Explanation:

To combine sign-in attempts from endpoints and on-premises AD DS domain controllers in Microsoft 365 Defender advanced hunting, you should query the tables that record those events and union them.
DeviceLogonEvents contains interactive and network logon activity collected from devices onboarded to Microsoft Defender for Endpoint, while IdentityLogonEvents contains sign-in activity observed by Microsoft Defender for Identity sensors on domain controllers. Using union appends the rows from both sources into a single result set while preserving columns such as Timestamp, AccountDomain, AccountName, AccountUpn, and AccountSid. This is preferable to a join because the goal is to list sign-in attempts (not correlate rows between tables). Applying take 100 to each table segment ensures you're pulling the 100 most recent entries from each source before unioning, then you can reorder and sort by Timestamp to review the consolidated timeline.
Therefore, to meet the requirement-identify the 100 most recent sign-in attempts recorded on devices and AD DS domain controllers-complete the KQL with union and IdentityLogonEvents.

Explanation:

Reference:
For CEF ingestion, Microsoft Sentinel uses a Linux "log forwarder" that runs the Log Analytics agent (OMS agent) and a syslog daemon (rsyslog/syslog-ng). The documented deployment flow is: first install the Log Analytics agent on the forwarder and connect it to your Sentinel workspace (Workspace ID/Key). Next, configure the agent to listen on TCP/UDP port 25226-the port the OMS agent uses to receive CEF-translated syslog messages locally-and forward them to the connected workspace (this forwarding is inherent once the agent is connected). Then configure the syslog daemon to receive the external product's CEF events on the chosen syslog port (commonly 514) and forward them locally to 127.0.0.1:25226. Finally, restart rsyslog
/syslog-ng and the OMS agent to apply changes. You do not forward events "directly to Sentinel" from syslog; the agent handles transport to the workspace. An OMS Gateway is only required when the forwarder has no direct Internet access and isn't part of the standard, minimal-effort path. This sequence ensures reliable, supported ingestion of CEF messages into Microsoft Sentinel with the least administrative overhead.

Explanation:

In Microsoft Sentinel (now part of Microsoft Defender XDR), Kusto Query Language (KQL) is used to create queries for workbooks, analytics rules, and hunting queries. The SecurityIncident table contains all incidents created in Sentinel, including their timestamps, severities, and metadata.
To meet the requirements:
* "List all incidents by incident number" # means grouping results by the field IncidentNumber.
* "Only include the most recent log for each incident" # means you must return only the latest record for each incident.
In KQL, the correct operator for this purpose is summarize arg_max(). The function arg_max (LastModifiedTime, *) returns the row (record) that has the maximum LastModifiedTime for each group- in this case, each unique IncidentNumber-along with all its other columns (indicated by *).
Therefore, the complete and correct query is:
SecurityIncident
| summarize arg_max(LastModifiedTime, *) by IncidentNumber
* summarize aggregates records based on the specified grouping key (IncidentNumber).
* arg_max() selects the latest record based on LastModifiedTime.
This query will return exactly one record per IncidentNumber, corresponding to the most recently modified incident entry-perfectly fulfilling the requirement of showing the latest incident state for each case in a Sentinel workbook.
# Correct selections:
* First dropdown: summarize
* Second dropdown: arg_max

Microsoft Sentinel Notebooks integrate with Azure Machine Learning and Jupyter Notebooks to provide advanced investigation, data visualization, and threat-hunting capabilities using Python and KQL.
For scenarios with massive IoT data or complex security events (such as from 10,000+ devices), notebooks are ideal because they allow security analysts to:
* Create custom visualizations to simplify investigation.
* Apply machine learning models for anomaly detection, correlation, and prediction.
* Perform deeper analytics than standard dashboards or built-in queries.
Microsoft documentation clearly states:
"Use notebooks in Microsoft Sentinel for advanced hunting, machine learning, and visualization. Notebooks help you extend Sentinel's analytics with AI-driven threat detection." Therefore, the correct recommendation is C. Notebooks.

The Risky users report in Microsoft Entra ID Protection provides visibility into users whose identities might have been compromised. It shows risk levels, risk states, and when risk detections occurred - allowing you to investigate activity for the last 90 days.
The Risk detections report lists individual risk events but not overall user risk status, while risky sign-ins report only sign-in attempts, not the cumulative user risk.
Thus, to determine whether User1's identity was compromised during the last 90 days, use the risky users report.