SC-200 Microsoft Security Operations Analyst Questions and Answers

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 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

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.

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.

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

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.

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

In Microsoft 365 Defender advanced hunting, email attachments are recorded in EmailAttachmentInfo and endpoint file activity is recorded in DeviceFileEvents. To correlate a malicious attachment to devices where the same file was observed, you join on the file hash (SHA256). The hunting guidance specifies using EmailAttachmentInfo to filter by sender and to ensure the attachment has a hash (isnotempty(SHA256)). Then, a join with DeviceFileEvents on SHA256 links the email-borne file to endpoint observations.

Before joining, it’s best practice to reduce the right-hand dataset with project to only needed fields (e.g., FileName, SHA256, DeviceName, DeviceId, Timestamp) to improve performance and limit data volume. After the join, use project again to shape the final result set for investigation, including timeline and pivot identifiers: Timestamp, FileName, SHA256, DeviceName, DeviceId, plus email context such as NetworkMessageId, SenderFromAddress, and RecipientEmailAddress.

Therefore, the correct operator sequence is: join (subquery over DeviceFileEvents with project) on SHA256, then final project of the incident-relevant columns.

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.

To integrate Microsoft Defender for Cloud with Azure DevOps (AzDO1) for detecting secrets exposed in pipelines, you must connect the two platforms using Microsoft’s built-in integration flow that enables Defender for Cloud to scan repositories and pipelines for vulnerabilities and sensitive data exposure.

Here’s the correct configuration process:

In the Defender for Cloud portal, you first need to add an environment that represents your Azure DevOps organization.

This step connects Defender for Cloud to the DevOps service, allowing it to monitor repositories, build pipelines, and artifacts.

Navigate to Defender for Cloud → Environment settings → Add environment → Azure DevOps.

You’ll then authenticate your Azure DevOps organization (AzDO1) to allow Defender for Cloud to scan your pipelines.

This setup enables Defender for DevOps, a capability within Defender for Cloud, to detect exposed secrets, insecure dependencies, and misconfigurations directly from pipeline activities.

Next, within Azure DevOps (AzDO1), install the Microsoft Defender for DevOps Security Scanner extension from the Azure DevOps Marketplace.

This extension integrates the Defender for Cloud scanning engine into the Azure DevOps pipeline process and enables automatic scanning for:

Hardcoded secrets in YAML pipelines,

Vulnerability findings in open-source dependencies, and

Infrastructure-as-code misconfigurations (for example, ARM, Terraform).

Once installed, the extension automatically works with Defender for Cloud, and any detected secret exposure or security issue is surfaced in Defender for Cloud’s “DevOps Security” dashboard.

Configure workflow automation: Applies to automated incident responses, not DevOps integration.

Enable a plan: Refers to enabling Defender plans for specific Azure resource types, not connecting DevOps.

Configure OAuth / Configure security policies: Required for custom integration scenarios, but not for standard Defender–DevOps onboarding.

✅ Final Correct Answer:

In Defender for Cloud: Add an environment

In AzDO1: Install an extension

In Microsoft 365 Defender’s Advanced Hunting (Kusto Query Language - KQL), investigators use the DeviceLogonEvents table to search for authentication attempts and filter failed logons. To detect failed sign-ins from specific devices, you begin by applying a where clause that filters only the relevant logon failures and the specific device names you want to investigate.

The correct syntax and logical order—based on Microsoft Security Operations (SecOps) and Microsoft 365 Defender hunting guide—is to first filter (where) the ActionType to “LogonFailed,” then narrow down the dataset to the target devices (DeviceName in ("CFOLaptop", "CEOLaptop", "COOLaptop")). This ensures that only failed authentication attempts from those three machines are included.

After filtering, the summarize operator is used to group the data by DeviceName and LogonType, counting how many failures occurred per device and logon type. This aggregation step follows Microsoft’s recommended practice for incident hunting, allowing analysts to quickly assess which devices are generating unusual authentication failure volumes.

Finally, the project statement selects only the LogonFailures output, simplifying the results for reporting or alerting purposes.

This query structure aligns exactly with Microsoft 365 Defender Advanced Hunting documentation for detecting failed logins on specific endpoints while maintaining query efficiency and clarity, minimizing administrative overhead during investigations

| From the Syslog configuration, remove the facilities that send CEF messages. | CEF1 | | From the Log Analytics agent, disable Syslog synchronization. | Server2 |

The goal is to eliminate duplicate events in the Azure Sentinel workspace (SW1). Duplication typically occurs when the same log source is sending data to Azure Sentinel via multiple collection methods.

Analysis of the Environment

SW1 is the Azure Sentinel (now Microsoft Sentinel) workspace, which is the final destination for all logs.

CEF1 is a Linux server configured as a log forwarder (often called a CEF collector) for Microsoft Sentinel. It uses the Log Analytics agent (or the newer Azure Monitor Agent) to ingest logs and is specifically configured to forward Common Event Format (CEF) logs to SW1.

Server1 sends CEF logs to CEF1. This is the intended, single collection path for Server1's CEF logs: Server1 CEF1 SW1. No duplication is inherent here.

Server2 sends Syslog logs to CEF1. This path is: Server2 CEF1 SW1.

Since CEF1 is running the Log Analytics agent (required to forward logs to SW1) and is configured to collect Syslog data (to receive Server2's logs), the Log Analytics agent on CEF1 will also attempt to ingest the Syslog messages it receives into SW1.

However, the Log Analytics agent itself can also be used to collect Syslog/CEF logs directly from the source server.

Addressing Duplication

Duplication is most likely to occur if a server is sending the same logs to a forwarder AND also has the Log Analytics agent configured to send the same logs directly to SW1.

Action 1: From the Syslog configuration, remove the facilities that send CEF messages.

Resource: CEF1

Reasoning: CEF1 is a Linux server running the Log Analytics agent and is acting as the collector. Server1 sends CEF logs to CEF1. These CEF logs are transmitted using Syslog (specifically, a custom Syslog format). If the Log Analytics agent on CEF1 is configured to collect all Syslog facilities, it will ingest the raw CEF Syslog messages it receives from Server1 AND also ingest the parsed CEF messages via its custom forwarding logic. To prevent the Syslog collector on CEF1 from ingesting the raw CEF messages that it is supposed to be forwarding, you must modify its Syslog configuration (e.g., in /etc/rsyslog.conf or equivalent) to ignore the facilities/log files used by the incoming CEF messages from Server1. The primary purpose of CEF1 is to receive and forward CEF, not to have its Log Analytics agent ingest the raw Syslog that transports the CEF payload.

Action 2: From the Log Analytics agent, disable Syslog synchronization.

Resource: Server2

Reasoning: Server2 is configured to send Syslog logs to CEF1 (Server2 CEF1 SW1). Since Server2 is a Linux server, it may also have the Log Analytics agent installed for other monitoring purposes. If the Log Analytics agent on Server2 is installed, it is configured by default to collect Syslog logs directly and send them to SW1 (Server2 SW1). This creates a duplicate path for the Syslog data:

Path A (Intended): Server2 Syslog CEF1 SW1

Path B (Duplication): Server2 Log Analytics Agent Syslog SW1

According to Microsoft Sentinel documentation on log ingestion, when using a dedicated forwarder (like CEF1) for Syslog/CEF, you must disable the Syslog collection on the Log Analytics agent of the source machine (Server2) to prevent this duplication. This is typically done by disabling Syslog synchronization in the Log Analytics agent configuration or removing the Syslog entry from the agent's data sources.

For hunting in Microsoft 365 Defender, email attachment metadata is in EmailAttachmentInfo (fields include Timestamp, FileName, SHA256, NetworkMessageId, etc.). To return every email that contains a specific attachment and optimize performance, apply time filters as early as possible and avoid unnecessary joins. Early filtering reduces the dataset to scan and speeds execution.

A performant query that meets “only last hour” and the attachment name requirement is:

EmailAttachmentInfo

| where Timestamp > ago(1h) // filter early for performance

| where Subject == "Document Attachment" and FileName == "Document.pdf"

| where Timestamp > ago(1h) // (idempotent second constraint; harmless)

Using joins (e.g., to DeviceFileEvents on SHA256) is not required to answer the question and would add overhead. The key is to filter on Timestamp and FileName within EmailAttachmentInfo, ensuring only emails from the last hour with Document.pdf are returned while keeping the query efficient.

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 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 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

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

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.

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

 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 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 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 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.

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.

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.”

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.

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.

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

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

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

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

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

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

 Table: DeviceFileEvents

 Aggregation function: count()

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

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

DeviceFileEvents

| where Timestamp > ago(2d)

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

| where activityCount > 5

Here’s why:

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

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

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

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

Thus, the verified and documented correct completions are:

Table: DeviceFileEvents

Aggregation function: count()

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

 External threat: Modify the Key Vault firewall settings.

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

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

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

Typical correlation looks like:

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

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

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

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

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