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Security-Operations-Engineerサンプル問題集、Security-Operations-Engineer試験問題

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>> Security-Operations-Engineerサンプル問題集 <<

Google Security-Operations-Engineer試験の準備方法｜権威のあるSecurity-Operations-Engineerサンプル問題集試験｜更新するGoogle Cloud Certified - Professional Security Operations Engineer (PSOE) Exam試験問題

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Google Security-Operations-Engineer 認定試験の出題範囲：

トピック
出題範囲

トピック 1

Data Management: This section of the exam measures the skills of Security Analysts and focuses on effective data ingestion, log management, and context enrichment for threat detection and response. It evaluates candidates on setting up ingestion pipelines, configuring parsers, managing data normalization, and handling costs associated with large-scale logging. Additionally, candidates demonstrate their ability to establish baselines for user, asset, and entity behavior by correlating event data and integrating relevant threat intelligence for more accurate monitoring.

トピック 2

Threat Hunting: This section of the exam measures the skills of Cyber Threat Hunters and emphasizes proactive identification of threats across cloud and hybrid environments. It tests the ability to create and execute advanced queries, analyze user and network behaviors, and develop hypotheses based on incident data and threat intelligence. Candidates are expected to leverage Google Cloud tools like BigQuery, Logs Explorer, and Google SecOps to discover indicators of compromise (IOCs) and collaborate with incident response teams to uncover hidden or ongoing attacks.

トピック 3

Detection Engineering: This section of the exam measures the skills of Detection Engineers and focuses on developing and fine-tuning detection mechanisms for risk identification. It involves designing and implementing detection rules, assigning risk values, and leveraging tools like Google SecOps Risk Analytics and SCC for posture management. Candidates learn to utilize threat intelligence for alert scoring, reduce false positives, and improve rule accuracy by integrating contextual and entity-based data, ensuring strong coverage against potential threats.

トピック 4

Monitoring and Reporting: This section of the exam measures the skills of Security Operations Center (SOC) Analysts and covers building dashboards, generating reports, and maintaining health monitoring systems. It focuses on identifying key performance indicators (KPIs), visualizing telemetry data, and configuring alerts using tools like Google SecOps, Cloud Monitoring, and Looker Studio. Candidates are assessed on their ability to centralize metrics, detect anomalies, and maintain continuous visibility of system health and operational performance.

Google Cloud Certified - Professional Security Operations Engineer (PSOE) Exam 認定 Security-Operations-Engineer 試験問題 (Q77-Q82):

質問 # 77
You are a security engineer at a financial technology company. You need to create a centralized dashboard to provide security posture visibility for your leadership team. The dashboard must meet these requirements:
- Provide insights from Security Command Center (SCC) findings and security-related events captured in Cloud Logging.
- Support large volumes of historical data.
- Be able to join SCC findings and audit logs.
You want to use the most effective visualization solution that uses Google Cloud managed services. What should you do?

A. Use the built-in SCC dashboard to visualize the SCC finding, and extract log counts for specific log events from Cloud Audit Logs.
B. Ingest the SCC findings and Cloud Audit Logs into a Cloud Storage bucket. Write a Python script that reads the data and uses Matplotlib to create the visualizations.
C. Export SCC findings and Cloud Audit Logs to BigQuery. Connect Looker Studio to the BigQuery datasets, and create the visualizations and filters.
D. Create custom metrics in Cloud Monitoring based on the SCC findings, and configure log-based metrics for security-related events. Build Cloud Monitoring dashboards to visualize these custom and log-based metrics.

正解：C

解説：
The most effective approach is to export SCC findings and Cloud Audit Logs into BigQuery, which supports large-scale storage and querying of historical data. You can then connect Looker Studio to BigQuery to create a centralized dashboard that visualizes and joins SCC findings with audit logs. This leverages fully managed Google Cloud services and provides scalability, flexibility, and real-time reporting for leadership visibility.

質問 # 78
You are a security engineer at a managed security service provider (MSSP) that is onboarding to Google Security Operations (SecOps). You need to ensure that cases for each customer are logically separated. How should you configure this logical separation?

A. In Google SecOps Playbooks, create a playbook for each customer.
B. In Google SecOps SOAR settings, create a role for each customer.
C. In Google SecOps SOAR settings, create a permissions group for each customer.
D. In Google SecOps SOAR settings, create a new environment for each customer.

正解：D

解説：
The correct mechanism for achieving logical data segregation for different customers in a Google Security Operations (SecOps) SOAR multi-tenant environment is by using Environments. The documentation explicitly states that "you can define different environments and environment groups to create logical data segregation." This separation applies to most platform modules, including cases, playbooks, and dashboards.
This feature is specifically designed for this use case: "This process is useful for businesses and Managed Security Service Providers (MSSPs) who need to segment their operations and networks. Each environment...
can represent a separate customer." When an analyst is associated with a specific environment, they can only see the cases and data relevant to that customer, ensuring strict logical separation.
While permission groups (Option C) and roles (Option A) are used to control what a user can do within the platform (e.g., view cases, edit playbooks), they do not provide the primary data segregation. Environments are the top-level containers that separate one customer's data and cases from another's. Playbooks (Option B) are automation workflows and are not a mechanism for logical separation.
(Reference: Google Cloud documentation, "Control access to the platform using SOAR permissions"; " Support multiple instances [SOAR]")

質問 # 79
You are receiving security alerts from multiple connectors in your Google Security Operations (SecOps) instance. You need to identify which IP address entities are internal to your network and label each entity with its specific network name. This network name will be used as the trigger for the playbook.

A. Enrich the IP address entities as the initial step of the playbook.
B. Configure each network in the Google SecOps SOAR settings.
C. Modify the entity attribute in the alert overview.
D. Create an outcome variable in the rule to assign the network name.

正解：B

解説：
Comprehensive and Detailed 150 to 250 words of Explanation From Exact Extract Google Security Operations Engineer documents:
The requirement is to identify internal entities and label them with a network name across alerts from
"multiple connectors." This is a global environment configuration task, not a per-playbook task.
In Google SecOps SOAR, you achieve this by configuring the Networks (or Environments) settings. The documentation states: "You can define your internal network ranges... When an entity is ingested, the system checks if the entity value falls within any of the defined ranges. If it does, the entity is marked as internal." Furthermore, you can assign a Network Name to these ranges. When an entity matches the range, it is automatically enriched with that network context. This allows you to set up Playbook Triggers based on the
"Network Name" field, satisfying the requirement. Option D (Enrichment step) is inefficient because it would require adding the step to every single playbook, whereas Option A solves it globally for the platform.
References: Google Security Operations Documentation > SOAR > Settings > Environments and Networks

質問 # 80
You are implementing Google Security Operations (SecOps) with multiple log sources. You want to closely monitor the health of the ingestion pipeline's forwarders and collection agents, and detect silent sources within five minutes. What should you do?

A. Create a notification in Cloud Monitoring using a metric-absence condition based on sample policy for each collector_id.
B. Create an ingestion notification for health metrics in Cloud Monitoring based on the total ingested log count for each collector_id.
C. Create a Looker dashboard that queries the BigQuery ingestion metrics schema for each log_type and collector_id.
D. Create a Google SecOps SIEM dashboard to show the ingestion metrics for each log_type and collector_id.

正解：A

解説：
The best solution is to create a Cloud Monitoring notification with a metric-absence condition for each collector_id. A metric-absence alert triggers when expected ingestion metrics are missing within a defined period (e.g., five minutes), which quickly identifies silent sources or failed collectors. This provides near real-time detection of ingestion health issues in the SecOps pipeline.

質問 # 81
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. 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.
B. Build a data table that contains the hash and domain, and link the list to a high-frequency rule for near real-time alerting.
C. Use Google SecOps search to identify recent uses of rundll32.exe, and tag affected assets for watchlisting.
D. Write a multi-event YARA-L detection rule that correlates the process relationship and hash, and run a retrohunt based on this rule.

正解：B

解説：
The core of this problem is the unreliable data quality for the file hash. A robust detection strategy cannot depend on an unreliable data point. Options B and C are weak because they create a dependency on the SHA256 hash, which the prompt states is "not reliably captured." This would lead to missed detections.
Option A is far too broad and would generate massive noise.
The best detection engineering practice is to use the reliable IoCs in a flexible and high-performance manner.
The domain is a reliable IoC (from DNS logs), and the hash is still a valuable IoC, even if it's only intermittently available.
The standard Google SecOps method for this is to create a List (referred to here as a "data table") containing both static IoCs: the hash and the domain. An engineer can then write a single, efficient YARA-L rule that references this list. This rule would trigger if either a PROCESS_LAUNCH event is seen with a hash in the list or a NETWORK_DNS event is seen with a domain in the list (e.g., (event.principal.process.file.sha256 in
%ioc_list) or (event.network.dns.question.name in %ioc_list)). This creates a resilient detection mechanism that provides two opportunities to identify the threat, successfully working around the unreliable data problem.
(Reference: Google Cloud documentation, "YARA-L 2.0 language syntax"; "Using Lists in rules"; "Detection engineering overview")

質問 # 82
......

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