval step increases dwell time and response time.
Option D is a detection engineering task (creating a YARA-L rule), not a SOAR playbook (response) action.
Option C is the only true automated containment action that directly addresses the new threat. The anomalous behavior of the privileged accounts would raise their Entity Risk Score within Google SecOps. A modern SOAR playbook can be configured to automatically trigger on this high-risk score and execute an identity-based containment action. Revoking tokens and suspending sessions for the compromised high-privilege accounts is the most effective way to immediately stop the attacker's lateral movement and malicious activity, thereby accelerating containment and minimizing dwell time.
Exact Extract from Google Security Operations Documents:
SOAR Playbooks and Automation: Google Security Operations (SecOps) SOAR enables the orchestration and automation of security responses. Playbooks are designed to execute a series of automated steps to respond to an alert.
Identity and Access Management Integrations: SOAR playbooks can integrate directly with Identity Providers (IdPs) like Google Workspace, Okta, and Microsoft Entra ID. A critical automated containment action for compromised accounts is to revoke active OAuth tokens, suspend user sessions, or disable the account entirely. This action immediately logs the attacker out of all active sessions and prevents them from re-authenticating.
Entity Risk: Detections and anomalous activities contribute to an entity's (e.g., a user or asset) risk score. Playbooks can be configured to use this risk score as a trigger. For example, if a high-privilege account's risk score crosses a critical threshold, the playbook can automatically execute identity containment actions.
Reference: Google Cloud Documentation: Google Security Operations > Documentation > SOAR > Playbooks > Playbook Actions
Google Cloud Documentation: Google Security Operations > Documentation > SOAR > Marketplace integrations > (e.g., Okta, Google Workspace)
Google Cloud Documentation: Google Security Operations > Documentation > Investigate > View entity risk scores