The best answer is Managed Assets dashboard . This dashboard-oriented view is used for operational visibility across managed endpoints and helps administrators understand how assets are distributed across important attributes, including policy-related coverage. The question asks for a “top-ten” review of sensor update, prevention, and device control policies as new hosts are added to the CID, which aligns with dashboard summarization rather than a per-host daily report. The Sensor Policy Daily Report is useful for reviewing assigned groups and policies for hosts, but it is not the best answer for a high-level top-ten usage review. Executive Summary is broader leadership reporting, not the operational location for policy-use distribution across managed assets. The CCFA reporting objective here is policy coverage awareness at scale.

The correct action is to create a Fusion SOAR workflow using the OverWatch remediation and prioritization playbook to contain the host and notify the SOC team. Fusion SOAR workflows automate response actions based on Falcon events. OverWatch detections are high-value human-hunted detections, and a predefined OverWatch playbook exists to support fast remediation actions such as containment, email notification, and related response steps. Emailing the OverWatch team is not the customer’s responsibility; the correct internal recipients are typically the SOC or incident response staff. Blocking “the detection” is not the correct workflow model because detections are records of observed behavior, while containment is the host-level response. Creating a detection is also incorrect because OverWatch already generated the detection.

Sensor Tampering Protection is the prevention policy setting that blocks attempts to interfere with core Falcon sensor components. The official prevention policy guidance states that when this setting is enabled, it “blocks attempts to tamper with the sensor” and protects “sensor-related files, folders and registry objects from renaming or deletion.” If disabled, Falcon may still create detections for tampering attempts, but it will not block the activity. This distinction is important because attackers commonly attempt to disable or corrupt endpoint security tooling before establishing persistence, evading detection, or executing payloads. Host Modification Protection, System Configuration Protection, and Sensor Modification Protection are not the named Falcon prevention setting for this control. The correct CCFA topic alignment is Policy Application, specifically Prevention Policy Settings > Sensor Capabilities > Sensor Tampering Protection.

The required role is Real Time Response - Administrator . RTR Administrator can create custom scripts, upload files for the put command, and perform higher-risk RTR operations that are not available to Active Responders. Active Responder can execute certain response actions and retrieve files, but it does not provide full script management capability. Workflow Author relates to Fusion SOAR workflows, not RTR script creation and sharing. Falcon Scripts Manager is not the correct default role in this context. The CCFA RTR topic separates roles into Read Only Analyst, Active Responder, and Administrator. Building, saving, and sharing custom scripts requires the administrative RTR capability, so RTR Administrator is the correct least role for that task.

The Machine-Learning Prevention report is used to evaluate what Falcon would have blocked under different machine-learning prevention levels. The official reporting guidance describes Machine Learning Prevention as a report that lets administrators “view malware that would have been blocked in your environment during the last 30 days based on different Machine Learning Prevention settings,” including Cautious, Moderate, or Aggressive levels. This makes option C the precise answer. The report is not the quarantine management dashboard; quarantined files are reviewed and released from the Quarantined Files area. It is also not primarily a spike-analysis dashboard for active attacks, although unusual volume may support investigation. Option D is close in theme but inaccurate because the purpose is not to summarize aggressiveness settings and actual quarantine totals; it is to model prevention impact across ML settings. Reference topics: Dashboards and Reports, Machine Learning Prevention report, prevention policy tuning, ML prevention levels.