SC-200 Practice Questions

 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.

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

The completed query is:

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

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

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

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

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

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

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

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

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

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

The other options are not suitable:

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

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

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

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

✅ Implement in the identity environment: Azure AD Password Protection

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

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

Putting it together, the recommended pattern is:

Im_Dns(pack="InfobloxNIOS")

| where DnsResponseCodeName == "NXDOMAIN"

| summarize count()

This approach satisfies all requirements:

Uses built-in ASIM (Im_Dns).

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

Targets NXDOMAIN responses for counting DNS request failures from Infoblox1.