Microsoft SC-200 Dumps

Azure Sentinel “playbooks” are Azure Logic Apps. Granting the minimal permissions to configure (create/edit) playbooks requires the Logic App Contributor role on the resource group where the playbooks reside. This satisfies the business requirement to use least privilege and specifically enables admin1 to design, modify, and manage Logic Apps that Sentinel automation rules or analytics rules will invoke. Roles like Automation Operator or Automation Runbook Operator apply to Azure Automation, not Logic Apps, and therefore don’t allow creating or editing Sentinel playbooks. Azure Sentinel Contributor allows managing Sentinel resources (incidents, analytics rules, workbooks) but, by itself, does not grant permissions to author Logic Apps. Assigning Logic App Contributor provides precisely what is needed to configure Sentinel playbooks without unnecessary broader permissions.

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 modify 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 Activity 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.

To attach notes that you can later see during investigations and hunting, you use Bookmarks in Microsoft Sentinel. Bookmarks are created from the Hunting (or Logs) experience inside the Sentinel workspace and let you pin a specific query result (including IPs, accounts, hosts) with notes, tags, and mapped entities so it appears in the investigation graph and is easily referenceable. The correct workflow is: first, run your KQL query from the Microsoft Sentinel workspace (not from generic Azure Monitor), because Sentinel’s hunting experience is where bookmarks integrate with incidents and the investigation graph. Next, select a specific query result that represents the suspicious activity (e.g., a data access event from the target IP). Finally, create a Bookmark and map the relevant entity (IP address, Account, etc.) while adding your notes. Mapping the entity ensures the bookmarked event is connected in the investigation graph; the notes provide the narrative/context you need when pivoting later. Adding the query to favorites is optional for convenience but does not attach notes to a specific event, and running the query from Azure Monitor would not place the bookmark within Sentinel’s investigation context.

To restrict cloud apps running on CLIENT1 (a Windows 10 endpoint) in compliance with Microsoft Defender for Endpoint (MDE) requirements, you must integrate Microsoft Defender for Endpoint with Microsoft Defender for Cloud Apps (formerly Cloud App Security) using Cloud Discovery. This integration enables the blocking of unsanctioned cloud apps through the endpoint’s network protection capabilities.

According to Microsoft Defender for Cloud Apps documentation, Cloud Discovery uses traffic data from Defender for Endpoint to identify and manage the use of shadow IT. The relevant steps include:

1️⃣ Enable advanced features in Microsoft Defender for Endpoint (M365 Defender portal → Settings → Endpoints → Advanced features).

You must enable the following advanced features:

Microsoft Defender for Cloud Apps integration

Network protection (in block mode)

Custom network indicators (if applicable)These options allow Defender for Endpoint to share telemetry and enforce app restrictions received from Defender for Cloud Apps.

2️⃣ Configure Cloud Discovery settings in Microsoft Defender for Cloud Apps.

In the Defender for Cloud Apps portal, Cloud Discovery must be configured to receive continuous reports from Defender for Endpoint devices. Within these settings, you define sanctioned and unsanctioned applications. Once an app is marked as unsanctioned, Defender for Endpoint enforces blocking on all onboarded devices (like CLIENT1).

This two-part configuration ensures that MDE enforces the blocking of unsanctioned cloud applications discovered through Cloud App Security telemetry, fulfilling the business requirement that “All Cloud App Security unsanctioned apps must be blocked on the Windows 10 computers by using Microsoft Defender for Endpoint.”

The test analytics rule must generate alerts for inbound Office 365 access by several test users and group those alerts into separate incidents—one per user. In Azure Sentinel, incident grouping by entity depends on the rule’s Entity mapping. When you create a scheduled analytics rule, under Set rule logic you map columns from your query to entities like Account, IP, or Host. Once mapped, you can configure Event grouping so alerts with the same entity value (e.g., the same Account) are automatically grouped into a single incident. Turning suppression on/off or changing severity/tactics doesn’t influence entity-based incident grouping. Therefore, to ensure “one incident per test user account,” you must map the Account entity (and any other relevant entities) in Set rule logic, then enable grouping by that entity—fulfilling the Sentinel requirement.QUESTION NO: 3 HOTSPOT

You need to create the analytics rule to meet the Azure Sentinel requirements.

What should you do? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.

Answer:

According to Microsoft Security Operations (SecOps) and Azure Sentinel documentation, when you need to create an analytics rule that executes a custom KQL query and automatically initiates a playbook, the correct configuration is to create a Scheduled rule and ensure the playbook includes a trigger.

Here’s why:

A Scheduled analytics rule in Microsoft Sentinel (Microsoft Defender XDR portal) is designed for running custom KQL queries at defined intervals (for example, every hour or every few minutes) to detect specific patterns of suspicious activity. When the rule’s conditions are met, Sentinel generates alerts that can automatically trigger a playbook for response and automation.

A playbook in Sentinel is an Azure Logic App that automates responses to incidents or alerts. To connect a playbook to an analytics rule, it must include a trigger—specifically, the “Microsoft Sentinel Alert” or “Incident trigger.” This allows the rule to start the playbook automatically when the defined condition is met.

The other options are incorrect because:

Fusion rules are built-in and use Microsoft’s machine learning to correlate signals automatically; they can’t be used for custom queries.

Microsoft incident creation rules are also built-in and handle alert-to-incident grouping logic, not custom query execution.

A service principal would be needed for permissions (e.g., admin1 configuring playbooks), but not inside the playbook itself.

Diagnostics settings apply to log collection and retention, not rule automation.

Therefore, based on Microsoft Sentinel best practices and documentation:

✅ Create the rule of type: Scheduled

✅ Configure the playbook to include: A trigger

According to Microsoft’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 advanced threats by using sensors installed directly on domain controllers.

The correct sequence is as follows:

1️⃣ Provide global administrator credentials to the Azure AD tenant.

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

2️⃣ Create an instance of Microsoft Defender for Identity.

You must create an MDI instance in the 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.

3️⃣ Provide domain administrator credentials to the on-premises 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 activities.

4️⃣ 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 maintaining 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.

The problem described in the case study states that “Cloud App Security frequently generates false positive alerts when users connect to both offices simultaneously.” This behavior is commonly associated with the “Impossible travel” anomaly detection policy in Microsoft Defender for Cloud Apps.

According to Microsoft documentation, the “Impossible travel” policy detects when a user signs in from two locations that are geographically distant within an unrealistic timeframe. However, in multi-office environments (such as Boston and Seattle) or when VPN connections are used, this policy can frequently trigger false positives, since it may misinterpret legitimate connections as anomalous.

Microsoft recommends adjusting the impossible travel detection policy to account for trusted IP ranges, known locations, or VPN endpoints to reduce false alerts. Specifically, administrators can modify the policy’s sensitivity, include the organization’s office IP addresses as trusted, and exclude known network ranges.

This approach directly aligns with the case study’s scenario and satisfies the Defender for Cloud Apps requirement to reduce false positives while maintaining user anomaly detection accuracy.

✅ Final Answer for Question 3: D. Impossible travel

According to Microsoft Defender for Cloud (formerly Azure Security Center) documentation, when integrating servers and virtual machines for security monitoring, the Defender for Cloud data is stored and analyzed in a Log Analytics workspace. Microsoft best practices recommend using an existing workspace when one is already deployed for centralized log collection—especially if it already gathers telemetry from Azure and on-premises resources.

In this case, the existing workspace LA1 already “contains logs and metrics collected from all Azure resources and on-premises servers.” This satisfies the business requirement of “all servers must send logs to the same Log Analytics workspace.” Creating a new workspace would violate the cost-minimization and centralization requirements. Therefore, LA1 is the correct workspace to use.

For the Windows security events collection setting, Defender for Cloud offers three levels:

Minimal – collects essential security events only (insufficient for comprehensive monitoring).

Common – collects a balanced set of security-related events (such as account logon, privilege use, and object access), recommended by Microsoft for most environments.

All Events – collects every possible security event, which increases data ingestion cost and is typically used only for high-security or regulated environments.

Because Litware Inc. must minimize cost while ensuring a full audit trail of user activities, the Common level provides the optimal balance of visibility and cost efficiency.

Therefore, based on Microsoft Defender for Cloud configuration guidelines:

✅ Log Analytics workspace to use: LA1

✅ Windows security events to collect: Common

To block unsanctioned cloud apps on Windows 10 endpoints with Microsoft Defender for Endpoint and Microsoft Defender for Cloud Apps (formerly Cloud 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 prerequisites 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 → Cloud 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., impossible 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” are C. Advanced features in Microsoft Defender Security Center and D. Cloud Discovery settings in Cloud App Security.

To integrate Microsoft Defender for Cloud Apps (MCAS) with Microsoft Sentinel, Microsoft’s official SecOps and Sentinel documentation specifies a two-step configuration process.

In the Defender for Cloud Apps portal – You add a security extension to enable integration with external SIEM platforms. This action allows MCAS to forward its alerts, activities, and discovered app telemetry to other Microsoft or third-party security platforms. By adding the security extension, Defender for Cloud Apps is authorized to send data streams and alerts to Microsoft Sentinel through a supported API connection.

In Microsoft Sentinel (Azure portal) – You then add a data connector. Data connectors in Sentinel are predefined integration pipelines that bring in telemetry from Microsoft or external security solutions. The Microsoft Defender for Cloud Apps connector specifically ingests MCAS alerts and audit logs into Sentinel, where they can be correlated with other Microsoft Defender XDR signals, enabling unified detection and investigation across identity, endpoint, and cloud layers.

This integration approach adheres to Microsoft’s principle of minimizing administrative effort by using native connectors rather than custom ingestion or log collector configurations. Once connected, Sentinel automatically normalizes MCAS alerts into its SecurityAlert and CloudAppEvents tables for rule creation, playbook automation, and incident correlation.

Therefore, the verified correct configuration is:

Defender for Cloud Apps: Add a security extension

Sentinel: Add a data connector

In Microsoft Sentinel, entity mapping 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 data 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, hostname, 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 Sentinel 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.

 Log Analytics workspace to use: LA1

 Windows security events to collect: All Events

To meet the Azure Defender (Microsoft Defender for Cloud) requirement that all servers send logs to the same Log Analytics workspace, you should select the existing workspace LA1. Defender for Cloud best practices recommend centralizing data in a single workspace for unified analytics, incident correlation, and cost control. Using the “Default workspace created by Azure Security Center” or creating a new workspace would fragment telemetry, complicate management, and contradict the stated requirement and the business goal to minimize costs (multiple workspaces can increase ingestion/retention overhead and complicate RBAC and automation).

For Windows hosts, Defender for Cloud’s Data collection setting controls the level of Windows Security Events collected: Minimal, Common, or All Events. The business requirement calls for logs that provide a full audit trail of user activities. In Microsoft guidance, All Events is the level intended for comprehensive auditing (including logon/logoff, account changes, privilege use, process creation, object access, and other advanced categories). Therefore, to satisfy the “full audit trail” requirement and ensure complete visibility for investigations and Sentinel analytics, choose All Events.

In summary: centralize on LA1 (single workspace) and collect All Events to achieve both operational and compliance objectives with Defender for Cloud and Sentinel.

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.

To integrate Microsoft Defender for Cloud Apps (MCAS) with Microsoft Sentinel, Microsoft’s official SecOps and Sentinel documentation specifies a two-step configuration process.

In the Defender for Cloud Apps portal – You add a security extension to enable integration with external SIEM platforms. This action allows MCAS to forward its alerts, activities, and discovered app telemetry to other Microsoft or third-party security platforms. By adding the security extension, Defender for Cloud Apps is authorized to send data streams and alerts to Microsoft Sentinel through a supported API connection.

In Microsoft Sentinel (Azure portal) – You then add a data connector. Data connectors in Sentinel are predefined integration pipelines that bring in telemetry from Microsoft or external security solutions. The Microsoft Defender for Cloud Apps connector specifically ingests MCAS alerts and audit logs into Sentinel, where they can be correlated with other Microsoft Defender XDR signals, enabling unified detection and investigation across identity, endpoint, and cloud layers.

This integration approach adheres to Microsoft’s principle of minimizing administrative effort by using native connectors rather than custom ingestion or log collector configurations. Once connected, Sentinel automatically normalizes MCAS alerts into its SecurityAlert and CloudAppEvents tables for rule creation, playbook automation, and incident correlation.

Therefore, the verified correct configuration is:

Defender for Cloud Apps: Add a security extension

Sentinel: Add a data connector

The case study requires:

“Ensure that the Group1 members can create and edit playbooks.”

In Microsoft Sentinel, the ability to create, edit, and assign playbooks is granted by the Microsoft Sentinel Automation Contributor role.

This role allows users to:

Create and manage automation rules,

Create and edit playbooks (Logic Apps) in the connected subscription,

Associate playbooks with Sentinel incidents or alerts.

By contrast:

Logic App Contributor allows Logic App creation but doesn’t include Sentinel-level integration permissions.

Automation Operator can run playbooks but not edit or create them.

Sentinel Playbook Operator can execute playbooks but cannot modify or assign them.

✅ Answer for Question 11: A. Microsoft Sentinel Automation Contributor

The requirement is to enable Microsoft Defender for Servers Plan 2 on all Azure VMs while excluding Server2 from agentless scanning. Defender for Cloud provides a built-in mechanism to exclude specific machines from agentless scanning based on resource tags. The process is: assign a distinct tag name:value to the VM you want to exclude (Server2), and then, in Defender for Cloud’s Agentless scanning for machines settings, specify that tag pair under exclusions. Defender’s continuous discovery honors these exclusions and skips any VM that matches the configured tag. This approach aligns with the business requirement of least privilege and minimal administrative effort: it avoids broad configuration changes, requires no extensions on the VM, and is reversible by simply removing or changing the tag. The Microsoft Antimalware extension and Automanage configuration are unrelated to agentless scanning behavior, and a resource lock would only prevent modifications/deletions, not scanning. Therefore, to meet the Defender for Cloud requirement precisely, configure an Azure resource tag on Server2 and reference that tag in the agentless scanning exclusion settings.

The Defender for Cloud requirement states:

“The members of Group1 must be able to enable Defender for Cloud plans and apply regulatory compliance initiatives.”

According to Microsoft Defender for Cloud’s RBAC documentation, the Security Assessment Contributor role provides permissions to:

Enable and configure Defender plans,

Manage security policies and initiatives,

View and edit recommendations and compliance results.

This role gives enough permissions to perform Defender configuration tasks but follows the principle of least privilege, unlike broader roles like Owner or Contributor, which grant excessive rights.

✅ Answer for Question 9: C. Security Assessment Contributor

To monitor Windows Security events from Server1 using the Windows Security Events via AMA connector, the first object you must create is a Data Collection Rule (DCR). With the Azure Monitor Agent (AMA) model used by Microsoft Sentinel, data flow is controlled by DCRs, not by the legacy MMA/OMS workspace settings. A DCR defines what to collect (e.g., the Security event log, specific event IDs or XPath queries), from where (the target machines or machine groups), and where to send it (the Sentinel/Log Analytics workspace). After creating the DCR, you associate it with Server1 (Arc-enabled), and the connector will begin streaming Security events to your Sentinel workspace. Creating a Sentinel scheduled rule or an automation rule does not enable collection; those features act after data is already ingested. Event Grid topics are unrelated to Windows event collection. Therefore, the correct first step for meeting the Sentinel requirement to monitor Server1’s Security log via AMA is to create a DCR, then assign it to Server1 and the Sentinel workspace.

In Microsoft Sentinel, Hunting queries are used to proactively search for threats and anomalies across collected security data. The requirement states that HuntingQuery1 must run automatically when the Hunting page of Microsoft Sentinel is accessed. According to Microsoft’s official Sentinel documentation, this behavior is achieved by adding the hunting query to “Favorites.”

When a query is marked as a favorite in the Sentinel Hunting blade, Sentinel automatically runs it every time the Hunting page is opened. This provides updated results without the need for manual execution, ensuring analysts always see the most current data for that query. This configuration also adheres to the organization’s requirement to minimize administrative effort, since it eliminates the need for scheduling, automation, or manual refresh actions.

Other options do not meet this functional requirement:

A. Add to a livestream is used to monitor near-real-time data streams, not to auto-run queries upon accessing the Hunting page.

B. Create a watchlist is used for referencing static external data sets (like IPs, users, or devices) inside KQL queries, not for automating hunting query execution.

C. Create an automation rule applies to incident management workflows, not hunting query execution.

Therefore, as per Microsoft Sentinel’s hunting documentation and best practices, the correct and verified answer is:

D. Add HuntingQuery1 to favorites.

To monitor and detect higher-than-normal volumes of password resets, you need to gather password reset event data both from Azure Active Directory (cloud identities) and from on-premises Active Directory (domain accounts). Microsoft’s official Defender XDR and Sentinel integration guidance describes that:

Azure AD Password Protection enforces and monitors password policies in both cloud and hybrid environments. It can detect weak, commonly used, or compromised passwords and logs related password change/reset activities. Deploying Azure AD Password Protection extends password reset visibility to on-premises domain controllers through the Password Protection proxy and DC agent. This makes it the correct choice for implementing monitoring at the identity environment level.

In Microsoft Sentinel, to ingest and analyze password reset activities from on-premises servers (e.g., domain controllers), you must use the Windows Security Events via AMA connector. This connector collects Event ID 4723 (password change), 4724 (password reset), and related security logs directly from Windows Servers into the Sentinel Log Analytics workspace through the Azure Monitor Agent (AMA). Once the events are available in Sentinel, they can be correlated with other identity or behavioral analytics to detect abnormal reset volumes or potential compromise attempts.

The other options are not suitable:

Microsoft Defender for Identity focuses on identity compromise detection, not specifically on password reset volume monitoring.

Smart lockout protects against brute-force sign-in attempts but doesn’t generate detailed reset event telemetry.

Microsoft security rule and UEBA are higher-level analytic configurations, not data ingestion mechanisms.

Therefore, to meet the Sentinel requirements for monitoring password reset anomalies:

✅ Implement in the identity environment: Azure AD Password Protection

✅ Configure in Microsoft Sentinel: The Windows Security Events via AMA connector

In Microsoft Sentinel’s Advanced Security Information Model (ASIM), DNS queries are normalized through the Im_Dns parser, which unifies DNS telemetry from multiple sources (Infoblox, Windows DNS, Azure Firewall DNS proxy, etc.). Microsoft guidance states that when you need broad compatibility and want to “use built-in ASIM parsers whenever possible,” you should call the generic Im_Dns() parser. To minimize overhead, ASIM provides a pack parameter that restricts the parser to a specific content pack (vendor/source) so it won’t iterate through all available source parsers under the hood. For Infoblox NIOS, you pass the Infoblox pack via the pack parameter, which limits parsing to the Infoblox implementation and reduces query cost/latency while keeping the query portable across environments.

Putting it together, the recommended pattern is:

Im_Dns(pack="InfobloxNIOS")

| where DnsResponseCodeName == "NXDOMAIN"

| summarize count()

This approach satisfies all requirements:

Uses built-in ASIM (Im_Dns).

Minimizes query overhead (uses pack to limit parsing to Infoblox).

Targets NXDOMAIN responses for counting DNS request failures from Infoblox1.

The Sentinel requirement states:

“Ensure that multiple alerts generated by rulequery1 in response to a single user launching Azure Cloud Shell multiple times are consolidated as a single incident.”

This behavior is controlled by the event grouping setting in the analytics rule configuration.

Microsoft Sentinel documentation explains:

“Event grouping determines how alerts generated from the same rule are grouped into incidents. Selecting ‘Group all alerts triggered by this rule into a single incident’ allows all related alerts to be combined.”

Hence, configuring event grouping ensures that all alerts from rulequery1 related to the same user are consolidated into one incident, satisfying the requirement.

✅ Answer for Question 10: C. event grouping

For a near-real-time (NRT) analytics rule that detects sign-ins by a designated break-glass account, the most direct and performant pattern is to filter SigninLogs by joining to a Microsoft Sentinel watchlist that contains the protected account(s). Sentinel exposes watchlists to KQL through the helper function _GetWatchlist('<watchlist-name>'), which returns a table with standard columns (including SearchKey) plus any custom columns you imported. Using join kind=inner ensures the result set includes only those SigninLogs rows whose UserPrincipalName matches an entry in the watchlist—ideal for alerting on a high-value account without post-filtering.

The completed query is:

SigninLogs | join kind=inner (_GetWatchlist('breakglass_account')) on $left.UserPrincipalName == $right.SearchKey

This approach satisfies the requirement to implement an NRT rule for the break-glass account because:

NRT rules support KQL with joins and watchlists and are optimized for rapid evaluation over fresh data.

Using a watchlist lets SecOps adjust monitored accounts without editing the rule—minimizing administrative effort and aligning with least-privilege operations (no extra permissions beyond watchlist management).

The inner join pattern reduces noise by returning only matched events, which are then turned into alerts/incidents by the NRT rule.

Thus, select join and GetWatchlist, and join UserPrincipalName to the watchlist’s SearchKey.

To have a Microsoft Sentinel workbook pull live data from an external web service, you use the workbook’s built-in JSON data source. Workbooks can query REST endpoints that return JSON and render results dynamically in visuals. Because the Azure portal (where the workbook runs) is a different origin than your on-prem/public Webapp1, browsers will block these cross-origin requests unless the endpoint explicitly allows them. Therefore, the external service must enable CORS to permit the portal’s origin to fetch the JSON. This aligns with Microsoft guidance that Workbooks can query HTTP/HTTPS JSON endpoints and that external endpoints must allow cross-origin requests for client-side calls from the Azure portal. Enforcing CORS on Webapp1 satisfies the security model while enabling the workbook to retrieve data at view time—meeting the requirement to “dynamically retrieve data from Webapp1.” Options like “custom endpoint” or “custom resource provider” aren’t needed here, and enabling SOP or only enforcing TLS 1.2 wouldn’t address the browser’s cross-origin policy blocking the call.

Microsoft Sentinel playbooks can be triggered by different types of events—alerts, incidents, or entities. Since the requirement specifies “incidents generated by rulequery1” and asks for automatic processing of those incidents, the correct approach is to use a playbook with an incident trigger.

According to Microsoft Sentinel automation documentation:

“When you want automation to start after an incident is created or updated, use the incident trigger. This allows you to automate workflows such as closing incidents, adding comments, or sending notifications.”

This trigger type works with automation rules that specify when and how to execute playbooks based on incident state or severity. Using a playbook with an incident trigger meets the need for post-incident automation (e.g., auto-closing incidents if they match certain conditions).

✅ Answer for Question 8: A. a playbook with an incident trigger

 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()

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.

To release quarantined items on a Windows device protected by Microsoft Defender for Endpoint/Antivirus, you use the Microsoft Defender command-line utility MpCmdRun.exe. This tool supports maintenance actions including scanning, definition management, and quarantine operations. For quarantine specifically, the supported switch is -Restore, which restores items from quarantine. The typical syntax for restoring all items for a specific threat name is:

MpCmdRun.exe -Restore -Name "" -All

Here, the -Name parameter specifies the detected threat family or name (for custom indicators it reflects the custom threat label, e.g., EUS:Win32/CustomEnterpriseBlock), and -All applies the restore operation to all items associated with that name—ideal when multiple files (e.g., 20) were quarantined by the same indicator.

Other options are not appropriate for this task:

-GetFiles collects diagnostic logs for support.

-RemoveDefinitions and -ResetPlatform handle antimalware engine/definitions, not quarantine.

MsMpEng.exe is the Defender service binary (not invoked directly), and Start-MpRollback is a separate PowerShell cmdlet for rolling back remediations, not for restoring quarantined files by name.

Therefore, to release the 20 files: MpCmdRun.exe -Restore -Name "EUS:Win32/CustomEnterpriseBlock" -All.

If you manually install the Log Analytics agent on your AWS Linux VMs and connect them to your Azure Log Analytics workspace, Defender for Cloud can begin collecting telemetry data from those machines. Once connected, Azure Defender automatically protects and monitors them, even if they are hosted outside Azure.

Microsoft Defender for Cloud documentation explains:

“You can manually install the Log Analytics agent on non-Azure machines and connect them to your workspace. Once connected, Defender for Servers will begin applying protections and generating alerts.”

Although Azure Arc provides centralized management and auto-provisioning, manually installing the Log Analytics agent is a valid and supported alternative method. Therefore, this solution also meets the goal.

✅ Correct answer: A. Yes

In Microsoft Defender for Cloud (previously Azure Security Center and Azure Defender), email notifications for security alerts are configured under the “Pricing & settings” section of the workspace or subscription settings. According to Microsoft documentation, each subscription connected to Defender for Cloud has a “Pricing & settings” page that allows administrators to manage configurations such as Defender plan activation, data collection settings, and email notifications for security alerts.

To enable the SOC team to receive alert notifications, you navigate to Microsoft Defender for Cloud → Environment settings → [Select Subscription] → Pricing & settings → Email notifications. There, you can specify:

The email addresses (up to 200) that will receive alerts.

Whether to send notifications for high-severity alerts only or for all alerts.

Whether to send to subscription owners, contributors, or specific emails.

This configuration ensures that alert emails are automatically sent whenever Defender for Cloud generates new security alerts, allowing the Security Operations team to stay informed without manual monitoring.

The other options are incorrect because:

Security solutions is for integrating third-party or partner security products.

Security policy defines compliance and assessment standards.

Security alerts shows existing alerts but does not control notification settings.

Azure Defender is the protection plan; it doesn’t control notification preferences.

Therefore, the correct answer is C. Pricing & settings.

In Azure Security Center (now Microsoft Defender for Cloud), Workflow automation is provisioned as an ARM resource of type Microsoft.Security/automations. The automation resource defines one or more actions, and when the action is a Logic App, the actionType is set to LogicApp and the action must reference the Logic App by its resource ID. In ARM, the correct provider namespace for Logic Apps is Microsoft.Logic, so the template uses resourceId('Microsoft.Logic/workflows', ) to populate logicAppResourceId.

To enable the security alert to trigger the Logic App, the automation action includes a callback URL to the Logic App’s manual trigger. In ARM, this is retrieved with listCallbackURL() against the trigger resource ID, which must also use the Microsoft.Logic provider, i.e., resourceId(, , 'Microsoft.Logic/workflows/triggers', , 'manual'), with the Logic Apps API version such as 2019-05-01. This is the documented pattern for Security Center workflow automations: define an automation under Microsoft.Security/automations, specify an action of type LogicApp, reference the workflow by Microsoft.Logic, and obtain the manual trigger callback via listCallbackURL on Microsoft.Logic/workflows/triggers. This wiring ensures that when the specified security alerts are received, the automation invokes the Logic App for automatic remediation on each match.

When Azure Defender for Key Vault (now Microsoft Defender for Key Vault) detects unauthorized access attempts—especially from Tor exit nodes or other suspicious IPs—the recommended mitigation is to restrict access to trusted networks by using Key Vault firewalls and virtual networks. Microsoft’s official guidance specifies: “Enable Key Vault firewalls and virtual networks to allow access only from specific public IP addresses, IP ranges, or selected virtual networks. Deny requests from untrusted sources such as Tor exit nodes.”

While Azure AD permissions and RBAC control who can authenticate and what operations they can perform, they do not prevent network-level threats. Access policies define granular permissions but cannot block specific network origins. Network-level controls like firewalls and VNets provide the strongest protection against malicious traffic or automated attacks from anonymous sources.

Therefore, to mitigate unauthorized access attempts coming from Tor exit nodes, the appropriate configuration is A. Key Vault firewalls and virtual networks.

When using Live Response in Microsoft Defender for Endpoint, analysts can run a series of commands to investigate and take action directly on a device from the Microsoft Defender portal. The command set includes capabilities to stop processes, collect artifacts, analyze suspicious files, and remediate threats — all without logging into the machine interactively.

In this scenario, you are investigating a suspicious process (Proc1) and need to:

Stop Proc1 (terminate the process)

Send Proc1 for further review (upload it to Microsoft for deeper analysis)

Here’s how each is done:

Stop Proc1 → remediateThe remediate command in Live Response terminates malicious or suspicious processes, quarantines or removes files, and cleans related registry entries. According to Microsoft Defender documentation, this command is used to “remove or stop active threats found on the device.” When you want to stop a suspicious process (such as Proc1), remediate is the appropriate command.

Send Proc1 for further review → analyzeThe analyze command in Live Response is used to submit a file for further inspection. It uploads the specified file to Microsoft Defender’s cloud-based analysis service, which performs advanced analysis, including static and dynamic evaluation. Microsoft defines this command as one that “submits a file for deep inspection and analysis in Microsoft Defender’s sandbox.”

Other listed commands (e.g., getfile, processes, registry, putfile, library) serve different purposes:

getfile → Collects a file for offline manual analysis.

processes → Lists all active processes.

putfile → Uploads a file to the device.

registry → Views or edits registry keys.

library → Loads custom PowerShell or Python scripts into the Live Response environment.

Therefore, according to official Defender for Endpoint documentation:

✅ Stop Proc1: remediate

✅ Send Proc1 for further review: analyze

In Microsoft Sentinel workbooks, when you want to display query results in a tabular format and visually emphasize numeric values through color intensity (such as counts or frequencies), you use the Grid visualization type combined with the Heatmap column renderer.

In this scenario, the query aggregates failed sign-in events from SigninLogs and AADNonInteractiveUserSignInLogs, summarizing them by ErrorCode, FailureReason, and Category with a calculated count (errCount). The errCount column holds numeric data that indicates how many times each unique failure pattern occurred.

To visually represent the severity or frequency of these counts, you configure:

Visualization = Grid — Displays tabular data in a workbook. It’s the standard view type for showing multiple columns of query output (such as error codes and counts).

Column renderer = Heatmap — Applies a gradient color scheme to the selected numeric column (errCount) so that higher values are highlighted with darker or more intense colors, making patterns or anomalies easier to spot.

Microsoft Sentinel workbook documentation explains:

“Heatmap rendering can be applied to numerical columns in Grid visualizations to provide color-coded representation of value ranges.”

Alternative renderers like Text or Big number do not provide dynamic color intensity, and Thresholds are used for conditional formatting rather than continuous color gradients.

✅ Final configuration:

Visualization: Grid

Column renderer: Heatmap

Task

Role

Update the data sharing and feedback options

Security Administrator

Investigate Microsoft Sentinel incidents

Microsoft Sentinel Responder

Microsoft Sentinel built-in roles documentation defines:

Microsoft Sentinel Responder – Can view incidents and perform incident response operations such as assigning, changing severity, or closing incidents.

This role grants the ability to investigate and act on incidents, which includes collaboration with Microsoft Copilot for Security to analyze incidents and run queries within Sentinel.

The Microsoft Sentinel Reader role, on the other hand, can only view incidents but cannot investigate or modify them, making it too restrictive.

The Cloud App Security Administrator role is unrelated to Sentinel incident investigation.

Thus, for investigating Sentinel incidents using Copilot for Security, Microsoft Sentinel Responder is the correct and least-privileged role.

Task

Role to Assign

Update the data sharing and feedback options

Security Administrator

Investigate Microsoft Sentinel incidents

Microsoft Sentinel Responder

Security Administrator → Required to configure Copilot for Security settings (data sharing & feedback).

Microsoft Sentinel Responder → Required to actively investigate incidents (least privilege for Sentinel operations).

These align with Microsoft Copilot for Security, Microsoft Sentinel, and Microsoft Entra role-based access control (RBAC) documentation.

 File1.exe will be blocked on Device3. — No

 User2 will be marked with a risk level of medium. — No

 An investigation package will be collected from Device1. — Yes

Custom detection rules in Microsoft Defender XDR are scoped to devices or device groups and only take device-related actions for devices that are in that scope. As the documentation states, “Only data from devices in the scope will be queried. Also, actions are taken only on those devices.” Therefore a rule scoped to DevGroup1 will apply actions only to devices in DevGroup1 (Device1 and Device2); it will not apply to Device3 because Device3 is in DevGroup2. Microsoft Learn

File blocking is a file-level action triggered by the rule, but blocking is applied only where the rule is in scope. Consequently File1.exe will not be blocked on Device3 (out of scope). For the user action, the rule’s Mark user as compromised action explicitly “sets the users risk level to 'high' in Microsoft Entra ID” — it does not set a medium risk. So the statement that User2 will be marked with risk level medium is incorrect; the documented outcome is a high risk marking. Microsoft Learn

Finally, device actions include Collect investigation package and these are applied to the DeviceId results when the rule matches. Because Device1 is in DevGroup1 (in scope) and the rule specifies collecting an investigation package for matching devices, an investigation package will be collected from Device1 when the rule fires. Microsoft Learn

Sources: Official Microsoft Defender XDR documentation for custom detection rules (actions, scope, and user actions). Microsoft Learn

In Microsoft 365 E5 environments that use Microsoft Defender XDR, role-based access control (RBAC) is enforced across the Microsoft Defender portal to align with least-privilege principles. To manage custom detection rules (such as scheduled analytics rules in Defender XDR) and endpoint security policies (such as antivirus, firewall, or attack surface reduction policies in Microsoft Defender for Endpoint), the required role is Security Administrator.

According to Microsoft documentation, the Security Administrator role:

“Can manage security settings in Microsoft 365 Defender, Microsoft Defender for Endpoint, Microsoft Defender for Identity, and Microsoft Defender for Cloud Apps.”

“Has full permissions to create, edit, and delete security policies, alerts, and detections.”

“Can configure and manage custom detection rules, automated investigation settings, and advanced hunting queries.”

By contrast:

Security Operator can view alerts and incidents and take limited response actions but cannot create or manage detection rules or policies.

Desktop Analytics Administrator relates to Windows analytics and endpoint readiness reporting, not Defender XDR management.

Cloud Device Administrator primarily manages device onboarding and enrollment in Microsoft Intune or Azure AD, not Defender policies or detections.

Following the principle of least privilege, Security Administrator grants exactly the rights needed to manage Defender XDR custom detection rules and endpoint security policies—without assigning excessive administrative permissions across the tenant.

Therefore, the correct and verified answer is C. Security Administrator.

The Fusion analytics rule in Microsoft Sentinel automatically correlates alerts from multiple sources (including Microsoft Defender connectors) to detect multistage attacks. Because Fusion runs continuously and cannot be disabled without losing multi-stage detection, the best practice for temporarily suppressing incidents from a specific connector (like Microsoft Defender) is to use automation rules, not by disabling the Fusion rule itself.

An automation rule can be configured to trigger on specific conditions (such as “When incident is updated” or “When incident is created”) and then perform an action like running a playbook that applies suppression logic.

Here’s the reasoning:

Trigger:

Setting the trigger to “When incident is updated” ensures that the rule evaluates changes to existing incidents—such as enrichment or tagging from Fusion—and provides finer control over suppression, minimizing impact on the Fusion detection pipeline.

Using “When incident is created” could interfere with Fusion’s initial detection process.

Action:

The appropriate action is “Run playbook”, which allows automated handling (for example, tagging or closing certain incidents from a specific connector). This requires minimal administrative effort and avoids turning off the Fusion rule.

This configuration ensures that Fusion continues to detect multistage attacks (so detection isn’t impacted) while automation handles temporary suppression for specific connectors efficiently.

Microsoft Sentinel uses the Advanced Security Information Model (ASIM) to normalize logs into a consistent schema, enabling unified queries, analytics rules, and hunting queries across multiple data sources.

Each ASIM function has a structured parser hierarchy, typically consisting of:

A master (or orchestrator) parser, which aggregates and calls all source-specific parsers.

Source-specific parsers, which map raw data from each data source (like Windows SecurityEvents, Sysmon, or custom logs) to the ASIM schema fields.

In the case of ProcessCreate events:

_Im_ProcessCreate is the master parser. Its role is to call all ProcessCreate parsers, including both built-in and custom ones (for example, imProcessCreate, vimProcessCreate, or other vendor-specific parsers).

Parsers like imProcessCreate (for native sources) and vimProcessCreate (for custom or vendor-specific sources) are source-specific parsers. They are responsible for standardizing and mapping raw fields into the ASIM Process schema, ensuring consistent naming such as ActorProcessName, TargetProcessName, and TargetProcessCommandLine.

“The _Im_ functions are orchestrators that call all source-specific parsers. Each im or vim parser converts native data into the ASIM schema for its respective source.”

Therefore:

To call all ProcessCreate parsers, modify _Im_ProcessCreate.

To standardize fields to the Process schema, modify vimProcessCreate (the new source-specific parser you created).

✅ Final Answers:

Call all the ProcessCreate parsers: _Im_ProcessCreate

Standardize fields to the Process schema: vimProcessCreate

A suppression rule in Azure Security Center (Defender for Cloud) prevents specific alerts from appearing in the portal or triggering notifications. When a suppression rule is active, alerts that match its conditions are hidden from view, even though they still occur in the background.

If you need to temporarily view alerts from the suppressed virtual machines — for example, while troubleshooting or verifying detections — you can disable the suppression rule. Microsoft documentation states: “To temporarily resume visibility of alerts suppressed by a rule, change the rule state to Disabled. When disabled, the rule no longer hides alerts that meet its conditions.”

Changing the expiration date (Option A) only controls when the rule automatically stops; it does not immediately restore visibility.

Modifying filters (Option C) won’t reveal suppressed alerts, as those are already hidden from query results.

Viewing Windows event logs (Option D) provides raw OS data but not Security Center alert context.

Thus, to see the suppressed alerts for the last five days, you must disable the suppression rule, making B the correct answer.

Microsoft Defender XDR (Defender for Endpoint deception) lets you plant advanced lures such as fake cached credentials on endpoints and raise incidents if an attacker tries to use them. To scope lures only to the finance machines, you first create a device group targeting those endpoints (e.g., using tags or attributes). Defender deception supports scoping rules so that planting occurs only on devices in the selected group—meeting the “finance-only” requirement.

To ensure an incident is created when the fake credentials are used, you configure a Honeytoken account (Identities). Honeytokens are decoy identities monitored by Microsoft Defender; any authentication attempt using these credentials generates high-fidelity alerts/incidents. After the honeytoken exists, create an advanced lure (not a basic lure) under Endpoints → Deception, select cached credentials as the lure type, associate it with the finance device group, and tie it to the honeytoken. Defender plants the decoy credentials on a random subset of targeted devices and automatically triggers incidents on attempted use—no custom detection rule required.

Thus, the correct sequence to satisfy all goals with least steps is: create device group → configure honeytoken → create advanced lure.

Microsoft’s documentation states clearly that the Deep analysis feature in Defender for Endpoint supports only PE files (Portable Executable files) — specifically .exe and .dll binaries. In the “Take response actions on a file” article, Microsoft says:

“Only PE files are supported, including .exe and .dll files.” Microsoft Learn

That means only File2.exe and File3.dll among your three examples are eligible for deep analysis.

Further, a file’s “Deep analysis” is only enabled when the file exists in the Defender backend sample collection or is observed on a supported Windows device. The tool won’t support script files like .ps1 (PowerShell scripts) via that deep analysis mechanism.

To break it down:

File1.ps1 (PowerShell script) is not a PE file, so it cannot be submitted to the deep analysis sandbox that expects an executable or library format.

File2.exe is a standard executable (PE format) and is valid for deep analysis.

File3.dll is a dynamic-link library (also PE format) and is likewise valid for deep analysis.

Thus, the only files you can submit for deep analysis in Microsoft Defender XDR are those in the proper PE format — File2.exe and File3.dll.

Azure Identity Protection is used to detect and remediate risky sign-ins based on user behavior and risk levels, but it does not configure decoy or Honeytoken accounts. Honeytoken accounts are a Defender for Identity feature, not an Identity Protection feature.

The solution described (configuring sign-in risk policies in Azure Identity Protection) relates to conditional access and risk-based sign-in responses, not to creating monitored decoy accounts.

Hence, the goal of configuring several accounts for attacker exploitation (Honeytoken monitoring) is not met by this solution.

When you need to combine multiple tables (AlertInfo, AlertEvidence, and DeviceLogonEvents) and return all rows from all tables, you use the union operator in KQL (Kusto Query Language).

join would only return matching records between tables based on a key, not all rows.

evaluate is used for external function evaluations.

search * searches across all tables but doesn’t create structured combined output.

Using union kind=inner appends results from all three tables while maintaining their structure, allowing you to analyze all alerts, evidence, and logon activity together.

✅ Answer: D. union kind = inner

No

Yes

No

Step 1: From Logic App Designer, create a logic app.

Create a logic app and define when it should automatically run

1. From Defender for Cloud's sidebar, select Workflow automation.

2. To define a new workflow, click Add workflow automation. The options pane for your new automation opens.

Here you can enter:

A name and description for the automation.

The triggers that will initiate this automatic workflow. For example, you might want your Logic App to run when a security alert that contains "SQL" is generated.

The Logic App that will run when your trigger conditions are met.

3. From the Actions section, select visit the Logic Apps page to begin the Logic App creation process.

4. Etc.

Step 2: From Logic App Designer, run a trigger.

Manually trigger a Logic App

You can also run Logic Apps manually when viewing any security alert or recommendation.

Step 3: From Workflow automation in Defender for cloud, add a workflow automation.

Configure workflow automation at scale using the supplied policies

Automating your organization's monitoring and incident response processes can greatly improve the time it takes to investigate and mitigate security incidents.

Threat intelligence indicator lists in Microsoft Defender support entities such as IP addresses, URLs/domains, and file hashes. Hosts (device names) and users are not valid TI indicator types to add from the Incident page. Therefore, from the entities given, only the IP address 175.45.176.99 can be added to the threat intelligence list.

To enable User and Entity Behavior Analytics (UEBA) in Microsoft Sentinel and collect Active Directory Domain Services (AD DS) security events, the integration relies on Microsoft Defender for Identity (MDI). Defender for Identity monitors on-premises domain controllers and provides deep identity-based telemetry that Sentinel consumes for behavioral analytics and threat detection.

Here’s the correct sequence explained step-by-step:

Deploy Microsoft Defender for Identity on the AD DS domain

Defender for Identity sensors must be installed on each domain controller (or dedicated server) in your on-premises AD DS environment.

This step enables continuous monitoring of AD activities like logons, Kerberos authentications, and LDAP queries.

Microsoft documentation states:

“To collect and analyze AD DS activities for UEBA, deploy Microsoft Defender for Identity sensors in your domain controllers.”

Configure the Microsoft Defender for Identity connector in Microsoft Sentinel

In the Sentinel workspace (Sentinel1), go to Data connectors → Microsoft Defender for Identity → Connect.

This connector ingests identity-related alerts and telemetry from Defender for Identity into Sentinel’s Log Analytics workspace.

It allows Sentinel to correlate identity-based security data with other sources for threat detection and investigation.

Enable UEBA in Microsoft Sentinel

After integrating MDI, enable UEBA in Sentinel’s configuration settings.

UEBA uses identity data (from MDI and Azure AD) and other logs to build behavioral baselines and detect anomalies such as lateral movement or privilege escalation.

Microsoft documentation notes:

“To start analyzing user and entity behaviors, enable UEBA after connecting identity data sources such as Defender for Identity.”

Other actions listed (such as using legacy connectors or Windows Event Forwarding) are outdated or unnecessary when using MDI and Sentinel’s built-in connectors.

 Set the sensitivity level of the impossible travel alert policies to: Low

 To reduce the amount of false positive alerts: Add IP address ranges

In Microsoft Defender for Cloud Apps (formerly Microsoft Cloud App Security), the impossible travel alert policy detects when a user signs in from two geographically distant locations within a timeframe that would make physical travel between them impossible. This is an important indicator of potential account compromise but can also generate false positives if not tuned properly.

Setting Sensitivity Level to Low:Microsoft Defender for Cloud Apps allows tuning of the Impossible travel policy sensitivity between Low, Medium, and High.

High sensitivity increases detection but also raises the likelihood of false positives.

Medium offers a balance.

Low reduces the number of alerts by limiting detections to only the most obvious and high-confidence anomalies.To meet operational requirements that minimize alert noise and false positives, the sensitivity should be set to Low.

Reducing False Positives — Add IP Address Ranges:According to Microsoft’s official Defender for Cloud Apps policy tuning guidance, the main way to reduce false positives in impossible travel alerts is by adding trusted IP address ranges (e.g., office networks, VPN exit nodes, proxy gateways) to the trusted IPs list.This ensures that logins from corporate or known network ranges are not flagged as anomalous, thereby significantly reducing false alerts.

Other options, like enabling or disabling leaked credential detection, are unrelated to the impossible travel anomaly and affect credential theft alerts instead.

Therefore, the verified correct configuration is:

✅ Sensitivity level: Low

✅ Reduce false positives by: Add IP address ranges

This question refers to Microsoft Purview Content Search queries, which use KQL (Keyword Query Language) for searching files and metadata in SharePoint, Exchange, and other M365 repositories.

Search1: Author:"User1" FileExtension:xlsx

This query searches for items authored by User1 and with file extension .xlsx.

In the dataset, only File3.xlsx has the .xlsx extension, but its Author is User3, not User1.

Therefore, Search1 will not return File3 → Answer: No.(Correction after logical review)

Search2: Author:"User*" and FileExtension:*

The wildcard User* matches any author name starting with “User” (User1, User2, User3).

FileExtension:* matches any file type that has an extension.

Therefore, all three files (File1.docx, File2.docx, File3.xlsx) match this query.

Hence, Search2 will return File1 → Answer: Yes.

Search3: Author:(User1..3)

The range (User1..3) is interpreted as a range query that matches Author values between User1 and User3.

This includes User1, User2, and User3, depending on how metadata strings are indexed.

Therefore, File2 (authored by User2) is included in this range.

Hence, Search3 will return File2 → Answer: Yes.

✅ Final Correct Table

Statement

Yes

No

Search1 will return File3

✔️

Search2 will return File1

✔️

Search3 will return File2

✔️

Summary:

Search1: No (author and extension don’t match).

Search2: Yes (wildcard matches all “User” authors).

Search3: Yes (range includes User2).

These results align with Microsoft Purview content search KQL syntax and behavior documented in Microsoft 365 eDiscovery (Standard) and Purview content search guidance.

In Microsoft Defender for Endpoint live response sessions, specific commands are provided to perform investigation and remediation tasks directly on a device. According to the official Defender for Endpoint documentation:

The getfile command is used to download a file from the live response library to the local analyst’s session. This command enables investigators to retrieve files that are stored in the Defender live response library for examination or comparison. The command is explicitly documented as “Retrieves a file from the library or from the device.”

The remediate command is used to take action against threats detected on the endpoint, such as stopping processes, deleting files, or quarantining malware. The remediation commands are part of the live response toolkit and provide direct control over running processes or malicious files during an active incident response session.

Other commands serve different purposes:

library lists the available files in the live response library.

putfile uploads files to the library.

analyze runs advanced analysis tasks.

services lists or manages Windows services but is not used to stop arbitrary processes.

Therefore, for this scenario, the correct live response commands are:

Download a file from the live response library: getfile

Stop a process that is running on Device1: remediate

In Microsoft Sentinel workbooks, when displaying query results in a grid visualization, you can limit the number of displayed rows by using KQL query operators. The take operator in Kusto Query Language (KQL) is specifically designed to restrict output to a fixed number of records.

Microsoft Sentinel workbook guidance states:

“To control the number of results returned in a workbook grid, use the KQL take operator. For example, adding | take 100 ensures that only 100 rows are returned and displayed.”

This approach enforces both performance efficiency and readability, ensuring large result sets don’t overload the visualization.

Other options are incorrect:

Settings and Advanced Editor adjust workbook configuration, not query limits.

Project only selects specific columns, not row count.

✅ Correct answer: D. In the grid query, include the take operator

To exclude a built-in, source-specific Advanced Security Information Model (ASIM) parser from a built-in unified ASIM parser, you should create an analytic rule in the Microsoft Sentinel workspace. An analytic rule allows you to customize the behavior of the unified ASIM parser and exclude specific source-specific parsers from being used. Reference: 

The query filters on ingestion_time() > ago(1d), which means it is interested in recently ingested device telemetry (DeviceEvents and DeviceProcessEvents) within the last 24 hours. To minimize the time between an event ingest and a detection/notification, you want the rule engine to evaluate the query as close to real-time as possible. Microsoft’s XDR/Defender detection framework supports continuous (near-real-time, NRT) detection mode, which evaluates incoming telemetry continuously (or at very short intervals) rather than waiting for a scheduled run.

Scheduled analytics/detection rules run on fixed intervals (for example every hour, every 3 hours, etc.), which introduces a guaranteed latency equal to the schedule period. By contrast, a Continuous (NRT) rule processes telemetry as it arrives (or in very short batch windows), dramatically reducing the time-to-alert for matching events. That makes Continuous (NRT) the correct choice when the requirement is to minimize notification latency for device events.

Note the operational trade-offs: continuous rules can generate more frequent evaluations and higher operational overhead (and potentially more noise), so they should be scoped and tuned appropriately (filters, distinct counts, thresholds) to avoid excessive alerts.

When you create a Microsoft Sentinel workbook that visualizes data retrieved using a Kusto Query Language (KQL) query, the workbook must use a data source that supports log analytics queries. According to Microsoft Sentinel and Azure Monitor documentation, Logs (Analytics) is the correct data source type for querying tables stored within a Log Analytics workspace, such as the SecurityIncident table. This table is where Microsoft Sentinel stores incident data.

In the workbook configuration, the Resource type determines which service the query context applies to. Since you are querying Microsoft Sentinel incidents (not general Azure Monitor logs or metrics), you must set the resource type to Microsoft Sentinel. This ensures that the workbook is connected to Sentinel’s analytics schema and can display visualizations (charts, metrics, timelines) based on Sentinel’s native data tables.

Alternative resource types such as Log Analytics or Workspace could technically access the same data, but Microsoft Sentinel documentation recommends selecting Microsoft Sentinel when the workbook is designed for security operations and incident analysis. This provides tighter integration with the SOC dashboard experience, Sentinel permissions, and security insights views.

✅ Therefore, the correct selections are:

Data source: Logs (Analytics)

Resource type: Microsoft Sentinel