20+ practice questions focused on Mitigate threats using Microsoft Sentinel — one of the most tested topics on the Microsoft Security Operations Analyst SC-200 exam. Each question includes a detailed explanation so you learn why the right answer is correct.
Start Mitigate threats using Microsoft Sentinel PracticeA security operations analyst is creating a scheduled analytics rule in Microsoft Sentinel to detect brute force attempts on Microsoft Entra ID authentication. Which data source is most appropriate for this rule?
Explanation: SigninLogs captures user authentication attempts to Microsoft Entra ID, including failed sign-ins, which are essential for detecting brute force attacks. This data source provides detailed properties such as IP address, application, and status codes (e.g., 50076 for invalid password), enabling accurate detection of repeated failed attempts. Azure Activity Logs, Office Activity Logs, and SecurityEvent do not contain Entra ID authentication events.
A security analyst wants to configure a playbook in Microsoft Sentinel that runs automatically when a specific alert is generated. Which trigger concept is used to invoke the playbook?
Explanation: In Microsoft Sentinel, playbooks are built on Azure Logic Apps, and the correct trigger to invoke a playbook automatically when an alert is generated is the Azure Logic Apps trigger. This trigger listens for the Sentinel alert creation event and initiates the playbook workflow. The other options are not valid trigger concepts within Sentinel's architecture.
A security analyst is preparing to use a Jupyter notebook for threat hunting in Microsoft Sentinel. Which of the following sequences of actions is correct to start executing the notebook?
Explanation: Option A is correct because the correct sequence to start executing a Jupyter notebook for threat hunting in Microsoft Sentinel is to first provision compute (i.e., create a compute instance in Azure Machine Learning), then clone the Sentinel notebooks from the official GitHub repository, connect to the Sentinel workspace using the msticpy library, and finally execute the cells. This order ensures the compute environment is ready before loading the notebooks and establishing the workspace connection.
A security operations center (SOC) uses Microsoft Sentinel. The team wants to detect anomalous behavior for a specific user account that typically logs in only during business hours from a known IP range. They create a scheduled analytics rule that queries the SigninLogs table for logins outside that range or outside business hours. To reduce false positives, which of the following configurations should the analyst apply?
Explanation: Option A is correct because setting an alert threshold (e.g., 5 occurrences within the query lookback period) reduces false positives by requiring the anomalous behavior to be persistent rather than a single outlier. In Microsoft Sentinel, the alert threshold filters out noise from occasional legitimate logins that might accidentally fall outside business hours or the known IP range, ensuring the rule only fires when the pattern is repeated enough to indicate a real threat.
A threat hunter in Microsoft Sentinel writes a KQL query in the Logs blade to find possible data exfiltration. The query uses the CommonSecurityLog table to look for large outbound file transfers from a specific IP address. The analyst wants to include only events where the total bytes sent in a 5-minute window exceed 100 MB. Which KQL operator combination would best achieve this?
Explanation: Option A is correct because it first filters the CommonSecurityLog table for the specific source IP, then uses `summarize` with `bin(TimeGenerated, 5m)` to aggregate total bytes sent in 5-minute windows, and finally filters for windows where the sum exceeds 100 MB (100,000,000 bytes). This correctly implements a time-windowed aggregation to detect large outbound transfers, which is the standard pattern for identifying data exfiltration over a period.
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