試験Security-Operations-Engineer トピック2 問題49 スレッド
Google Security-Operations-Engineerのリアル試験問題集
問題 #: 49
トピック #: 2
問題 #: 49
トピック #: 2
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?
You want to detect this anomalous data access behavior using minimal effort. What should you do?
おすすめの解答：D 解答を投票する
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")*
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")*
星山** 2025-12-04 03:58:07
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