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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	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 2	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。
トピック 3	脅威ハンティング：この試験セクションでは、サイバー脅威ハンターのスキルを評価し、クラウドおよびハイブリッド環境全体にわたる脅威のプロアクティブな特定に重点を置いています。高度なクエリの作成と実行、ユーザーおよびネットワークの行動分析、インシデントデータと脅威インテリジェンスに基づく仮説の構築能力が試されます。受験者は、BigQuery、Logs Explorer、Google SecOpsなどのGoogle Cloudツールを活用して侵害の兆候（IOC）を発見し、インシデント対応チームと連携して、隠れた攻撃や進行中の攻撃を発見することが求められます。
トピック 4	モニタリングとレポート：このセクションでは、セキュリティオペレーションセンター（SOC）アナリストのスキルを評価し、ダッシュボードの構築、レポートの生成、ヘルスモニタリングシステムの維持管理について学習します。特に、主要業績評価指標（KPI）の特定、テレメトリデータの可視化、Google SecOps、Cloud Monitoring、Looker Studio などのツールを使用したアラートの設定に重点を置いています。受験者は、指標の一元管理、異常検知、システムのヘルスと運用パフォーマンスの継続的な可視性維持能力について評価されます。

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Security-Operations-Engineer練習問題、Security-Operations-Engineer資格受験料

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Google Cloud Certified - Professional Security Operations Engineer (PSOE) Exam 認定 Security-Operations-Engineer 試験問題 (Q22-Q27):

質問 # 22
Your organization recently conducted a penetration test on their environment. You have been tasked with identifying a successful attack chain. The required log sources have been ingested into Google Security Operations (SecOps). You discover anomalous outbound traffic to external domains. You suspect that the finding is a communication to a command and control (C2) infrastructure. You need to identify the least common network communications over the last 14 days. What should you do?

A. Perform a Google SecOps SOAR search that looks for cases with low rolling prevalence of NETWORK_CONNECTION or NETWORK_HTTP events over the last 14 days.
B. Perform a Google SecOps SIEM raw log search that looks for low rolling prevalence domains with NETWORK_CONNECTION or NETWORK_HTTP in the firewall and proxy logs over the last 14 days.
C. Perform a Google SecOps SIEM UDM search that looks for NETWORK_CONNECTION or NETWORK_HTTP events with low rolling prevalence for principal domains over the last 14 days.
D. Perform a Google SecOps SIEM UDM search that looks for NETWORK_CONNECTION or NETWORK_HTTP events with low rolling prevalence for target domains over the last 14 days.

正解：D

解説：
To identify rare network communications that could indicate C2 activity, you should run a Google SecOps SIEM UDM search for NETWORK_CONNECTION or NETWORK_HTTP events and filter for low rolling prevalence on target domains over the past 14 days. This approach highlights unusual outbound communications to external domains that are least common in your environment, aligning with C2 detection best practices.

質問 # 23
You have discovered that a server that hosts an internal web application has been accidentally exposed to the internet for 48 hours. Logging is enabled on the server. You want to use Google Security Operations (SecOps) to run a UDM search against the server logs to identify whether there have been any successful exploitations against it. What event field search should you use?

A. Perform a search for network traffic where the principal is rarely seen by using the principal.ip UDM field.
B. Perform a search for sign-on activity for user accounts that are not expected on the server by using the principal.user.userid UDM field.
C. Perform a search for process launches and commands that are rarely seen by using the metadata.event_type UDM field.
D. Perform a search for antimalware or endpoint security events by using the product_event_type UDM field.

正解：C

解説：
To check for successful exploitations, you need to look for abnormal process launches and commands that indicate post-exploitation activity. In Google SecOps UDM, this is done by searching with the metadata.event_type field, which classifies events such as process execution.
Unusual or rarely seen processes provide strong indicators of compromise.

質問 # 24
You are conducting proactive threat hunting in your company's Google Cloud environment. You suspect that an attacker compromised a developer's credentials and is attempting to move laterally from a development Google Kubernetes Engine (GKE) cluster to critical production systems. You need to identify IOCs and prioritize investigative actions by using Google Cloud's security tools before analyzing raw logs in detail. What should you do next?

A. Review threat intelligence feeds within Google Security Operations (SecOps), and enrich any anomalies with context on known IOCs, attacker tactics, techniques, and procedures (TTPs), and campaigns.
B. Investigate Virtual Machine (VM) Threat Detection findings in Security Command Center (SCC).
Filter for VM Threat Detection findings to target the Compute Engine instances that serve as the nodes for the cluster, and look for malware or rootkits on the nodes.
C. In the Security Command Center (SCC) console, apply filters for the cluster and analyze the resulting aggregated findings' timeline and details for IOCs. Examine the attack path simulations associated with attack exposure scores to prioritize subsequent actions.
D. Create a Google SecOps SOAR playbook that automatically isolates any GKE resources exhibiting unusual network connections to production environments and triggers an alert to the incident response team.

正解：C

解説：
The most effective next step is to use Security Command Center (SCC) to filter for the relevant GKE cluster and analyze the aggregated findings. By examining the timeline and attack exposure scores, you can quickly identify potential IOCs and prioritize investigative actions. This approach leverages Google Cloud's built-in security tools for initial triage before diving into raw log analysis.

質問 # 25
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.ip_in_range_cidr(all $e.principal.ip, "192.168.2.0/24")
B. not net.ip_in_range_cidr(any $e.principal.ip, "192.168.2.0/24")
C. not net.ip_in_range_cidr(all $e.principal.ip, "192.168.2.0/24")
D. net.ip_in_range_cidr(any $e.principal.ip, "192.168.2.0/24")

正解：B

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.ip_in_range_cidr(...) 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.ip_in_range_cidr(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 <repeated_field>: The expression evaluates to true if it is true for at least one of the values in the repeated field.
* all <repeated_field>: The expression evaluates to true only if it is true for all of the values in the repeated field.
Functions > net.ip_in_range_cidr: The net.ip_in_range_cidr 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.ip_in_range_cidr(any $e.principal.ip, "10.0.0.0/8") or net.ip_in_range_cidr(any $e.principal.ip,
"172.16.0.0/12") or net.ip_in_range_cidr(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.ip_in_range_cidr

質問 # 26
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. Add an exception in the detection rule to exclude matches originating from specific asset groups.
C. Add the IP address to a Google SecOps reference list, and configure the rule to suppress alerts for that list.
D. Reduce the severity score in the rule configuration when the IOC match occurs in any internal IP address range.

正解：B

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 # 27
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