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Security-Operations-Engineer練習問題集 & Security-Operations-Engineer日本語問題集
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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 | - Incident Response: This section of the exam measures the skills of Incident Response Managers and assesses expertise in containing, investigating, and resolving security incidents. It includes evidence collection, forensic analysis, collaboration across engineering teams, and isolation of affected systems. Candidates are evaluated on their ability to design and execute automated playbooks, prioritize response steps, integrate orchestration tools, and manage case lifecycles efficiently to streamline escalation and resolution processes.
| | 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.
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Security-Operations-Engineer練習問題集は、Google Cloud Certified - Professional Security Operations Engineer (PSOE) Examに合格するための信頼できるサポートとなりますいろいろな人はGoogleのSecurity-Operations-Engineerを長い時間で復習して試験の模式への不適応で失敗することを心配していますから、我々CertShikenはあなたに試験の前に試験の真実な模式を体験させます。GoogleのSecurity-Operations-Engineer試験のソフトは問題数が豊富であなたに大量の練習で能力を高めさせます。そのほかに、専門家たちの解答への詳しい分析があります。あなたにGoogleのSecurity-Operations-Engineer試験に自信を持たせます。
Google Cloud Certified - Professional Security Operations Engineer (PSOE) Exam 認定 Security-Operations-Engineer 試験問題 (Q115-Q120):質問 # 115
You have noticed that a Google Security Operations (SecOps) detection rule that detects excessive network connections is triggering too frequently and creating too many false positive alerts. You want to improve the rule to reduce the noise without reducing the effectiveness of the rule. What change to the detection rule should you implement?
- A. Update the YARA-L events: section to exclude the most common IP addresses involved in the network connection alerts to reduce the number of alerts.
- B. Add a threshold in the YARA-L condition: section to ensure that the rule only alerts after a certain number of connections.
- C. Include a 10 minute timeframe for the same source and destination of network connections in the YARA-L match: section to aggregate the alerts.
- D. Assign a risk score in the YARA-L outcome: section to prioritize alerts more effectively in the alert queue.
正解:B
解説:
To reduce false positives for a rule detecting excessive network connections, you should add a threshold in the YARA-L condition: section. This ensures that the rule triggers only after a specified number of connections, filtering out normal or benign activity while maintaining the effectiveness of detecting truly excessive network behavior.
質問 # 116
You are responsible for evaluating the level of effort required to integrate a new third-party endpoint detection tool with Google Security Operations (SecOps). Your organization's leadership wants to minimize customization for the new tool for faster deployment. You need to verify that the Google SecOps SOAR and SIEM support the expected workflows for the new third-party tool. You must recommend a tool to your leadership team as quickly as possible. What should you do?
Choose 2 answers
- A. Develop a custom integration that uses Python scripts and Cloud Run functions to forward logs and orchestrate actions between the third-party tool and Google SecOps.
- B. Identify the tool in the Google SecOps Marketplace, and verify support for the necessary actions in the workflow.
- C. Configure a Pub/Sub topic to ingest raw logs from the third-party tool, and build custom YARA-L rules in Google SecOps to extract relevant security events.
- D. Review the architecture of the tool to identify the cloud provider that hosts the tool.
- E. Review the documentation to identify if default parsers exist for the tool, and determine whether the logs are supported and able to be ingested.
正解:B、E
解説:
Comprehensive and Detailed Explanation
The core task is to evaluate a new tool for fast, low-customization deployment across the entire Google SecOps platform (SIEM and SOAR). This requires checking the two main integration points: data ingestion (SIEM) and automated response (SOAR).
* SIEM Ingestion (Option B): To minimize customization for the SIEM, you must verify that Google SecOps can ingest and understand the tool's logs out-of-the-box. This is achieved by checking the Google SecOps documentation for a default parser for that specific tool. If a default parser exists, the logs will be automatically normalized into the Unified Data Model (UDM) upon ingestion, requiring zero custom development.
* SOAR Orchestration (Option C): To minimize customization for SOAR, you must verify that pre- built automated actions exist. The Google SecOps Marketplace contains all pre-built SOAR integrations (connectors). By finding the tool in the Marketplace, you can verify which actions (e.g.,
"Quarantine Host," "Get Process List") are supported, confirming that response playbooks can be built quickly without custom scripting.
Options D and E describe high-effort, custom integration paths, which are the exact opposite of the "minimize customization for faster deployment" requirement.
Exact Extract from Google Security Operations Documents:
Default parsers: Google Security Operations (SecOps) provides a set of default parsers that support many common security products. When logs are ingested from a supported product, SecOps automatically applies the correct parser to normalize the raw log data into the structured Unified Data Model (UDM) format. This is the fastest method to begin ingesting and analyzing new data sources.
Google SecOps Marketplace: The SOAR component of Google SecOps includes a Marketplace that contains a large library of pre-built integrations for common third-party security tools, including EDR, firewalls, and identity providers. Before purchasing a new tool, an engineer should verify its presence in the Marketplace and review the list of supported actions to ensure it meets the organization's automation and orchestration workflow requirements.
References:
Google Cloud Documentation: Google Security Operations > Documentation > Ingestion > Default parsers > Supported default parsers Google Cloud Documentation: Google Security Operations > Documentation > SOAR > Marketplace integrations
質問 # 117
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. Write a multi-event YARA-L detection rule that correlates the process relationship and hash, and run a retrohunt based on this rule.
- D. Use Google SecOps search to identify recent uses of rundll32.exe, and tag affected assets for watchlisting.
正解:B
解説:
Comprehensive and Detailed 150 to 250 words of Explanation From Exact Extract Google Security Operations Engineer documents:
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 # 118
You are a member of the incident response team working in a global enterprise. You need to identify all potential Google Threat Intelligence IOCs within your organization's data using Google Security Operations (SecOps). What should you do?
- A. Use the Alerts & IOCs page in Google SecOps.
- B. Use the Cases page in Google SecOps.
- C. Create YARA-L rules to detect and alert when Google Threat Intelligence identifies potential threats.
- D. Use Gemini to perform a search for potential cybersecurity threats against your organization's data.
正解:A
解説:
The correct approach is to use the Alerts & IOCs page in Google SecOps, which provides visibility into all potential IOCs detected by Google Threat Intelligence within your organization's data. This page consolidates IOC matches, enrichment, and drilldowns, enabling efficient investigation of potential threats.
質問 # 119
You have identified a common malware variant on a potentially infected computer. You need to find reliable IoCs and malware behaviors as quickly as possible to confirm whether the computer is infected and search for signs of infection on other computers. What should you do?
- A. Search for the malware hash in Google Threat Intelligence, and review the results.
- B. Create a Compute Engine VM, and perform dynamic and static malware analysis.
- C. Run a Google Web Search for the malware hash, and review the results.
- D. Perform a UDM search for the file checksum in Google Security Operations (SecOps). Review activities that are associated with, or attributed to, the malware.
正解:A
解説:
The correct answer is A. The most effective and reliable method for a security engineer to "find reliable IoCs and malware behaviors" is to use Google Threat Intelligence (GTI). When a known indicator like a file hash is identified, the primary workflow is threat enrichment. Google Threat Intelligence, which is a core component of the Google SecOps platform and incorporates intelligence from Mandiant and VirusTotal, is the dedicated tool for this. Searching the hash in GTI provides a comprehensive report on the malware variant, including all associated reliable IoCs (e.g., C2 domains, IP addresses, related file hashes) and malware behaviors (TTPs, attribution, and context). This directly fulfills the user's need.
In contrast, Option D (UDM search) is the subsequent step. A UDM search is used to hunt for indicators within your own organization's logs. An engineer would first use GTI to gather the full list of IoCs and behaviors, and then use UDM search to hunt for all of those indicators across their environment. Option B (Web Search) is unreliable for professional operations, and Option C (manual analysis) is too slow for a
"common malware variant" and the need to act "quickly."
(Reference: Google Cloud documentation, "Google Threat Intelligence overview"; "Investigating threats using Google Threat Intelligence"; "View IOCs using Applied Threat Intelligence")
質問 # 120
......
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