Free Microsoft SC-200 Practice Exam with Questions & Answers

This solution also does not meet the requirement. Applying filters to the already-imported worksheet rows in Excel (or attempting to reduce rows in the workbook UI) does not change how Power Query initially samples and infers the JSON schema during the Get & Transform steps. Power Query’s schema detection typically happens on a preview/sample of the source data when the query is first evaluated. If the JSON property you need does not appear in that sampled subset, P ower Query will not create a column for it even if you later filter the data to include rows that do contain that property. In short, post-export filtering in Excel is not a reliable way to force Power Query to detect and expand missing JSON properties.

Th e reliable approaches are to increase the number of rows Power Query uses for schema detection (Query Options / Data Load / preview/sample settings) or to explicitly parse each AuditData cell with JSON.Document (or equivalent M functions) and then expand t he record fields—these approaches ensure the specific JSON properties are surfaced as columns. Reducing rows via Excel filters does not address the sampling/schema-detection behavior and therefore fails to satisfy the requirement.

To complete the KQL query against the BehaviorAnalytics t able, 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 p ane, 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 typ e your KQL, making it straightforward to complete a clause like | where < ColumnName > == 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 autocompl ete to insert the correct column name.

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 t o 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 _GetWatc hlist( ' < 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 requireme nt 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—minimi zing 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 .

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 configurati on.

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 combine d.”

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 Sentinel, Hunting queries are used to proactively search for threats and anomalies across collected security data. The requirement states that HuntingQuery1 must run auto matically 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 orga nization’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 da ta 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 automati on 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 case s tudy 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

Microsoft Sentinel playboo ks 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 inc ident 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

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 da ily 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 overhe ad 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 o f 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 re tention 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 cos ts → 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.

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