{"id":15817,"date":"2026-09-18T09:42:09","date_gmt":"2026-09-18T09:42:09","guid":{"rendered":"https:\/\/www.examlabs.com\/certification\/?p=15817"},"modified":"2026-09-18T09:42:09","modified_gmt":"2026-09-18T09:42:09","slug":"microsoft-sc-200-practice-test-questions-and-exam-dumps-part20-q381-400","status":"publish","type":"post","link":"https:\/\/www.examlabs.com\/certification\/microsoft-sc-200-practice-test-questions-and-exam-dumps-part20-q381-400\/","title":{"rendered":"Microsoft SC-200 Practice Test Questions and Exam Dumps Part20 Q381-400"},"content":{"rendered":"

<\/h1>\n

View Full <\/b>Microsoft SC-200 Exam Dumps<\/b><\/a> and Practice Test Dumps.<\/b><\/h2>\n

Question 381<\/b><\/h3>\n

Which KQL operator is useful for expanding an array or dynamic property into separate rows?<\/b><\/p>\n

    \n
  1. mv-expand<\/span><\/li>\n
  2. summarize<\/span><\/li>\n
  3. project<\/span><\/li>\n
  4. distinct<\/span><\/li>\n<\/ol>\n

    Correct Answer: 1<\/b><\/p>\n

    Explanation<\/b><\/p>\n

    The KQL <\/span>mv-expand<\/span> operator expands a dynamic array or property into individual records. This is useful when security telemetry contains multiple values within one field and an analyst needs to examine each value separately. For example, a record containing several IP addresses, process arguments, or other dynamic elements can be expanded for more detailed analysis. Analysts should understand the structure of the source field before using this operator because expansion can significantly increase the number of returned rows. Additional filtering may help keep large investigation queries manageable.<\/span><\/p>\n

    Question 382<\/b><\/h3>\n

    A SOC analyst needs to standardize event data from different security products so that similar fields can be queried consistently. Which Microsoft Sentinel capability supports this goal?<\/b><\/p>\n

      \n
    1. Workbooks<\/span><\/li>\n
    2. Advanced Security Information Model (ASIM)<\/span><\/li>\n
    3. Watchlists<\/span><\/li>\n
    4. Automation rules<\/span><\/li>\n<\/ol>\n

      Correct Answer: 2<\/b><\/p>\n

      Explanation<\/b><\/p>\n

      Microsoft Sentinel’s Advanced Security Information Model, or ASIM, provides normalized schemas for common security event types. It helps analysts work with data from different sources using consistent field names and query approaches. This can simplify hunting and detection development when organizations collect telemetry from multiple vendors and products. ASIM parsers can translate source-specific fields into normalized representations. Analysts should verify that the appropriate parser and schema are available for their data source. Normalization can reduce query complexity while helping detections remain more consistent across different environments.<\/span><\/p>\n

      Question 383<\/b><\/h3>\n

      Which KQL function can divide a timestamp into fixed time intervals for trend analysis?<\/b><\/p>\n

        \n
      1. parse_json()<\/span><\/li>\n
      2. extract()<\/span><\/li>\n
      3. bin()<\/span><\/li>\n
      4. strcat()<\/span><\/li>\n<\/ol>\n

        Correct Answer: 3<\/b><\/p>\n

        Explanation<\/b><\/p>\n

        The KQL <\/span>bin()<\/span> function groups values into fixed-size intervals. It is commonly used with timestamps to create time-based summaries. For example, an analyst can group security events into five-minute or one-hour intervals to identify periods of increased activity. A query using <\/span>summarize count() by bin(TimeGenerated, 1h)<\/span> can provide an event trend by hour. This technique is useful for detecting spikes and comparing activity over time. Analysts should select an interval appropriate to the investigation because excessively large or small bins can obscure meaningful patterns.<\/span><\/p>\n

        Question 384<\/b><\/h3>\n

        Which Microsoft Sentinel capability provides prebuilt content packages that can include connectors, analytics rules, hunting queries, workbooks, and playbooks?<\/b><\/p>\n

          \n
        1. Content hub<\/span><\/li>\n
        2. Investigation graph<\/span><\/li>\n
        3. Incident queue<\/span><\/li>\n
        4. Entity mapping<\/span><\/li>\n<\/ol>\n

          Correct Answer: 1<\/b><\/p>\n

          Explanation<\/b><\/p>\n

          The Microsoft Sentinel content hub provides packaged security content for different products, services, and scenarios. Depending on the solution, packages can include analytics rules, hunting queries, workbooks, playbooks, parsers, and other components. This allows security teams to deploy relevant capabilities without creating every component from scratch. Analysts should review the included content and customize it where necessary for their environment. Installing a solution does not automatically mean every detection is appropriate for production, so teams should test rules and understand their data requirements before enabling them broadly.<\/span><\/p>\n

          Question 385<\/b><\/h3>\n

          Which KQL function can extract a portion of text from a string using a regular expression pattern?<\/b><\/p>\n

            \n
          1. strcat()<\/span><\/li>\n
          2. extract()<\/span><\/li>\n
          3. bin()<\/span><\/li>\n
          4. isempty()<\/span><\/li>\n<\/ol>\n

            Correct Answer: 2<\/b><\/p>\n

            Explanation<\/b><\/p>\n

            The KQL <\/span>extract()<\/span> function can retrieve text from a string by using a regular expression pattern. This is useful when security logs contain structured information embedded inside larger strings, such as command lines, URLs, identifiers, or event messages. An analyst can use a regular expression to capture the relevant portion and return it as a new value. Regular expressions should be designed carefully because overly broad patterns can produce incorrect results or unnecessary processing. Analysts should validate extracted values against known examples before relying on them for detection or investigation decisions.<\/span><\/p>\n

            Question 386<\/b><\/h3>\n

            A security analyst wants to investigate activity involving a specific user, IP address, and device across Microsoft Defender data. Which capability is designed for interactive cross-table security investigations?<\/b><\/p>\n

              \n
            1. Secure Score<\/span><\/li>\n
            2. Advanced Hunting<\/span><\/li>\n
            3. Attack simulation training<\/span><\/li>\n
            4. Device discovery<\/span><\/li>\n<\/ol>\n

              Correct Answer: 2<\/b><\/p>\n

              Explanation<\/b><\/p>\n

              Microsoft Defender Advanced Hunting provides an interactive query environment for investigating security telemetry across supported Microsoft Defender data. Analysts can combine multiple conditions and tables to examine activity associated with users, devices, IP addresses, processes, files, and other entities. This makes it useful for threat hunting and incident investigation when analysts need to move beyond individual alerts. Effective hunting requires appropriate time filters and carefully selected fields. Analysts should also validate suspicious findings against additional telemetry because a single matching event does not necessarily establish malicious behavior.<\/span><\/p>\n

              Question 387<\/b><\/h3>\n

              Which KQL operator is commonly used to combine text values into a single string?<\/b><\/p>\n

                \n
              1. strcat()<\/span><\/li>\n
              2. mv-expand<\/span><\/li>\n
              3. bin()<\/span><\/li>\n
              4. project-away<\/span><\/li>\n<\/ol>\n

                Correct Answer: 1<\/b><\/p>\n

                Explanation<\/b><\/p>\n

                The KQL <\/span>strcat()<\/span> function combines multiple string expressions into one resulting string. Analysts can use it to construct readable values or combine fields during investigation and analysis. For example, a query could combine a username and domain into a single identifier for easier review. This can also be useful when creating calculated fields for reporting or grouping. Analysts should ensure that source fields contain the expected data types and values. If null or inconsistent values are present, the resulting output should be validated before being used for security conclusions.<\/span><\/p>\n

                Question 388<\/b><\/h3>\n

                Which Microsoft Sentinel feature allows an analyst to associate a security event with entities such as accounts, hosts, IP addresses, or URLs when configuring detection logic?<\/b><\/p>\n

                  \n
                1. Data connector<\/span><\/li>\n
                2. Entity mapping<\/span><\/li>\n
                3. Content hub<\/span><\/li>\n
                4. Workbook<\/span><\/li>\n<\/ol>\n

                  Correct Answer: 2<\/b><\/p>\n

                  Explanation<\/b><\/p>\n

                  Entity mapping in Microsoft Sentinel helps identify important entities within alert or event data. Examples include accounts, hosts, IP addresses, URLs, files, and other supported entity types. Correct mapping gives incidents useful context and allows Sentinel to associate related security information with recognizable entities. When creating analytics rules, analysts should map the relevant fields accurately and ensure that the source data contains reliable values. Incorrect mappings can reduce investigation context or cause analysts to interpret unrelated records as belonging to the same entity.<\/span><\/p>\n

                  Question 389<\/b><\/h3>\n

                  Which KQL operator can remove unnecessary columns from the query output?<\/b><\/p>\n

                    \n
                  1. project-away<\/span><\/li>\n
                  2. join<\/span><\/li>\n
                  3. contains<\/span><\/li>\n
                  4. summarize<\/span><\/li>\n<\/ol>\n

                    Correct Answer: 1<\/b><\/p>\n

                    Explanation<\/b><\/p>\n

                    The KQL <\/span>project-away<\/span> operator removes specified columns from the result set while retaining other columns. It is useful when analysts want to simplify output without explicitly listing every field that should remain. This can improve readability when working with tables containing many columns. For example, analysts can remove technical or redundant fields after completing calculations while keeping the information needed for investigation. Analysts should use this operator carefully when later query stages require a removed field, because once a column is excluded it cannot be referenced by subsequent operations.<\/span><\/p>\n

                    Question 390<\/b><\/h3>\n

                    Which Microsoft Defender XDR capability helps security teams identify and prioritize security weaknesses across devices and software?<\/b><\/p>\n

                      \n
                    1. Threat analytics<\/span><\/li>\n
                    2. Exposure management<\/span><\/li>\n
                    3. Safe Attachments<\/span><\/li>\n
                    4. Live response<\/span><\/li>\n<\/ol>\n

                      Correct Answer: 2<\/b><\/p>\n

                      Explanation<\/b><\/p>\n

                      Microsoft Defender Vulnerability Management and related exposure management capabilities help organizations identify vulnerabilities, misconfigurations, exposed assets, and other security weaknesses. Security teams can use this information to understand where risk exists and prioritize remediation activities. Exposure management extends the focus beyond individual alerts by considering weaknesses that could contribute to an attack path. Analysts should review the affected assets, vulnerability details, and available remediation guidance before taking action. Prioritization should also consider the organization’s asset importance and the actual exposure of the affected systems.<\/span><\/p>\n

                      Question 391<\/b><\/h3>\n

                      Which KQL function can test whether a string or dynamic value is empty?<\/b><\/p>\n

                        \n
                      1. isempty()<\/span><\/li>\n
                      2. bin()<\/span><\/li>\n
                      3. count()<\/span><\/li>\n
                      4. max()<\/span><\/li>\n<\/ol>\n

                        Correct Answer: 1<\/b><\/p>\n

                        Explanation<\/b><\/p>\n

                        The KQL <\/span>isempty()<\/span> function checks whether a value is empty. It can be useful when analysts need to filter records based on whether a field contains meaningful information. For example, an investigation may need to identify events where a particular username, command-line field, or other value was not populated. Checking for empty values can improve query accuracy and prevent misleading analysis. Analysts should distinguish between empty values and null values because they can have different meanings depending on the data source and query context. Proper handling of both improves investigation reliability.<\/span><\/p>\n

                        Question 392<\/b><\/h3>\n

                        An organization wants Sentinel to recognize a particular field from an alert as the affected user so the incident contains useful identity context. What should the analyst configure?<\/b><\/p>\n

                          \n
                        1. Entity mapping<\/span><\/li>\n
                        2. Workbook parameters<\/span><\/li>\n
                        3. Watchlist refresh<\/span><\/li>\n
                        4. Data retention<\/span><\/li>\n<\/ol>\n

                          Correct Answer: 1<\/b><\/p>\n

                          Explanation<\/b><\/p>\n

                          Entity mapping allows Sentinel analytics rules to identify important fields as recognized entities. If a field represents an affected account or user, mapping it appropriately can provide additional identity context within incidents. This can improve investigation workflows because analysts can more easily understand which entities are involved and pivot to related information. Entity mapping should use the correct source field and entity type. If the source data is inconsistent or incorrectly mapped, Sentinel may provide incomplete or misleading context, so analysts should validate mappings during rule testing.<\/span><\/p>\n

                          Question 393<\/b><\/h3>\n

                          Which KQL operator can test whether a field begins with a specified string?<\/b><\/p>\n

                            \n
                          1. endswith<\/span><\/li>\n
                          2. startswith<\/span><\/li>\n
                          3. contains<\/span><\/li>\n
                          4. has_any<\/span><\/li>\n<\/ol>\n

                            Correct Answer: 2<\/b><\/p>\n

                            Explanation<\/b><\/p>\n

                            The KQL <\/span>startswith<\/span> operator tests whether a string begins with a specified sequence of characters. It can be useful when analysts need to identify values sharing a common prefix, such as particular hostnames, file paths, account naming patterns, or URL structures. This differs from <\/span>contains<\/span>, which searches for a sequence anywhere within the string. Analysts should choose the operator that matches the intended detection logic. Prefix-based filtering can reduce unrelated results when the beginning of a field has a known and meaningful structure.<\/span><\/p>\n

                            Question 394<\/b><\/h3>\n

                            Which Microsoft Sentinel capability can help identify suspicious behavior by using machine learning-based user and entity behavior analytics?<\/b><\/p>\n

                              \n
                            1. UEBA<\/span><\/li>\n
                            2. Watchlists<\/span><\/li>\n
                            3. Workbooks<\/span><\/li>\n
                            4. Data connectors<\/span><\/li>\n<\/ol>\n

                              Correct Answer: 1<\/b><\/p>\n

                              Explanation<\/b><\/p>\n

                              User and Entity Behavior Analytics, commonly referred to as UEBA, can help identify anomalous behavior involving users and other entities. Microsoft Sentinel can use behavioral analytics to establish patterns and highlight activity that differs from expected behavior. This can provide additional investigation context when analysts review suspicious accounts, devices, or other entities. UEBA findings should not automatically be treated as proof of compromise because unusual activity may have legitimate explanations. Analysts should correlate behavioral findings with authentication, endpoint, network, and other available security telemetry before making response decisions.<\/span><\/p>\n

                              Question 395<\/b><\/h3>\n

                              Which KQL function can extract a value from a JSON-formatted string or dynamic object?<\/b><\/p>\n

                                \n
                              1. parse_json()<\/span><\/li>\n
                              2. avg()<\/span><\/li>\n
                              3. strcat()<\/span><\/li>\n
                              4. bin()<\/span><\/li>\n<\/ol>\n

                                Correct Answer: 1<\/b><\/p>\n

                                Explanation<\/b><\/p>\n

                                The KQL <\/span>parse_json()<\/span> function converts a JSON-formatted string into a dynamic object that can be queried for individual properties. Security logs often contain nested information inside JSON structures, so parsing can make those values accessible for filtering and analysis. After parsing, analysts can reference specific properties within the resulting dynamic value. This is useful when investigating application logs, API data, and other structured telemetry. Analysts should first verify that the source field contains valid JSON because malformed or unexpected input can affect query results and lead to incomplete analysis.<\/span><\/p>\n

                                Question 396<\/b><\/h3>\n

                                Which Microsoft Defender capability can provide recommendations for reducing attack surface and improving security posture based on identified weaknesses?<\/b><\/p>\n

                                  \n
                                1. Threat Explorer<\/span><\/li>\n
                                2. Exposure management<\/span><\/li>\n
                                3. Safe Links<\/span><\/li>\n
                                4. Attack simulation training<\/span><\/li>\n<\/ol>\n

                                  Correct Answer: 2<\/b><\/p>\n

                                  Explanation<\/b><\/p>\n

                                  Microsoft Defender exposure management capabilities can help organizations identify security weaknesses and provide information that supports reducing exposure to threats. Security teams can review affected assets, vulnerabilities, attack paths, and other security posture information to determine where remediation may reduce risk. This approach complements alert investigation by addressing conditions that could make future attacks easier. Analysts should review the evidence behind recommendations and consider business requirements before implementing changes. Exposure information is most useful when organizations continuously monitor their environment and address important weaknesses as they are identified.<\/span><\/p>\n

                                  Question 397<\/b><\/h3>\n

                                  Which KQL operator can determine whether a string field matches any value from a specified list?<\/b><\/p>\n

                                    \n
                                  1. has_any<\/span><\/li>\n
                                  2. project<\/span><\/li>\n
                                  3. order by<\/span><\/li>\n
                                  4. summarize<\/span><\/li>\n<\/ol>\n

                                    Correct Answer: 1<\/b><\/p>\n

                                    Explanation<\/b><\/p>\n

                                    The KQL <\/span>has_any<\/span> operator can test whether a field contains any term from a specified set of values. It can be useful when analysts need to search for multiple known indicators or terms in one query. For example, an investigation can use a list of suspicious values instead of writing separate conditions for every item. Analysts should understand how KQL tokenizes string fields because term-based operators do not always behave like exact substring comparisons. For precise matching requirements, analysts should select an operator appropriate to the structure and purpose of the field.<\/span><\/p>\n

                                    Question 398<\/b><\/h3>\n

                                    Which Microsoft Sentinel capability allows analysts to create visual dashboards from query results and monitor security trends?<\/b><\/p>\n

                                      \n
                                    1. Entity mapping<\/span><\/li>\n
                                    2. Workbooks<\/span><\/li>\n
                                    3. ASIM<\/span><\/li>\n
                                    4. Automation rules<\/span><\/li>\n<\/ol>\n

                                      Correct Answer: 2<\/b><\/p>\n

                                      Explanation<\/b><\/p>\n

                                      Microsoft Sentinel workbooks provide interactive visualizations and dashboards based on security data and queries. Analysts can use them to monitor trends, summarize incidents, review authentication activity, and display other operational information. Workbooks can contain charts, tables, metrics, and interactive controls that help security teams understand their environment. They are primarily used for visualization and analysis rather than automated response. Analysts should ensure that workbook queries use appropriate data sources and time ranges. A visually clear dashboard is useful only when the underlying data is accurate and relevant to the monitoring objective.<\/span><\/p>\n

                                      Question 399<\/b><\/h3>\n

                                      Which KQL function returns the average value of a numeric field?<\/b><\/p>\n

                                        \n
                                      1. min()<\/span><\/li>\n
                                      2. max()<\/span><\/li>\n
                                      3. avg()<\/span><\/li>\n
                                      4. dcount()<\/span><\/li>\n<\/ol>\n

                                        Correct Answer: 3<\/b><\/p>\n

                                        Explanation<\/b><\/p>\n

                                        The KQL <\/span>avg()<\/span> aggregation function calculates the average value of a numeric field. Analysts can use it with <\/span>summarize<\/span> to calculate averages across selected groups, such as average response time, average event size, or another measurable security-related value. The usefulness of an average depends on the quality and distribution of the underlying data. Extreme values can influence the result significantly, so analysts should consider whether an average accurately represents the behavior being studied. Filtering to the appropriate time period and event population is also important.<\/span><\/p>\n

                                        Question 400<\/b><\/h3>\n

                                        A SOC analyst receives a suspicious alert and needs to determine whether the same indicator appears across multiple Microsoft security workloads. Which approach is most appropriate?<\/b><\/p>\n

                                          \n
                                        1. Review only the original alert<\/span><\/li>\n
                                        2. Use Advanced Hunting and correlate relevant telemetry<\/span><\/li>\n
                                        3. Modify the workbook title<\/span><\/li>\n
                                        4. Refresh a watchlist without investigating the alert<\/span><\/li>\n<\/ol>\n

                                          Correct Answer: 2<\/b><\/p>\n

                                          Explanation<\/b><\/p>\n

                                          Advanced Hunting provides a practical way to search and correlate security telemetry across supported Microsoft Defender workloads. When an alert contains a suspicious indicator, an analyst can search for related files, processes, domains, IP addresses, users, devices, and other activity across available tables. This broader investigation can help determine whether the indicator appears elsewhere and whether the original alert is part of a larger activity pattern. Analysts should use suitable time ranges and relevant fields, then validate findings against additional evidence before determining the appropriate incident response.<\/span><\/p>\n

                                           <\/p>\n","protected":false},"excerpt":{"rendered":"

                                          View Full Microsoft SC-200 Exam Dumps and Practice Test Dumps. Question 381 Which KQL operator is useful for expanding an array or dynamic property into separate rows? mv-expand summarize project distinct Correct Answer: 1 Explanation The KQL mv-expand operator expands a dynamic array or property into individual records. This is useful when security telemetry contains […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[1648,1647],"tags":[],"_links":{"self":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/15817"}],"collection":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/comments?post=15817"}],"version-history":[{"count":1,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/15817\/revisions"}],"predecessor-version":[{"id":15818,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/15817\/revisions\/15818"}],"wp:attachment":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/media?parent=15817"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/categories?post=15817"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/tags?post=15817"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}