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| Certification Vendor: | Google Cloud |
|---|---|
| Exam Name: | Professional Security Operations Engineer Exam |
| Exam Number: | Professional Security Operations Engineer (PSO Engineer) |
| Exam Duration: | 120 minutes |
| Real Exam Qty: | Approximately 50–60 questions |
| Available Languages: | Japanese, English |
| Exam Format: | Multiple choice, Multiple select |
| Certificate Validity Period: | 2 years |
| Related Certifications: | Google Cloud Certified - Professional Cloud Security Engineer Google Cloud Certified - Associate Cloud Engineer |
| Exam Price: | $200 USD (may vary by region) |
| Recommended Training: | Google Cloud Security Operations Training Google Cloud Security Engineer Learning Path |
| Exam Registration: | Google Cloud Certification Registration Kryterion Webassessor Exam Scheduling |
| Sample Questions: | Google Security-Operations-Engineer Sample Questions |
| Exam Way: | Online proctored or test center (Kryterion/Webassessor) |
| Pre Condition: | Recommended: 3+ years industry experience in security operations or SOC roles, and 1+ year experience with Google Cloud security technologies |
| Official Syllabus URL: | https://cloud.google.com/certification/security-operations-engineer |
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NEW QUESTION # 63
You are responsible for identifying suspicious activity and security events at your organization.
You have been asked to search in Google Security Operations (SecOps) for network traffic associated with an active HTTP backdoor that runs on TCP port 5555. You want to use the most effective approach to identify traffic originating from the server that is running the backdoor. What should you do?
Answer: D
Explanation:
The backdoor is running on TCP port 5555 on the server, meaning the server is the source of the traffic. In Google Security Operations (SecOps), the field principal.port represents the source port of the traffic, while target.port represents the destination. Since you want to identify traffic originating from the compromised server, filtering on principal.port = 5555 is the most effective approach.
NEW QUESTION # 64
Your company recently started pulling JSON logs from a third-party system into Google Security Operations (SecOps). You noticed that some fields are missing, and you want to parse them into UDM fields as quickly as possible. What should you do?
Answer: B
Explanation:
The fastest way to handle missing fields in JSON logs is to create parser extensions using the no- code approach in Google SecOps. This allows you to quickly map additional fields into UDM without writing code or waiting on support requests, ensuring rapid parsing and normalization of the third-party logs.
NEW QUESTION # 65
Your company uses Google Security Operations (SecOps) Enterprise and is ingesting various logs. You need to proactively identify potentially compromised user accounts. Specifically, you need to detect when a user account downloads an unusually large volume of data compared to the user's established baseline activity.
You want to detect this anomalous data access behavior using minimal effort. What should you do?
Answer: C
Explanation:
Comprehensive and Detailed 150 to 250 words of Explanation From Exact Extract Google Security Operations Engineer documents:
The requirement to detect activity that is *unusual* compared to a *user's established baseline* is the precise definition of **User and Endpoint Behavioral Analytics (UEBA)**. This is a core capability of Google Security Operations Enterprise designed to solve this exact problem with **minimal effort**.
Instead of requiring analysts to write and tune custom rules with static thresholds (like in Option A) or configure external metrics (Option B), the UEBA engine automatically models the behavior of every user and entity. By simply **enabling the curated UEBA detection rulesets**, the platform begins building these dynamic baselines from historical log data.
When a user's activity, such as data download volume, significantly deviates from their *own* normal, established baseline, a UEBA detection (e.g., `Anomalous Data Download`) is automatically generated. These anomalous findings and other risky behaviors are aggregated into a risk score for the user. Analysts can then use the **Risk Analytics dashboard** to proactively identify the highest-risk users and investigate the specific anomalous activities that contributed to their risk score. This built-in, automated approach is far superior and requires less effort than maintaining static, noisy thresholds.
*(Reference: Google Cloud documentation, "User and Endpoint Behavioral Analytics (UEBA) overview";
"UEBA curated detections list"; "Using the Risk Analytics dashboard")*
NEW QUESTION # 66
Which approach BEST improves detection of compromised service accounts in Google Cloud?
Answer: D
Explanation:
Service accounts rarely fail authentication; behavioral deviation detection is most effective.
NEW QUESTION # 67
You are a security operations engineer in an enterprise that uses Google Security Operations (SecOps). You need to improve your detection coverage and reduce the false positive detection ratio as quickly as possible.
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 achieve improved coverage and reduced false positives "as quickly as possible," the correct action is to enable curated detections. These are pre-built rules managed entirely by Google, removing the need for internal development time.2 According to Google Security Operations documentation, Curated Detections are "built by our Google Cloud Threat Intelligence (GCTI) team, and are actively maintained to reduce manual toil in your team."3 The documentation explicitly highlights their speed and fidelity: "Our detections provide security teams with high quality, actionable, out-of-the-box threat detection content...4 This release helps understaffed and overstressed security teams... quickly identify threats."5 Furthermore, Curated Detections are categorized into "Precise" and "Broad" types to directly address false positive concerns.6 The documentation states: "Precise rules: Find malicious behavior with a higher degree of confidence with fewer false positives due to the more specific nature of the rule."7 By enabling these, an organization immediately gains high-fidelity coverage without the lead time required to "Develop" or
"Design" custom YARA-L rules (Options C and D) or the potential noise of raw TIP data (Option B).8 References: Google Security Operations Documentation > Detection > Use the curated detections page; Google Cloud Blog > Introducing curated detections in Chronicle SecOps Suite9
NEW QUESTION # 68
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