SC-200 Exam Dumps - Microsoft Security Operations Analyst

ï‚· Log Analytics workspace to use: LA1

ï‚· Windo ws 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 practic es 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 l evel 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 (inclu ding 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 Al l 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.

According to Micros oft’s official Defender for Identity deployment documentation, setting up Microsoft Defender for Identity (MDI) on an on-premises domain controller (DC) such as DC1 requires a specific sequence of steps. MDI monitors and protects Active Directory from adva nced threats by using sensors installed directly on domain controllers.

The correct sequence is as follows:

1️ ⃣ Provide global administrator credentials to the Azure AD tenant.

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

2️ ⃣ Create an instance of Microsoft Defender for Identity.

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

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

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

4️ ⃣ Install the sensor on DC1.

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

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

✅ Final Order:

Provide global administrator credentials →

Create instance of Microsoft Defender for Identity →

Provide domain administrator credentials →

Install the sensor on DC1.

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

According to Microsoft Sentinel documentation, automation rules are used to automatically assign, tag, or close incidents as soon as they are created. Automation rules can filter incidents based on attributes such as alert name, severity, or source , and then perform specific actions like assigning incidents to users or groups .

In this scenario, incidents are generated by Rule1 from two different AD DS domains — contoso.com and fabrikam.com — and each must be routed to a specific security group for triage. The most efficient approach is to create two automation rules , each filtering incidents by domain (one for contoso.com and on e for fabrikam.com) and automatically assigning them to Group1 and Group2 respectively. This approach minimizes administrative overhead because automation rules are easy to maintain and don’t require the complexity of a playbook.

A playbook (Logic App) cou ld technically achieve the same outcome, but it introduces more administrative management, permissions, and maintenance. Hence, per Microsoft Sentinel best practices, multiple automation rules provide a lightweight and direct automation layer for incident assignment.

✅ Correct Answer: C. two automation rules assigned to Rule1

When User and Entity Behavior Analytics (UEBA) is enabled in Microsoft Sentinel , it creates several dedicated tables within the Log Analytics workspace to store pr ocessed data for behavioral analytics and anomaly detection. Each table serves a specific purpose according to Microsoft documentation.

BehaviorAnalytics Table — for viewing entity data The BehaviorAnalytics table stores enriched information about entities (such as users, hosts, IP addresses, and applications) and their observed behaviors. Each record includes multiple fields that describe user or entity activities, risk scores, and behavioral baselines. Microsoft Sentinel documentation states:

“Use the Beh aviorAnalytics table to view the entity data collected and analyzed by UEBA. This table contains fields for each type of entity, including account, host, and IP data.”

Therefore, to view the entity data with detailed attributes for each type, you query the BehaviorAnalytics table in KQL.

Anomalies Table — for assessing rule quality The Anomalies table is used to analyze the results of anomaly detection rules and evaluate their effectiveness. Each record represents an anomaly event generated by UEBA’s machin e learning or statistical models. Microsoft’s UEBA and Sentinel analytics documentation explains:

“Use the Anomalies table to assess the performance and quality of your anomaly detection rules. The table helps you identify how well each rule detects unusua l activities and whether it produces false positives.”

Thus, when you need to measure how well your rules perform (i.e., their quality, hit rate, or alert effectiveness), you use the Anomalies table.

Summary Mapping:

View entity data → BehaviorAnalytics

Assess rule quality → Anomalies

This mapping aligns directly with the functionality of UEBA-related tables in Microsoft Sentinel and follows official documentation for analyzing entity behaviors and anomaly rule performance.

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 Acco unt 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.

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Acc ording 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 an d 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 incident s 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 opt ions 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, bas ed 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 , Defende r 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, en abling 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

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-min imization 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 compr ehensive 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 increase s 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