SC-200 Exam Dumps - Microsoft Certified: Security Operations Analyst Associate Questions and Answers

To block unsanctioned cloud apps on Windows 10 endpoints with Microsoft Defender for Endpoint and Microsoft Defender for Cloud Apps (formerly Cl oud App Security), you must enable and configure the product integration on both sides. First, in Microsoft Defender Security Center → Settings → Advanced features , turn on the Microsoft Defender for Cloud Apps integration (and ensure network protection pr erequisites are met). This allows Defender for Endpoint to receive the unsanctioned app list and enforce endpoint-based blocking when users on CLIENT1 attempt to access those apps via the browser or client.

Second, in Defender for Cloud Apps → Settings → C loud Discovery , configure the Microsoft Defender for Endpoint integration and enable Block unsanctioned apps . In Cloud Discovery, apps are discovered, assessed, and can be tagged as Unsanctioned . Once the MDE integration is enabled, that tag is exported to endpoints, which then enforce blocking based on the tenant’s app catalog and policies.

Options A (Onboarding settings) are for enrolling devices and do not control app blocking behavior. B (Anomaly detection policies) govern behavioral detections (e.g., i mpossible travel, anonymous IP) and are unrelated to endpoint enforcement of app access. Therefore, the two configurations you must modify to meet the requirement “block unsanctioned apps on Windows 10 computers by using Microsoft Defender for Endpoint” ar e C. Advanced features in Microsoft Defender Security Center and D. Cloud Discovery settings in Cloud App Security .

In Microsoft Sentinel , a bookmark is a saved record of an event or query result that an analyst can use to support investigations. Bookmarks are typically created during proactive threat hunting or investigation activities to capture and retain evidence for later correlation or incident enrichment.

According to Microsoft’s Sentinel documentation, the Hunting blade pro vides a workspace where analysts can run Kusto Query Language (KQL) queries across data sources to identify anomalies or suspicious activity. When a suspicious event or pattern is found, the analyst can create a bookmark directly from the Hunting blade . Th is bookmark stores query details, time range, and contextual information for use in later investigations.

Once the bookmark exists, it can be associated with a specific incident within Sentinel. This association occurs through the Incident blade , where analysts manage alerts, incidents, and investigation details. From the Incident blade, you can attach or link existing bookmarks to provide additional evidence or context, enriching the investigation record and improving traceability within the incident ma nagement lifecycle.

In summary:

Hunting blade → Used to create bookmarks from proactive searches or hunting queries.

Incident blade → Used to associate bookmarks with ongoing incidents for investigation correlation.

Therefore, the correct sequence is:

Crea te a bookmark using the Hunting blade , and associate it with the incident using the Incident blade .

The requirement states that Cloud App Security (Defender for Cloud Apps) must determine whether a user’s connection is anomalous based on tenant-level patterns , and the current false positives occur when users connect through two office egress points at the same time. These symptoms align with the Impossible travel anomaly detection policy, which learns normal sign-in geolocation patterns and flags sign-ins from distant locations within an unrealistically short time window. To meet the requirement and reduce false positives, you modif y the Impossible travel policy settings—such as excluding trusted corporate IP ranges/VPN egress points and tuning sensitivity—so detections better reflect tenant-wide behavior rather than isolated user hops via different office exits. Policies like Activi ty from anonymous/suspicious IP addresses rely on threat-intel lists of anonymizers or known-bad sources and don’t address the “two-office” scenario. Risky sign-in is part of Azure AD Identity Protection, not the MCAS anomaly policy to tune here. Thus, the policy to modify is Impossible travel .

In Microsoft Sentinel, entity ma pping is a critical configuration that ensures detected events and alerts are correctly represented in the investigation graph , incidents , and hunting experiences . The Sentinel requirements in the case study specify:

“Add notes to events that represent dat a access from a specific IP address to provide the ability to reference the IP address when navigating through an investigation graph while hunting.”

To meet this requirement, the analytic rule must be configured to map entities such as IP address, user, h ostname, or URL in the Set rule logic section. This mapping allows the incident and its related alerts to visually associate with those entities, enabling analysts to pivot and investigate in the Sentinel investigation graph.

According to Microsoft Sentine l documentation:

“Entity mapping in analytic rules helps correlate alerts and incidents to specific entities such as accounts, IPs, or hosts, enabling richer investigation experiences and faster triage.”

Therefore, configuring entity mapping directly under Set rule logic ensures that incidents are enriched with contextual information (for example, the specific IP address), meeting both the functional and investigative requirements.

✅ Final Answer for Question 2: C. From Set rule logic, map the entities.

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 Securit y 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 la beled 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 inventor y 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.

To complete the KQL query against the BehaviorAnalytics t able, 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 p ane, 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 typ e your KQL, making it straightforward to complete a clause like | where < ColumnName > == 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 autocompl ete to insert the correct column name.

 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 typi cal 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 t o 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 DeviceProcess Events 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 Micros oft’s official Defender for Identity deployment documentation, setting up Microsoft Defender for Identity (MDI) on an on-premises domain controller (DC) such as DC1 requires a specific sequence of steps. MDI monitors and protects Active Directory from adva nced threats by using sensors installed directly on domain controllers.

The correct sequence is as follows:

1️ ⃣ Provide global administrator credentials to the Azure AD tenant.

Microsoft Defender for Identity is created and managed in the Microsoft 365 Def ender portal or Microsoft Security portal, which requires global administrator permissions to provision the MDI instance and connect it to the Azure AD tenant.

2️ ⃣ Create an instance of Microsoft Defender for Identity.

You must create an MDI instance in th e portal before deploying any sensors. This instance defines your Defender for Identity workspace and generates the configuration package that sensors will later use to connect to the cloud service.

3️ ⃣ Provide domain administrator credentials to the on-pr emises Active Directory domain.

Domain admin credentials are required for the sensor installation because the sensor must access the domain controller’s security logs, event logs, and directory services to monitor authentication traffic and suspicious acti vities.

4️ ⃣ Install the sensor on DC1.

Since DC1 is a domain controller connected directly to the internet, the standard sensor (not the standalone sensor) is installed directly on it. The standalone sensor is used only when you do not want to install the sensor directly on a DC. The sensor automatically connects to the created MDI instance and begins collecting telemetry for detection.

This sequence aligns with Microsoft Defender for Identity deployment guidance, ensuring secure onboarding of DC1 while mai ntaining the principle of least privilege and meeting the business requirement to protect all domain controllers.

✅ Final Order:

Provide global administrator credentials →

Create instance of Microsoft Defender for Identity →

Provide domain administrator credentials →

Install the sensor on DC1.

As outlined in Microsoft’s official Defender for Of fice 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 c ollabora tion 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 d etected, 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.