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Google Security-Operations-Engineer Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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.
Topic 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.
Topic 3
  • 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.
Topic 4
  • 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.
Topic 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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Google Cloud Certified - Professional Security Operations Engineer (PSOE) Exam Sample Questions (Q108-Q113):

NEW QUESTION # 108
Your organization uses the curated detection rule set in Google Security Operations (SecOps) for high priority network indicators. You are finding a vast number of false positives coming from your on-premises proxy servers. You need to reduce the number of alerts. What should you do?

Answer: D

Explanation:
Comprehensive and Detailed Explanation
The correct solution is Option B. This is a common false positive tuning scenario.
The "high priority network indicators" rule set triggers when it sees a connection to or from a known- malicious IP or domain. The problem states the false positives are coming from the on-premises proxy servers.
This implies that the proxy server itself is initiating traffic that matches these indicators. This is often benign, legitimate behavior, such as:
* Resolving a user-requested malicious domain via DNS to check its category.
* Performing an HTTP HEAD request to a malicious URL to scan it.
* Fetching its own threat intelligence or filter updates.
In all these cases, the source of the network connection is the proxy server. In the Unified Data Model (UDM), the source IP of an event is stored in the principal.ip field.
To eliminate these false positives, you must create a rule exclusion (or add a not condition to the rule) that tells the detection engine to ignore any events where the principal.ip is the IP address of your trusted proxy servers. This will not affect the rule's ability to catch a workstation behind the proxy (whose IP would be the principal.ip) connecting through the proxy to a malicious target.ip.
Exact Extract from Google Security Operations Documents:
Curated detection exclusions: Curated detections can be tuned by creating exclusions to reduce false positives from known-benign activity. You can create exclusions based on any UDM field.
Tuning Network Detections: A common source of false positives for network indicator rules is trusted network infrastructure, such as proxies or DNS servers. This equipment may generate traffic to malicious domains or IPs as part of its normal operation (e.g., DNS resolution, content filtering lookups). In this scenario, the traffic originates from the infrastructure device itself. To filter this noise, create an exclusion where the principal.ip field matches the IP address (or IP range) of the trusted proxy server. This prevents the rule from firing on the proxy's administrative traffic while preserving its ability to detect threats from end-user systems.
References:
Google Cloud Documentation: Google Security Operations > Documentation > Detections > Curated detections > Tune curated detections with exclusions Google Cloud Documentation: Google Security Operations > Documentation > Detections > Overview of the YARA-L 2.0 language


NEW QUESTION # 109
You are building a detection rule in Google Security Operations (SecOps) to alert on requests to potentially malicious domains. You are planning to use the logs from your network detection and response (NDR) solution but you need to reduce noise and narrow the scope of detections. You want to minimize cost and deploy the solution quickly. What should you do?

Answer: C

Explanation:
The most effective and efficient approach is to ingest threat intelligence platform (TIP) logs and build a multi-event rule in Google SecOps that correlates domains found in your NDR logs with your TIP's known malicious domains. This method quickly narrows detection scope to high- confidence IOCs, reduces noise, and minimizes cost and complexity compared to manual enrichment or additional monitoring services.


NEW QUESTION # 110
A SOC uses Chronicle SIEM and wants to reduce alert fatigue without lowering detection coverage. What is the BEST strategy?

Answer: B

Explanation:
Entity correlation and risk scoring preserve coverage while reducing noise.


NEW QUESTION # 111
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?

Answer: B

Explanation:
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.


NEW QUESTION # 112
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?

Answer: D

Explanation:
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


NEW QUESTION # 113
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