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Google Security-Operations-Engineer 시험요강:

주제소개
주제 1
  • 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.
주제 2
  • 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.
주제 3
  • 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.
주제 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.
주제 5
  • Platform Operations: This section of the exam measures the skills of Cloud Security Engineers and covers the configuration and management of security platforms in enterprise environments. It focuses on integrating and optimizing tools such as Security Command Center (SCC), Google SecOps, GTI, and Cloud IDS to improve detection and response capabilities. Candidates are assessed on their ability to configure authentication, authorization, and API access, manage audit logs, and provision identities using Workforce Identity Federation to enhance access control and visibility across cloud systems.

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최신 Google Cloud Certified Security-Operations-Engineer 무료샘플문제 (Q66-Q71):

질문 # 66
You are a SOC analyst working a case in Google Security Operations (SecOps). The case contains a file hash that your playbooks have automatically enriched with VirusTotal context and categorized as likely malicious. You need to quickly identify devices and users in your organization who have interacted with this file. What should you do?

정답：D

설명：
The most effective approach is to build a playbook to perform a UDM search matching on the file hash in Google SecOps SIEM. This will automatically search across your ingested telemetry to identify all devices and users that have interacted with the file, accelerating response and investigation without requiring manual intervention.


질문 # 67
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?

정답：D

설명：
On-premises firewalls cannot send logs directly to Google SecOps. The correct approach is to deploy a third-party agent (such as Bindplane or NXLog) in your on-premises environment and configure the firewalls to forward Syslog data to that agent. The agent then reliably forwards the logs to Google SecOps for ingestion.


질문 # 68
A Google Security Operations (SecOps) detection rule is generating frequent false positive alerts. The rule was designed to detect suspicious Cloud Storage enumeration by triggering an alert whenever the storage.
objects.list API operation is called using the api.operation UDM field. However, a legitimate backup automation tool that uses the same API, causing the rule to fire unnecessarily. You need to reduce these false positives from this trusted backup tool while still detecting potentially malicious usage. How should you modify the rule to improve its accuracy?

정답：A

설명：
Comprehensive and Detailed Explanation
The correct solution is Option D. The problem is that a known, trusted principal (the backup tool's service account) is performing a legitimate action (storage.objects.list) that happens to look like the suspicious behavior the rule is designed to catch.
The most precise and effective way to reduce these false positives without weakening the rule's ability to catch malicious actors is to create an exception for the trusted principal.
By adding principal.user.email != "backup-bot@fcobaa.com" (or the equivalent principal.user.userid) to the events or condition section of the YARA-L rule, the rule will now only evaluate events where the actor is not the known-good backup bot.
* Option A is incorrect because it just lowers the priority of the false positive; it doesn't stop it from being generated.
* Option B is incorrect because the legitimate tool might also perform repeated calls, leading to the same false positive.
* Option C is incorrect because api.service_name = "storage.googleapis.com" is less specific than api.
operation = "storage.objects.list" and would likely increase the number of false positives by triggering on any storage API call.
Exact Extract from Google Security Operations Documents:
Reduce false positives: When a detection rule generates false positives due to known-benign activity (e.g., from an administrative script or automation tool), the best practice is to add a not condition to the rule to exclude the trusted entity.8 You can filter on UDM fields to create exceptions. For example, to prevent a rule from firing on activity from a specific service account, you can add a condition to the events section such as:
and $e.principal.user.userid != "trusted-service-account@project.iam.gserviceaccount.com" This technique, often called "allow-listing" or "suppression," improves the rule's accuracy by focusing only on unknown or untrusted principals.
References:
Google Cloud Documentation: Google Security Operations > Documentation > Detections > Overview of the YARA-L 2.0 language > Add not conditions to prevent false positives


질문 # 69
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?

정답：A

설명：
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


질문 # 70
Which Google Cloud security feature MOST helps enforce the principle of least privilege at scale?

정답：D

설명：
IAM predefined roles and conditions minimize excessive permissions and limit blast radius.


질문 # 71
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