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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 検知エンジニアリング:この試験セクションでは、検知エンジニアのスキルを評価し、リスク特定のための検知メカニズムの開発と微調整に焦点を当てます。検知ルールの設計と実装、リスク値の割り当て、そしてGoogle SecOps Risk AnalyticsやSCCなどのツールを活用したポスチャ管理が含まれます。受験者は、脅威インテリジェンスを活用してアラートスコアリングを行い、誤検知を削減し、コンテキストデータとエンティティベースのデータを統合することでルールの精度を向上させ、潜在的な脅威に対する強力なカバレッジを確保する方法を習得します。

トピック 2 インシデント対応:このセクションでは、インシデント対応マネージャーのスキルを測定し、セキュリティインシデントの封じ込め、調査、解決に関する専門知識を評価します。試験内容には、証拠収集、フォレンジック分析、エンジニアリングチーム間の連携、影響を受けたシステムの隔離が含まれます。受験者は、自動化されたプレイブックの設計と実行、対応手順の優先順位付け、オーケストレーションツールの統合、そしてケースライフサイクルの効率的な管理によってエスカレーションと解決プロセスを効率化する能力について評価されます。

トピック 3 データ管理:このセクションでは、セキュリティアナリストのスキルを評価し、脅威の検知と対応のための効果的なデータ取り込み、ログ管理、コンテキストエンリッチメントに焦点を当てます。取り込みパイプラインの設定、パーサーの設定、データ正規化の管理、大規模ログ記録に伴うコストの処理能力を評価します。さらに、イベントデータを相関分析し、関連する脅威インテリジェンスを統合することで、ユーザー、資産、エンティティの行動に関するベースラインを確立し、より正確な監視を行う能力も評価します。

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Google Cloud Certified – Professional Security Operations Engineer (PSOE) Exam 認定 Security-Operations-Engineer 試験問題 (Q25-Q30): 質問 # 25 You are developing a new detection rule in Google Security Operations (SecOps). You are defining the YARA-L logic that includes complex event, match, and condition sections. You need to develop and test the rule to ensure that the detections are accurate before the rule is migrated to production. You want to minimize impact to production processes. What should you do?

A. Develop the rule logic in the UDM search, review the search output to inform changes to filters and logic, and copy the rule into the Rules Editor. B. Develop the rule in the Rules Editor, define the sections of the rule logic, and test the rule by setting it to live but not alerting. Run a YARA-L retrohunt from the rules dashboard. C. Use Gemini in Google SecOps to develop the rule by providing a description of the parameters and conditions, and transfer the rule into the Rules Editor. D. Develop the rule in the Rules Editor, define the sections of the rule logic, and test the rule using the test rule feature. 正解:D

解説: Comprehensive and Detailed 150 to 250 words of Explanation From Exact Extract Google Security Operations Engineer documents: The Google Security Operations (SecOps) platform provides an integrated, zero-impact workflow for developing and testing detections. The standard method is to use the “Test Rule” feature, which is built directly into the Rules Editor. After the detection engineer has defined the complete YARA-L logic (including events, match, and condition sections), they can click the “Test Rule” button. This function performs a historical search (a retrohunt) against a specified time range of UDM data (e.g., last 24 hours, last 7 days). The platform then returns a list of all events that would have triggered the detection, without creating any live alerts, cases, or impacting production. This allows the engineer to “ensure that the detections are accurate” by reviewing the historical matches, identifying potential false positives, and refining the rule's logic. This iterative “develop and test” cycle within the editor is the primary method for validating a rule before it is enabled. While UDM search (Option A) is useful for testing the events section logic, it cannot test the full match and condition logic of the rule. Setting a rule to “live but not alerting” (Option D) is a valid, later step, but the “Test Rule” feature is the correct initial development and testing tool. (Reference: Google Cloud documentation, “Create and manage rules using the Rules Editor”; “Test a rule”)

質問 # 26 You are a SOC analyst at an organization that uses Google Security Operations (SecOps). You are investigating suspicious activity in your organization's environment. Alerts in Google SecOps indicate repeated PowerShell activity on a set of endpoints. Outbound connections are made to a domain that does not appear in your threat intelligence feeds. The activity occurs across multiple systems and user accounts. You need to search across impacted systems and user identities to identify the malicious user and understand the scope of the compromise. What should you do?

A. Perform a YARA-L 2.0 search to correlate activity across impacted systems and users. B. Use the Behavioral Analytics dashboard in Risk Analytics to identify abnormal IP-based activity and high-risk user behavior. C. Use the User Sign-In Overview dashboard to monitor authentication trends and anomalies across all users. D. Perform a raw log search for the suspicious domain string, and manually pivot to related user activity. 正解:A

解説: The most effective approach is to perform a YARA-L 2.0 search that correlates activity across impacted systems and user identities. YARA-L rules can link PowerShell execution events, outbound connections, and user activity, enabling you to identify the malicious user and the scope of the compromise efficiently, rather than relying on manual log searches or only analyzing authentication trends.

質問 # 27 You are responsible for identifying suspicious activity and security events in your organization's environment. You discover that some detection rules are generating false positives when the principal.ip field contains one or more IP addresses in the 192.168.2.0/24 subnet. You want to improve these detection rules using the principal.ip repeated field. What should you add to the YARA-L detection rules?

A. net.ipinrangecidr(all $e.principal.ip, “192.168.2.0/24”) B. not net.ipinrangecidr(all $e.principal.ip, “192.168.2.0/24”) C. not net.ipinrangecidr(any $e.principal.ip, “192.168.2.0/24”) D. net.ipinrangecidr(any $e.principal.ip, “192.168.2.0/24”) 正解:C

解説: Comprehensive and Detailed Explanation The correct solution is Option D. The goal is to exclude events (i.e., stop false positives) when the principal. ip field contains any IP from the trusted 192.168.2.0/24 subnet. The principal.ip field in UDM is a repeated field, meaning it can hold an array of values (e.g., [“1.2.3.4”, “192.168.2.5”]). YARA-L provides the any and all quantifiers to handle repeated fields.9 * any $e.principal.ip: This checks if at least one IP in the array meets the condition. * all $e.principal.ip: This checks if every IP in the array meets the condition. The function net.ipinrangecidr(...) returns true if an IP is in the specified range. Therefore, the logic we need is: “do not trigger this rule if any of the IPs in the principal.ip field are in the 192.168.2.0/24 range.” This translates directly to the YARA-L syntax: not net.ipinrangecidr(any $e.principal.ip, “192.168.2.0/24”) * Option B would only find events from that subnet. * Option A would only find events where all associated IPs are in that subnet. * Option C is the logical inverse of A and would incorrectly filter out events that might be malicious (e. g., [“1.2.3.4”, “192.168.2.5”] would not be excluded because all IPs are not in the range). Exact Extract from Google Security Operations Documents: YARA-L 2.0 language syntax > Repeated fields and boolean expressions: When a boolean expression, such as a function call, is applied to a repeated field, you can use the any or all keywords to specify how the expression should be evaluated.10 * any : The expression evaluates to true if it is true for at least one of the values in the repeated field. * all : The expression evaluates to true only if it is true for all of the values in the repeated field. Functions > net.ipinrangecidr: The net.ipinrangecidr function is useful to bind rules to specific parts of the network.11 To exclude all private netblocks as defined in RFC1918, you can add a not to the start of the criteria: and not (net.ipinrangecidr(any $e.principal.ip, “10.0.0.0/8”) or net.ipinrangecidr(any $e.principal.ip, “172.16.0.0/12”) or net.ipinrangecidr(any $e.principal.ip, “192.168.0.0/16”)) References: Google Cloud Documentation: Google Security Operations > Documentation > Detections > YARA-L 2.0 language syntax Google Cloud Documentation: Google Security Operations > Documentation > Detections > YARA-L 2.0 functions > net.ipinrangecidr

質問 # 28 A security analyst wants to detect lateral movement between Compute Engine instances using valid credentials. Which data source is MOST useful?

A. Compute Engine serial console output B. VPC Flow Logs C. Identity-aware Proxy logs D. Cloud Load Balancer logs 正解:B

解説: VPC Flow Logs reveal internal east-west traffic patterns that can expose lateral movement behavior.

質問 # 29 Your Google Security Operations (SecOps) instance is generating a high volume of alerts related to an IP address that recently appeared in a threat intelligence feed. The IP address is flagged as a known command and control (C2) server by multiple vendors. The IP address appears in repeated DNS queries originating from a sandboxing system and test environment used by your malware analysis team. You want to avoid alert fatigue while preserving visibility in the event that the IOC reappears in real production telemetry. What should you do?

A. Temporarily disable the rule to avoid unnecessary alerts until the IOC expires in the threat feed. B. Reduce the severity score in the rule configuration when the IOC match occurs in any internal IP address range. C. Add an exception in the detection rule to exclude matches originating from specific asset groups. D. Add the IP address to a Google SecOps reference list, and configure the rule to suppress alerts for that list. 正解:C

解説: The correct approach is to add an exception in the detection rule that excludes matches from the sandboxing and test environment asset groups. This prevents alert fatigue by suppressing non- production noise, while still maintaining full visibility and alerting if the same IOC reappears in real production telemetry.

質問 # 30 ......

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