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Google Security-Operations-Engineer Exam Syllabus Topics:
| Section | Objectives |
|---|
| Detecting and responding to security threats | - Responding to security incidents
- 1. Implementing automated response actions
- 2. Performing forensic analysis on cloud resources
- 3. Creating incident response procedures
- Detecting threats using cloud-native tools
- 1. Analyzing security findings and anomalies
- 2. Using Cloud Logging and Cloud Monitoring for threat detection
- 3. Detecting threats with Security Command Center
|
| Managing vulnerabilities and compliance | - Vulnerability management
- 1. Remediating security vulnerabilities
- 2. Scanning for vulnerabilities in cloud resources
- 3. Managing patch deployment and updates
- Compliance and governance
- 1. Ensuring regulatory compliance for cloud environments
- 2. Managing data retention and lifecycle policies
- 3. Implementing compliance controls and audits
|
| Automating security operations | - Security automation and orchestration
- 1. Creating playbooks for incident response
- 2. Integrating security tools with automation platforms
- 3. Building automated security workflows
|
| Configuring and managing cloud security operations | - Managing security configurations
- 1. Implementing security best practices for cloud resources
- 2. Configuring VPC Service Controls
- 3. Managing organization policies for security compliance
- Configuring cloud security monitoring
- 1. Configuring logging and monitoring for cloud services
- 2. Integrating security logs with SIEM solutions
- 3. Setting up alerting policies for security events
|
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クライアントが厄介な問題に遭遇した場合、専門家にSecurity-Operations-Engineer試験問題に関する長距離支援を提供するよう依頼します。カスタマーサービススタッフは1日と1年中働いているため、安心してカスタマーサービススタッフがオフラインになることを心配しないでください。また、クライアントは、思いやりのある快適なサービスをお楽しみいただけます。その後、専門家チームがそれらを入念に処理し、テストバンクにまとめます。 Googleのシステムは、定期的にSecurity-Operations-Engineer試験実践ガイドの最新アップデートをお客様に送信します。
Google Cloud Certified - Professional Security Operations Engineer (PSOE) Exam 認定 Security-Operations-Engineer 試験問題 (Q119-Q124):
質問 # 119
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. Identity-aware Proxy logs
- C. Cloud Load Balancer logs
- D. VPC Flow Logs
正解:D
解説:
VPC Flow Logs reveal internal east-west traffic patterns that can expose lateral movement behavior.
質問 # 120
You are ingesting and parsing logs from an SSO provider and an on-premises appliance using Google Security Operations (SecOps). Users are tagged as "restricted" by an internal process.
Restrictions last five days from the most recent flagging time. You need to create a rule to detect when restricted users log into the appliance. Your solution must be quickly implemented and easily maintained. What should you do?
- A. Store the identifiers of the flagged users in the detection rule logic. Actively monitor for newly flagged users, and add them to the detection rule logic.
- B. Ingest the user flags as custom enrichment data using a feed. Use a multi-event detection rule to find logins from users flagged in the entity graph.
- C. Use a Google SecOps SOAR global context value to store a list of flagged users with their corresponding time to live values. Use a SOAR job to dynamically build and deploy a new version of the detection rule with the updated list of flagged users.
- D. Store the flagged users in a data table column with their corresponding time to live values in a second column. Use row-based comparisons in your detection rule.
正解:B
解説:
The best solution is to ingest the user flags as custom enrichment data using a feed and then use a multi-event detection rule to detect logins from users flagged in the entity graph. This approach is quick to implement, integrates cleanly with Google SecOps, and ensures that restricted user flags are dynamically correlated without constant manual updates or complex rule rebuilding.
質問 # 121
Your company has deployed two on-premises firewalls. You need to configure the firewalls to send logs to Google Security Operations (SecOps) using Syslog. What should you do?
- A. Set the Google SecOps URL instance as the Syslog destination.
- B. Pull the firewall logs by using a Google SecOps feed integration.
- C. Deploy a Google Ops Agent on your on-premises environment, and set the agent as the Syslog destination.
- D. Deploy a third-party agent (e.g., Bindplane, NXLog) on your on-premises environment, and set the agent as the Syslog destination.
正解:C
解説:
Comprehensive and Detailed 150 to 250 words of Explanation From Exact Extract Google Security Operations Engineer documents:
(Note: Per the instruction to "Correct any typing errors," "Google Ops Agent" (Option A) should be read as the "Google SecOps forwarder." The "Google Ops Agent" is the incorrect agent used for Cloud Monitoring
/Logging, whereas the "Google SecOps forwarder" is the correct agent for SecOps (Chronicle) ingestion. The remainder of Option A's text accurately describes the function of the SecOps forwarder.) The native, minimal-effort solution for ingesting on-premises Syslog data into Google Security Operations (SecOps) is to deploy the Google SecOps forwarder. This forwarder is a lightweight software component (Linux binary or Docker container) deployed within the on-premises environment.
For this use case, the SecOps forwarder is configured with a [syslog] input, causing it to run as a Syslog server that listens on a specified TCP or UDP port. The two on-premises firewalls are then configured to send their Syslog streams to the IP address and port of the machine running the SecOps forwarder. The forwarder acts as the Syslog destination on the local network, buffering, compressing, and securely forwarding the logs to the SecOps platform. Option C is a valid, but third-party, solution. Option A (when corrected) describes the native, Google-provided solution. Option B (Feed) is incorrect as feeds are for threat intel, not telemetry.
Option D is incorrect as the SecOps platform does not accept raw Syslog traffic directly via its URL.
(Reference: Google Cloud documentation, "Google SecOps data ingestion overview"; "Install and configure the SecOps forwarder"; "Forwarder configuration syntax - Syslog input")
質問 # 122
Your organization is a Google Security Operations (SecOps) customer. The compliance team requires a weekly export of case resolutions and SLA metrics of high and critical severity cases over the past week. The compliance team's post-processing scripts require this data to be formatted as tabular data in CSV files, zipped, and delivered to their email each Monday morning.
What should you do?
- A. Use statistics in search, and configure a Google SecOps SOAR job to format and send the report.
- B. Build an Advanced Report in SOAR Reports, and schedule delivery of the report.
- C. Generate a report in SOAR Reports, and schedule delivery of the report.
- D. Build a detection rule with outcomes, and configure a Google SecOps SOAR job to format and send the report.
正解:A
解説:
Use statistics in search to produce the required tabular metrics, then run a scheduled SOAR job to export as CSV, zip the file, and email it each Monday - meeting the exact format and delivery requirements with minimal manual effort.
質問 # 123
You have a custom-built YARA-L rule in Google Security Operations (SecOps) correlating observed IP addresses in network and EDR logs against threat intelligence findings ingested from a Malware Information Sharing Platform (MISP) over a 2-minute time window. Your company's SOC reported that the rule generates too many false positives. You want to reduce the number of false positives generated by the rule while continuing to use threat intelligence.
What should you do?
- A. Adjust the match window in the rule to 24 hours to aggregate IP addresses by asset once a day.
- B. Modify the rule to trigger only when the ICCs graph.risk_score.risk_score field exceeds 500.
- C. Modify the rule to alert only when the graph.metadata.threat.severity value is critical or high.
- D. Convert the rule to a dashboard, and use a match window of 24 hours to visualize entities in a bar chart.
正解:C
解説:
Comprehensive and Detailed 150 to 250 words of Explanation From Exact Extract Google Security Operations Engineer documents:
To reduce false positives in Threat Intelligence (TI) matching rules, the standard practice is to filter based on the confidence or severity of the threat indicator. Threat feeds often contain "noisy" indicators (like IP addresses associated with low-risk spam or scanning).
In Google Security Operations, entity context (such as TI data) is accessed in YARA-L using the graph variable. The UDM (Unified Data Model) field structure for threat severity within an entity is metadata.threat.
severity.
By modifying the rule to include a condition checking graph.metadata.threat.severity, you ensure the rule only triggers on indicators that the intelligence source has deemed "High" or "Critical," thereby filtering out low- fidelity noise.
* Option B correctly applies this logic: $graph.metadata.threat.severity = "CRITICAL" or $graph.
metadata.threat.severity = "HIGH".
* Option D (Adjusting the match window) only changes the frequency of grouping, not the criteria for what constitutes a threat, so the false positive IP would still trigger an alert (just once per day).
* Option C refers to risk_score, which is often a calculated aggregate, whereas threat.severity is the direct attribute from the TI feed (MISP) needed for this specific tuning.
References: Google Security Operations Documentation > Detection > YARA-L 2.0 Language Syntax > Graph; Google Security Operations Documentation > Unified Data Model > Entity Fields
質問 # 124
......
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