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| Section | Weight | Objectives |
|---|---|---|
| Incident response | 21% | - Automate response workflows - Develop and use response playbooks - Investigate security incidents - Contain and eradicate threats |
| Observability | 10% | - Report security posture and risks - Analyze telemetry and metrics - Improve security visibility - Design monitoring and alerting strategies |
| Detection engineering | 22% | - Optimize detection logic and reduce false positives - Manage detection lifecycle - Develop detection rules (YARA-L, Sigma) - Implement threat intelligence into detections |
| Data management | 14% | - Ingest and normalize logs and data - Implement Unified Data Model (UDM) - Validate data quality and completeness - Manage data retention and storage |
| Threat hunting | 19% | - Use threat intelligence in hunting - Document and share findings - Design and execute threat hunts - Analyze anomalies and behaviors |
| Platform operations | 14% | - Manage Google Security Operations platform - Monitor platform health and performance - Configure Security Command Center - Manage access and permissions |
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NEW QUESTION # 109
Your team has onboarded a new log source from a third-party DNS filtering solution. After ingestion, you observe that key UDM fields such as network.dns.questions.name and metadata.product_event_type are missing from the parsed events in Google Security Operations (SecOps). You suspect that the default parser does not fully align with the source format. You need to ensure these fields are available for downstream detection rules that rely on DNS query telemetry and event categorization. What should you do?
Answer: B
Explanation:
The correct approach is to create a parser extension that maps the missing source fields (e.g., DNS query names and event type) to the appropriate UDM fields and attach it to the existing parser. Parser extensions allow you to customize field mappings without replacing the default parser, ensuring that downstream detections relying on DNS telemetry and event categorization work correctly.
NEW QUESTION # 110
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: C
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 # 111
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?
Answer: D
Explanation:
(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")
NEW QUESTION # 112
Which Google Cloud security feature MOST helps enforce the principle of least privilege at scale?
Answer: A
Explanation:
IAM predefined roles and conditions minimize excessive permissions and limit blast radius.
NEW QUESTION # 113
You work for an organization that operates an ecommerce platform. You have identified a remote shell on your company's web host. The existing incident response playbook is outdated and lacks specific procedures for handling this attack. You want to create a new, functional playbook that can be deployed as soon as possible by junior analysts. You plan to use available tools in Google Security Operations (SecOps) to streamline the playbook creation process. What should you do?
Answer: B
Explanation:
Comprehensive and Detailed Explanation
The correct solution is Option C. The primary constraints are to "streamline" the process, create a "new, functional playbook," get it "as soon as possible," and "use available tools in Google Security Operations." Google Security Operations integrates Gemini directly into the SOAR platform to accelerate security operations. One of its key capabilities is generative playbook creation. This feature allows an analyst to describe their intended objectives in natural language (e.g., "Create a playbook to investigate and respond to a remote shell alert"). Gemini then generates a complete, logical playbook flow, including investigation, enrichment, containment, and eradication steps.
This generated playbook serves as a high-quality draft. The analyst can then add the necessary customizations (like specific tools, notification endpoints, or contacts for the e-commerce platform) and, most importantly, test the playbook to ensure it is functional and reliable for junior analysts to execute. This workflow directly meets all the prompt's requirements, especially "streamline" and "as soon as possible." Option D (creating a custom playbook from scratch and using a red team) is the exact opposite of streamlined and fast. Option B involves patching an "outdated" playbook, not creating a new one. Option A incorrectly bundles a specific remediation action (filtering traffic) with the playbook creation process.
Exact Extract from Google Security Operations Documents:
Gemini for Security Operations: Gemini in Google SecOps provides generative AI to assist analysts and engineers. Within the SOAR capability, Gemini can generate entire playbooks from natural language prompts.
Playbook Creation with Gemini: Instead of building a playbook manually, an engineer can describe the intended objectives of the response plan. Gemini will generate a new playbook with a logical structure, including relevant actions and conditional branches. This generated playbook serves as a strong foundation, which can then be refined. The engineer can add necessary customizations to tailor the playbook to the organization's specific environment, tools, and processes. Before deploying the playbook for use by the SOC, it is a best practice to test it against simulated alerts to validate its functionality and ensure it runs as expected.
References:
Google Cloud Documentation: Google Security Operations > Documentation > SOAR > Gemini in SOAR > Create playbooks with Gemini
NEW QUESTION # 114
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