Microsoft Updated SC-200 Exam Questions and Answers by yaqub

This solution does not reliably meet the requirement. The underlying problem is not the absolute count of rows in the CSV but how Power Query (Get & Transform) infers and expands JSON properties: Power Query samples the input rows to discover the JSON schema/fields. If a particular JSON property does not appear in the sample used for schema detection, Power Query will not create a column for it when you expand the JSON. Simply reducing the exported record count from Defender (so you end up with fewer rows) only helps if the exported dataset by chance contains the missing JSON properties within the rows Power Query samples. It is not a guaranteed or controlled fix and therefore does not meet the requirement.

The correct fixes are to (a) adjust Power Query’s sampling/preview settings so it inspects more rows (so the property is included in schema detection), or (b) explicitly parse the JSON and expand fields using Power Query M functions (for example parse each AuditData with JSON.Document and then expand Record fields), which ensures the desired properties become columns regardless of initial sampling. Reducing Defender export size is not the recommended or reliable approach.

To identify which blobs were deleted in an Azure Storage account, you must review Azure Storage Analytics logs, which record all operations (including DeleteBlob and DeleteContainer requests) made against the storage service.

These logs contain details such as timestamp, requester IP, operation type, and object name—allowing you to pinpoint the exact blobs deleted.

Activity logs (Option B) record control-plane operations (e.g., resource creation or configuration changes), not data-plane operations like blob deletions.

Alert details and related entities (Options C and D) summarize detection context but do not include full operation-level details.

✅ Correct Answer: A. the Azure Storage Analytics logs

In Microsoft Sentinel (and Azure Monitor Logs), when you create a custom parser, it must output a consistent schema from a KQL query that only transforms or reshapes data. The query cannot include commands that control presentation, sorting, or time filtering logic such as sort by, take, or explicit where TimeGenerated > ago(). These constructs are valid in ad-hoc queries and hunting scenarios but not supported inside parser definitions because parsers are designed to provide reusable, schema-consistent structured data for analytics, detection rules, and normalization.

In the provided example, line 5 uses:

sort by TimeGenerated desc nulls last

Sorting is purely a presentation operation and is not allowed within a parser definition. A parser should only manipulate and project columns (for example, project, extend, parse, etc.) but should not include sort, limit, or take clauses.

Other lines serve valid data filtering and shaping purposes:

Line 2 filters the time range (TimeGenerated > ago(7d)), which can remain for a specific scenario setup.

Line 3 applies a valid operation filter (where Operation contains "delete").

Line 4 projects relevant fields.

Therefore, to prepare this query for inclusion in a custom parser, you must remove line 5, the sort by command, ensuring the parser complies with Sentinel’s parser syntax and execution engine requirements.

Hence, the correct answer is C. Remove line 5.

Comprehensive and Detailed Explanation with all Microsoft Security Operations (SecOps) documents: =

To search for specific criteria in Amazon Web Services (AWS) logs and generate incidents using Microsoft Sentinel, the configuration process follows a structured sequence according to Microsoft Sentinel documentation and the Azure Sentinel playbook for AWS integration.

Add the Amazon Web Services (AWS) connector

Before Sentinel can analyze AWS data, you must integrate AWS logs using the Amazon Web Services data connector. This connector streams AWS CloudTrail and other AWS log data into your Sentinel workspace. Microsoft’s documentation states:“Use the Amazon Web Services (AWS) connector to stream CloudTrail events and security logs into Microsoft Sentinel for analysis and alerting.”

Without this connector, Sentinel cannot query or detect AWS-specific activities.

Create a custom analytics rule that uses a scheduled query

Once data ingestion is established, you create an analytics rule in Sentinel using a scheduled query to continuously search for specific conditions (e.g., unauthorized access attempts, changes to VPC settings, etc.).

Microsoft specifies: “Custom analytics rules run KQL queries on a schedule to detect specific patterns or anomalies across ingested data sources.”

Set the alert logic

After defining your rule, you configure the alert logic to determine when Sentinel should trigger an alert or incident. This includes setting thresholds, event frequency, severity levels, and entity mappings.

Microsoft Sentinel’s official guidance notes: “Alert logic defines the conditions under which an alert is generated from the query results.”