Free Microsoft SC-200 Practice Exam with Questions & Answers | Set: 4

To show labeled files from Windows 10 endpoints in the Azure Information Protection – Data discovery dashboard, you must first enable the built-in integration between Microsoft Defender for Endpoint and Azure Information Protection (AIP). This is turned on in the Microsoft Defender Security Center under Settings → Advanced features. When enabled, Defender for Endpoint inventories sensitivity labels seen on files across managed Windows devices and streams that telemetry to the AIP Data discovery experience, providing visibility into where labeled data resides on endpoints. Scanner clusters and content scan jobs in AIP are intended for on-premises repositories (file shares/SharePoint servers), not for endpoint discovery. Device health/compliance reports do not surface or forward label inventory to AIP. Therefore, the first configuration step is enabling the AIP integration advanced feature in Defender for Endpoint so labeled files on Windows clients appear in the AIP Data discovery dashboard.

 Internal threat: Modify the access policy settings for the key vault.

 External threat: Modify the Key Vault firewall settings.

For internal threats involving a potential compromise of Fabrikam’s own Azure AD applications, the most direct and least disruptive remediation is to modify the Key Vault access policies (or RBAC assignments, if RBAC for Key Vault data-plane is in use) to immediately remove or reduce the compromised service principal’s permissions (Get/List/Decrypt/Sign/Wrap). Microsoft guidance for Key Vault access emphasizes least privilege and promptly revoking credentials or app permissions when compromise is suspected. Access policies (or data-plane RBAC) govern which identities can access secrets, keys, and certificates; adjusting these stops further data-plane actions by the compromised app. “Resource locks” protect against deletion or configuration changes at the management plane, but they do not remove a compromised identity’s ability to read or use vault objects, so they are not an appropriate first response for this scenario.

For external threats, Microsoft recommends hardening Key Vault firewall and networking: restrict public network access, allow only required IPs, use virtual network rules, and prefer private endpoints. Key Vault includes a built-in firewall for IP and VNet ACLs; tuning these controls reduces exposure to the public internet and blocks unauthorized traffic. NSGs apply to IaaS subnets/nics and don’t directly secure the public Key Vault endpoint. Azure Firewall can add perimeter control, but it is not necessary for remediating a specific Key Vault Defender alert; the most effective and immediate remediation is tightening the Key Vault firewall settings to limit external access pathways.

In Microsoft Sentinel (built on Azure Monitor Logs), analytics and hunting queries are executed within a Log Analytics workspace. To run Sentinel queries for Fabrikam, the tenant must have at least one workspace (with Sentinel enabled) in its subscription to host rules, incidents, hunting queries, and workbooks. Sentinel’s cross-workspace/tenant capability is provided by cross-resource queries in Kusto Query Language (KQL). The key construct for reaching outside the current workspace is the workspace() function, which lets you reference another Log Analytics workspace by name or resource ID—even across subscriptions or tenants when proper permissions (often via Azure Lighthouse or guest access) are in place.

Typical correlation looks like:

union workspace('Fabrikam-WS').SecurityEvent, workspace('Contoso-WS').SecurityEvent | …

Here, workspace() is the required element to bring together data sets from multiple tenants; operators like extend and project only shape columns and do not establish cross-tenant scope. Therefore, to meet the requirements with minimal overhead: Fabrikam needs one workspace to host its Sentinel content, and you use workspace() in your KQL to correlate Contoso and Fabrikam data.

To remediate active attacks automatically once alerts or incidents are detected, Microsoft Sentinel uses playbooks, which are workflows built on Azure Logic Apps. These playbooks can execute remediation actions—such as isolating a machine, blocking an account, or triggering other security control changes—without manual intervention. Microsoft’s documentation clearly states that “playbooks in Microsoft Sentinel are based on workflows built in Azure Logic Apps” and that they can “automate and orchestrate your threat response by using playbooks … run a playbook on-demand or automatically in response to specific alerts or incidents.”

When an analytics rule in Sentinel triggers an alert or incident, you can attach an automation rule which in turn invokes a playbook (i.e. a Logic Apps workflow) to perform the remediation steps. The automation rule defines the trigger conditions and calls the playbook action as part of its response actions.

Let us evaluate other options:

Azure Automation runbooks (Option A) are powerful for scripting in Azure (e.g., PowerShell or Python) and can perform remediation tasks, but they are not the native mechanism within Sentinel for orchestrated, alert-driven response workflows.

Azure Functions (Option C) are serverless compute for custom code, but you would have to build and integrate orchestration logic manually; they are not the out-of-box SOAR component in Sentinel.

Azure Sentinel livestreams (Option D) is not a recognized remediation automation component—it is irrelevant in this context.

Therefore, the correct solution to remediate active attacks (triggering automated actions in response to alerts/incidents with minimal manual effort) is to use Azure Logic Apps (via Sentinel playbooks) as the orchestration engine. Logic Apps are the documented foundation of Sentinel’s automation response capabilities.

As outlined in Microsoft’s official Defender for Office 365 documentation, this service provides comprehensive protection against threats targeting Microsoft 365 collaboration tools—such as SharePoint Online, OneDrive for Business, and Microsoft Teams. The marketing team uses SharePoint Online for vendor collaboration and has experienced incidents in which vendors uploaded malicious files. Microsoft Defender for Office 365 specifically addresses this scenario through features like Safe Attachments and Safe Links, which automatically scan uploaded or shared files for malware and block access to harmful content.

When a vendor uploads a file to SharePoint Online, Defender for Office 365 inspects the file in real time within a virtual sandbox environment before allowing users to open or share it. If malware is detected, the system quarantines or removes the file and notifies administrators. These detection and remediation capabilities prevent infection propagation, protect sensitive marketing data, and maintain compliance with Contoso’s security posture.

By leveraging Defender for Office 365, Contoso’s marketing team can continue external collaboration safely, ensuring that all uploaded files are scanned and validated before internal access—thereby resolving their specific malware-related issue.

 Table: DeviceFileEvents

 Aggregation function: count()

In Microsoft Defender XDR advanced hunting, data tables such as DeviceFileEvents, DeviceProcessEvents, and CloudAppEvents are used to investigate various types of activities. Since this query aims to investigate an issue related to file activity—specifically identifying when files have been accessed, modified, or created repeatedly—the correct data source table is DeviceFileEvents. This table contains information about file-level activities recorded by Defender for Endpoint sensors, including file path, file name, action type, and user account involved.

The KQL structure shown in the image follows standard hunting query syntax:

DeviceFileEvents

| where Timestamp > ago(2d)

| summarize activityCount = count() by FolderPath, FileName, ActionType, AccountDisplayName

| where activityCount > 5

Here’s why:

The where Timestamp > ago(2d) clause filters results from the last 2 days, a typical timeframe for immediate investigations.

The summarize operator groups events by FolderPath, FileName, ActionType, and AccountDisplayName, then uses count() to determine how many times each file was acted upon.

Finally, where activityCount > 5 filters to show only unusually high-frequency activity, which might indicate suspicious or automated file manipulation.

Microsoft Defender XDR documentation highlights that DeviceFileEvents is the correct schema for file activity investigations, while DeviceProcessEvents focuses on process creation and execution, and CloudAppEvents targets cloud application usage.

Thus, the verified and documented correct completions are:

Table: DeviceFileEvents

Aggregation function: count()

According to Microsoft Security Operations documentation, Microsoft Defender for Endpoint is designed to protect endpoint devices—including Windows, macOS, Android, and iOS—against cyberattacks through advanced behavioral analysis, threat intelligence, and automated investigation and remediation. In the given case study, the sales team exclusively uses iOS devices and has previously experienced attacks while exchanging files using third-party applications. These unmanaged file-sharing methods exposed the team to malware, phishing, and data leakage threats.

By implementing Microsoft Defender for Endpoint on iOS, Contoso can apply unified endpoint protection across all mobile devices. Defender for Endpoint’s mobile threat defense (MTD) capabilities detect malicious apps, risky network connections, jailbroken devices, and phishing attempts. It also integrates with Microsoft Intune for compliance enforcement and conditional access—ensuring only secure, compliant devices can access corporate resources. This directly mitigates the security challenges faced by the sales team while minimizing manual investigation effort through automated response.

Therefore, the issue affecting the sales team (mobile device attacks and unsafe file transfers) can be effectively resolved using Microsoft Defender for Endpoint.

To complete the KQL query against the BehaviorAnalytics table, you need to know the exact column name (for example, the Boolean field that flags a new or first-time country for the sign-in). Microsoft’s standard method to discover table schemas and column names is the Logs (Log Analytics) query window. In this pane, the left-hand Schema browser lists all connected tables and, when expanded, shows every column name and data type. Selecting a table (e.g., BehaviorAnalytics) reveals its fields, and the editor provides IntelliSense/autocomplete for columns as you type your KQL, making it straightforward to complete a clause like | where == true.

Security alerts in Azure Security Center (Defender for Cloud), the Azure Activity log, and Azure Advisor do not expose the per-table column schema needed to build KQL filters. Security Center surfaces alerts and recommendations; the Activity log records control-plane operations; and Advisor provides optimization guidance—none of these replace the Logs experience for exploring data schemas.

Therefore, to accurately identify and verify the column required in the where clause for failed sign-ins from a first-time country, you should use the Log Analytics workspace query window, consult the Schema pane for the BehaviorAnalytics table, and leverage the editor’s autocomplete to insert the correct column name.