SC-200 Microsoft Security Operations Analyst Questions and Answers

msticpy is a library for InfoSec investigation and hunting in Jupyter Notebooks. It includes functionality to: query log data from multiple sources. enrich the data with Threat Intelligence, geolocations and Azure resource data. extract Indicators of Activity (IoA) from logs and unpack encoded data.

MSTICPy reduces the amount of code that customers need to write for Microsoft Sentinel, and provides:

Data query capabilities, against Microsoft Sentinel tables, Microsoft Defender for Endpoint, Splunk, and other data sources.

Threat intelligence lookups with TI providers, such as VirusTotal and AlienVault OTX.

Enrichment functions like geolocation of IP addresses, Indicator of Compromise (IoC) extraction, and WhoIs lookups.

Visualization tools using event timelines, process trees, and geo mapping.

Advanced analyses, such as time series decomposition, anomaly detection, and clustering.

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

Defender for Cloud alerts include a kill chain intent field that maps to MITRE ATT&CK tactics (for example, PrivilegeEscalation, Persistence, CredentialAccess). In the alert JSON this is exposed as intent; searching that key lets you filter for alerts where the tactic is Privilege Escalation.

Applying “quick fix” remediations from Azure Defender (Microsoft Defender for Cloud) changes the underlying resources (VMs). Beyond the Security Admin role (which grants security policy/manage permissions), the user needs write permissions on the target resources. To follow least privilege, grant Contributor on RG1 (the resource group containing the 100 VMs) so SecAdmin1 can remediate only those resources—rather than the entire subscription (over-privilege) or Owner (unnecessary). Security Reader is read-only and cannot apply fixes.

To summarize security events over the last week and chart them by day, use KQL time binning on the event timestamp. In Sentinel/Log Analytics, bin() groups records into fixed time buckets on a datetime column—here, TimeGenerated. Pair that with a time filter for the past 7 days and render as a timechart. The key pattern is:

SecurityEvent

| where TimeGenerated >= ago(7d)

| summarize Count = count() by bin(TimeGenerated, 1d)

| render timechart

bin is the correct aggregator for time-based bucketing.

TimeGenerated is the standard timestamp column used across Sentinel tables for ingestion time.

Using a 1-day bin shows the daily counts; the where TimeGenerated >= ago(7d) limits results to the past week.

render timechart visualizes the grouped counts over time.

In the answer area shown, you select bin and TimeGenerated; (the full query would also include the where line and a 1d bin size to meet the “by day” requirement).

When designing a Microsoft Sentinel workspace, cost optimization and data retention management are two key considerations. Microsoft Sentinel stores data in an Azure Log Analytics workspace, and pricing for data ingestion and retention is managed through Log Analytics settings.

Minimize costs for daily ingested data:Microsoft’s documentation on Log Analytics pricing models states that you can choose between Pay-As-You-Go (PAYG) and Commitment Tiers. The Commitment Tier model allows you to commit to a fixed amount of daily ingestion (for example, 20 GB/day in this case) at a lower per-GB cost compared to PAYG pricing. If your ingestion volume is predictable (as in this scenario—20 GB per day), this model provides significant cost savings without the administrative overhead of managing caps or throttling.Therefore, to minimize ingestion cost, the correct choice is “Use a commitment tier.”

Maximize the data retention period without incurring extra costs:By default, Microsoft Sentinel (via Log Analytics) provides 90 days of data retention at no additional charge. Extending retention beyond 90 days incurs additional storage charges. According to Microsoft’s official guidance, “Log Analytics retains data for 90 days at no cost; data kept beyond that period is billed at the retention rate.”Therefore, to maximize the free retention period while avoiding extra cost, the correct configuration is “Set retention to 90 days.”

Summary:

Minimize costs for daily ingested data → Use a commitment tier

Maximize retention without extra costs → Set retention to 90 days

This configuration ensures both cost efficiency and maximum free data availability, aligning with Microsoft Security Operations (SecOps) and Sentinel best practices.

In Microsoft Sentinel, data from applications, security solutions, and services is ingested through data connectors. To integrate App1 with Sentinel and meet its monitoring or data ingestion requirements, you must configure a connector.

Microsoft Sentinel documentation specifies:

“Data connectors provide the integration point between Microsoft Sentinel and other data sources, including Microsoft services, third-party solutions, and custom applications.”

Connectors manage authentication, data formats, and continuous ingestion of logs or alerts into Sentinel’s Log Analytics workspace.

Other options:

API connection – supports Logic Apps but not direct Sentinel ingestion.

Trigger – used for playbooks or automation, not data ingestion.

Authorization – a setting used within connectors or playbooks, not configured separately.

✅ Correct configuration for App1: a connector

To collect event logs from Linux virtual machines in Microsoft Sentinel, the data ingestion path must be correctly configured through Azure Monitor (Log Analytics) and Sentinel connectors. The correct order of actions is as follows:

1️⃣ Add Microsoft Sentinel to a workspace:

Sentinel requires a Log Analytics workspace as its data foundation. You must first enable Microsoft Sentinel on a workspace by selecting Microsoft Sentinel → Add → Select workspace. This step prepares the workspace to receive and analyze security data.

2️⃣ Add a Syslog connector to the workspace:

Linux systems send event data through Syslog. You must enable the Syslog connector within the Sentinel workspace. The connector defines which Syslog facilities and severities should be collected and ingested into Sentinel. It acts as the integration bridge between the Linux hosts and Sentinel’s analytics engine.

3️⃣ Install the Log Analytics agent for Linux on the virtual machines:

To forward the logs, each Linux virtual machine needs the Log Analytics agent (OMS agent). This agent collects the configured Syslog and performance data and sends it to the connected Log Analytics workspace.

This sequence ensures proper setup for Linux log ingestion: Sentinel is first activated, then the Syslog data source is configured, and finally, agents are deployed to gather and transmit the logs.

Use livestream to run a specific query constantly, presenting results as they come in.

According to Microsoft Purview and Kusto Query Language (KQL) documentation, when you need to filter audit or activity logs based on a specific date range, the correct approach is to use the between (datetime()..datetime()) operator. This operator allows defining a start and end datetime for filtering events. The AuditLogs table is the appropriate data source for searching Microsoft 365 audit events, including file activities in SharePoint and Teams sites.

In this scenario, the goal is to identify all files related to “Project1” stored on the Team1 site between February 1, 2023, and February 10, 2023. The where clause is used to filter the results first by Timestamp (activity date/time) and then by FileName to find the relevant project files.

The correct syntax aligns exactly with the Microsoft KQL documentation pattern:

| where Timestamp between (datetime(StartDate)..datetime(EndDate))

This ensures that only audit records falling within that time window are retrieved.

Other options are incorrect because they either lack valid KQL structure or use unsupported syntax for date ranges. Thus, Option D is the verified answer.

To minimize administrative effort in responding to incidents and remediating security threats in Microsoft Sentinel, you should use the platform’s built-in automation and orchestration capabilities — specifically Automation Rules and Playbooks.

Microsoft Sentinel Automation Rules (Option C):

Automation rules in Sentinel allow you to automate incident management tasks such as assigning owners, changing severity, tagging, or automatically running playbooks when an incident or alert is created.

They help standardize responses across similar alerts, significantly reducing manual intervention.

Microsoft documentation states:

“Automation rules simplify the management of playbook triggers and incident handling by allowing you to define actions that automatically occur when incidents are created or updated.”

Microsoft Sentinel Playbooks (Option D):

Playbooks are Logic App–based workflows that automate responses to security alerts or incidents.

They can perform remediation actions such as disabling compromised accounts, isolating infected devices, blocking IP addresses, or sending notifications to SOC teams.

You can link playbooks directly to analytics rules or call them through automation rules for end-to-end automation.

Incorrect Options:

A. Bookmarks are used for hunting investigations, not for automation or remediation.

B. Azure Automation runbooks can be used for specific administrative scripts but require more manual setup and integration — not the most efficient choice for Sentinel’s automated workflows.

E. Azure Functions apps are custom code execution environments; while powerful, they are not the primary tool for Sentinel’s automated response.

The goal is to automatically execute the Azure Logic App (app1), which is known as a Playbook in Microsoft Sentinel, in response to a newly created alert.

The Playbook (Logic App): An Azure Logic App used for security response in Microsoft Sentinel is called a Playbook. It is a workflow that must start with a trigger, typically the Microsoft Sentinel incident or Microsoft Sentinel alert trigger.

The Trigger Mechanism (Automation Rule): To run a playbook automatically when an alert or incident is created, you must use a Microsoft Sentinel Automation Rule.

Required Flow: The Logic App (app1) acts as the action to be performed, but it must be called by an automation component. The recommended and modern approach for linking automated actions (playbooks) to alerts and incidents in Microsoft Sentinel is via Automation Rules.

The sequence of operations is:

The Azure AD Connector ingests data into the Microsoft Sentinel workspace.

A corresponding Analytics Rule (Option C) detects a threat in that data and generates an Alert. This alert usually leads to the creation of an Incident.

The Automation Rule (Option D) is configured to:

Trigger: When an incident is created (or when an alert is created).

Condition: Filter for the specific alert or incident (e.g., where the Analytic Rule Name is the one that detects the Azure AD threat).

Action: Select the Run playbook action and specify app1.

While an Analytics Rule (C) generates the initial alert, the Automation Rule (D) is the specific component that takes that alert/incident as input and performs the action of launching the Logic App (playbook) automatically. The ability to invoke playbooks directly from Analytics Rules (the "Alert automation (classic)" method) is deprecated in favor of using Automation Rules, making the Automation Rule the correct and contemporary first step for this requirement.

Box 1: AzureActivity

The AzureActivity table includes data from many services, including Microsoft Sentinel. To filter in only data from Microsoft Sentinel, start your query with the following code:

Box 2: autocluster()

Example: description: |

'Listing of storage keys is an interesting operation in Azure which might expose additional

secrets and PII to callers as well as granting access to VMs. While there are many benign operations of this

type, it would be interesting to see if the account performing this activity or the source IP address from

which it is being done is anomalous.

The query below generates known clusters of ip address per caller, notice that users which only had single

operations do not appear in this list as we cannot learn from it their normal activity (only based on a single

event). The activities for listing storage account keys is correlated with this learned

clusters of expected activities and activity which is not expected is returned.'

AzureActivity

| where OperationNameValue =~ "microsoft.storage/storageaccounts/listkeys/action"

| where ActivityStatusValue == "Succeeded"

| join kind= inner (

AzureActivity

| where OperationNameValue =~ "microsoft.storage/storageaccounts/listkeys/action"

| where ActivityStatusValue == "Succeeded"

| project ExpectedIpAddress=CallerIpAddress, Caller

| evaluate autocluster()

) on Caller

| where CallerIpAddress != ExpectedIpAddress

| summarize StartTime = min(TimeGenerated), EndTime = max(TimeGenerated), ResourceIds = make_set(ResourceId), ResourceIdCount = dcount(ResourceId) by OperationNameValue, Caller, CallerIpAddress

| extend timestamp = StartTime, AccountCustomEntity = Caller, IPCustomEntity = CallerIpAddress

In Microsoft Defender for Cloud Apps (formerly Microsoft Cloud App Security), the impossible travel and sign-in from risky IP addresses anomaly detection policies automatically analyze user activity patterns and compare sign-in locations to detect suspicious behavior. However, when legitimate corporate IPs are not correctly identified as trusted, these policies may generate excessive false positives.

According to Microsoft’s official documentation on managing IP address ranges:

“To reduce false positive alerts from trusted network locations, define your organization’s known IP address ranges in the Defender for Cloud Apps portal. Tagging these as Corporate ensures that sign-ins originating from these IPs are treated as safe and excluded from anomaly detection alerts.”

To implement this properly:

Add the IP addresses to the Corporate address range category (Option B) — This explicitly identifies these ranges as trusted corporate networks. Once defined, Microsoft Cloud App Security (MCAS) automatically suppresses anomaly alerts (like impossible travel or risky IP alerts) from these known sources.

Override automatic data enrichment (Option A) — Automatic data enrichment uses Microsoft’s threat intelligence and geolocation services to classify IPs. When you override it, the system respects your manual classification (Corporate, VPN, Risky, etc.) rather than reclassifying based on Microsoft’s enrichment data. This ensures that your defined corporate IPs remain categorized correctly, avoiding repeated alerting on known legitimate sign-ins.

The other options are not appropriate:

C. Increase sensitivity level would actually make alerts even more frequent rather than reduce them.

D. Add IPs to “other” category does not stop alerts; only the Corporate category suppresses impossible travel alerts.

E. Activity policy exclusion is not used for anomaly detection tuning; it applies to specific custom activity conditions.

Therefore, the correct configuration to suppress legitimate corporate alerts and follow best practice for false positive reduction is to override automatic data enrichment (A) and add corporate IPs to the Corporate address range category (B).

In Microsoft Defender for Cloud, regulatory compliance standards such as PCI DSS 4.0, ISO 27001, and NIST SP 800-53 are part of the Cloud Security Posture Management (CSPM) capabilities. To assign or view these regulatory initiatives, the CSPM plan must first be enabled for the environment.

According to Microsoft Defender for Cloud documentation, when you open Environment settings → Security policy, you can view and manage the assigned initiatives. If the option to “Add more industry and regulatory standards” is grayed out or unavailable, it means that the CSPM plan is not active for that subscription.

Once you enable the Defender CSPM plan, Defender for Cloud automatically assigns the Microsoft Cloud Security Benchmark (MCSB) initiative and allows you to add additional frameworks such as PCI DSS 4.0, NIST, or SOC 2.

Option A (Correct) — Enabling CSPM unlocks regulatory compliance capabilities, allowing you to assign the PCI DSS 4.0 initiative.

Option B — Disabling MCSB is unnecessary and not required; it’s automatically included when CSPM is enabled.

Options C and D — Continuous export settings (to Event Hubs or Log Analytics) are used for exporting data, not enabling compliance initiatives.

Hence, the first step to make the “Add more standards” option available is to enable the Cloud Security Posture Management (CSPM) plan on the subscription.

In Microsoft 365 Defender, Advanced Hunting is the dedicated feature for creating and managing custom Kusto Query Language (KQL) queries used to proactively detect threats, investigate suspicious activity, and track ongoing risks across Microsoft 365 services (including Defender for Endpoint, Defender for Identity, Defender for Cloud Apps, and Defender for Office 365).

A custom tracked query is a saved hunting query that continuously assesses your environment’s threat status over time. According to Microsoft documentation, you create, edit, and manage these queries under the “Advanced Hunting” page in the Microsoft 365 Defender portal. The tracked queries appear under the “Custom detection rules” or “Tracked queries” tabs within this section, depending on their purpose.

Here’s how it works:

Navigate to Microsoft 365 Defender portal → Hunting → Advanced Hunting.

Use KQL to define your custom query that identifies specific threat patterns or anomalies.

Save it as a tracked query, allowing Microsoft 365 Defender to run it regularly and surface ongoing results in the hunting dashboard.

Other options are not correct:

Policies & rules – used for configuration of alerting and security policies, not ad-hoc or proactive hunting.

Explorer – provides real-time investigation of emails and threats but doesn’t support custom KQL queries.

Threat analytics – offers intelligence-driven insights and reports but doesn’t allow creating custom queries.

✅ Correct Answer: D. Advanced Hunting

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.

According to Microsoft Defender for Cloud automation documentation, Logic Apps can be integrated to automatically respond to recommendations or alerts. To test or automate remediation from Defender for Cloud, you must configure an automation workflow (Logic App) that triggers when a recommendation is created or updated.

Microsoft defines the available triggers for Defender for Cloud automation as follows:

When a Defender for Cloud Recommendation is created or triggered – This event type initiates the Logic App whenever a new security recommendation appears or an existing one changes state. It’s the proper trigger for remediation scenarios because recommendations typically indicate a detected security misconfiguration or risk requiring action.

When a Defender for Cloud Alert is created or triggered – This applies to security alerts, not recommendations.

When a response to a Defender for Cloud alert is triggered – Used for incident-response workflows, not for testing remediation logic.

In the context of the question, the goal is to test automatic remediation for detected configuration issues (security risks). Those are surfaced as recommendations within Defender for Cloud, not as alerts.

Next, the execution source should be configured under:

Trigger the execution of LA1 from: Recommendations

This ensures that Defender for Cloud will execute the Logic App (LA1) automatically when relevant recommendations are triggered, allowing immediate testing and validation of the remediation logic.

In summary:

To test the Logic App remediation workflow in Defender for Cloud, you must:

Set the trigger type to “When a Defender for Cloud Recommendation is created or triggered”.

Configure it to execute from “Recommendations”.

Thus, the verified correct answers are:

✅ Set the LA1 trigger to: When a Defender for Cloud Recommendation is created or triggered

✅ Trigger the execution of LA1 from: Recommendations

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 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 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.”

To determine if ZAP moved your message, you can use either the Threat Protection Status report or Threat Explorer (and real-time detections).

 To the AD DS domain controllers, deploy: Microsoft Defender for Identity sensors

 For Sentinel1, configure: The Security Events data source

To enable User and Entity Behavior Analytics (UEBA) in Microsoft Sentinel using data from on-premises Active Directory Domain Services (AD DS), you must collect detailed identity and authentication events. This requires integration with Microsoft Defender for Identity (MDI) — the native tool designed to monitor domain controller traffic and send identity telemetry to Microsoft Sentinel.

According to Microsoft documentation:

“To collect signals from your on-premises Active Directory domain controllers, install Microsoft Defender for Identity sensors directly on each domain controller or on a dedicated server.”

These sensors capture Windows event logs and network authentication data (Kerberos, NTLM, LDAP) and send the information securely to Defender for Identity cloud service.

The Azure Connected Machine agent and Azure Monitor agent do not provide the specialized identity telemetry required by UEBA. Therefore, the correct deployment is the Microsoft Defender for Identity sensors.

After integrating Defender for Identity, you must connect the right data source in Microsoft Sentinel.

The Security Events data source ingests domain controller event logs (such as sign-ins, account management, and group membership changes) that UEBA relies on for behavioral baselines and anomaly detection.

Microsoft Sentinel UEBA documentation confirms:

“UEBA uses data from identity-related data sources, such as SecurityEvents and Azure Active Directory sign-ins, to build user and entity profiles and detect anomalies.”

The Audit Logs and Signin Logs tables are specific to Azure AD and cloud identity events — not on-premises AD DS domain controllers.

✅ Final Configuration:

Deploy Microsoft Defender for Identity sensors to the domain controllers.

Configure The Security Events data source for Sentinel1.

To integrate on-premises servers into Microsoft Defender for Cloud and support features such as Threat and Vulnerability Management (TVM) and data collection rules, the servers must first be connected to Azure Arc. Azure Arc allows non-Azure servers to be managed as Azure resources and enables Defender for Cloud capabilities such as endpoint protection, vulnerability management, and compliance assessment.

The correct sequence of steps is:

1️⃣ Generate the installation script:

In the Azure portal, navigate to Azure Arc → Servers → Add. From there, select the appropriate subscription and resource group, then generate the Azure Arc onboarding script. This script includes registration commands and configuration for the machine to connect to Azure.

2️⃣ Install the Azure Connected Machine agent:

On each on-premises server, run the generated script. This installs the Azure Connected Machine agent (Azure Arc agent), which establishes communication between the on-premises server and Azure. After installation, the server appears as an Arc-enabled machine in the Azure portal.

3️⃣ Create an Azure Arc Data Controller (if data services are required):

Once the machines are connected, you can configure data services or additional Defender for Cloud features (such as data collection and TVM). The Azure Arc Data Controller provides hybrid data capabilities for managing and monitoring on-premises workloads.

This sequence aligns with Microsoft’s Defender for Cloud documentation and Arc onboarding best practices, ensuring minimal administrative overhead while enabling full SecOps visibility across hybrid environments.

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 view incidents across multiple Sentinel workspaces (50 in this case), the central view is provided from the Microsoft Sentinel page in the Azure portal.

This page provides a multi-workspace incident view, allowing SOC analysts to see all incidents across all connected workspaces without switching manually.

Microsoft Sentinel – Incidents → shows incidents from a single workspace only.

Microsoft Sentinel – Workbooks → used for analytics visualization.

Log Analytics workspaces → only for log storage and queries, not consolidated incident management.

✅ Correct Answer: C. Microsoft Sentinel

In advanced hunting, EmailPostDeliveryEvents records post-delivery actions taken on messages (e.g., Zero-hour Auto Purge – ZAP, Automated Investigation and Response). To find emails that were detected but not removed, filter for the post-delivery action with ActionType has "ZAP" and ActionResult == "Error", which indicates ZAP attempted to remove the item but failed.

To correlate these mailbox events with recipient sign-ins, join to SigninLogs. The mailbox recipient field is RecipientEmailAddress; the corresponding identity field in sign-in data is the user principal name, exposed in normalized hunting schemas as AccountUpn. Therefore, use:

join kind=inner (...) on $left.RecipientEmailAddress == $right.AccountUpn

A complete pattern looks like:

EmailPostDeliveryEvents

| where Timestamp >= ago(7d)

| where ActionType has "ZAP" and ActionResult == "Error"

| project ErrorTime=Timestamp, ActionType, NetworkMessageId, RecipientEmailAddress

| join kind=inner (

SigninLogs

| project AccountUpn=UserPrincipalName, AppDisplayName, Protocol, DeviceDetail

) on $left.RecipientEmailAddress == $right.AccountUpn

| project ErrorTime, ActionType, NetworkMessageId, RecipientEmailAddress, AppDisplayName, Protocol, DeviceDetail

This returns failed ZAP removals and the associated recipient sign-in context.

In Microsoft 365 Defender advanced hunting, if you want to automatically receive alerts based on a KQL query—such as detecting when a process disables System Restore—you must convert that query into a custom detection rule. According to Microsoft’s official documentation, custom detection rules “run hunting queries on a schedule and create alerts and incidents when results are found.”

In order for the detection rule to function properly and correlate results across devices and incidents, the query must output DeviceId and ReportId. These fields are mandatory for any advanced hunting query that you want to convert into a detection rule because they uniquely identify the device and event instance. Without them, the rule cannot properly generate correlated alerts.

Therefore:

Create a detection rule (A) – ensures the query runs automatically and alerts are generated.

Add DeviceId and ReportId (E) – required for detection rule creation and accurate device/event correlation.

Other options are incorrect:

Suppression rule (B) filters alerts, not generate them.

Order by Timestamp (C) is optional for display, not alerting.

DeviceNetworkEvents (D) is unrelated to this process query.

 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()

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

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.

 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.

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

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 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.

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

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.

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 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

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

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.

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.

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 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.

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

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

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('<</b>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.