{"id":22945,"date":"2026-09-26T09:55:22","date_gmt":"2026-09-26T09:55:22","guid":{"rendered":"https:\/\/www.examlabs.com\/certification\/?p=22945"},"modified":"2026-09-26T09:55:22","modified_gmt":"2026-09-26T09:55:22","slug":"splunk-splk-1004-practice-test-questions-and-exam-dumps-part14-q261-280","status":"publish","type":"post","link":"https:\/\/www.examlabs.com\/certification\/splunk-splk-1004-practice-test-questions-and-exam-dumps-part14-q261-280\/","title":{"rendered":"Splunk SPLK-1004 Practice Test Questions and Exam Dumps Part14 Q261-280"},"content":{"rendered":"

View Full <\/b>Splunk SPLK-1004 Exam Dumps<\/b><\/a> and Practice Test Dumps<\/b><\/h2>\n

 <\/p>\n

Question 261.<\/b><\/p>\n

Which Splunk command is most appropriate when an analyst needs to calculate the total number of bytes for each combination of <\/b>host<\/b> and <\/b>application<\/b>?<\/b><\/p>\n

    \n
  1. <\/b> stats sum(bytes) BY host application<\/span>
    \n<\/span>2.<\/b> table host application bytes<\/span>
    \n<\/span>3.<\/b> dedup host application<\/span>
    \n<\/span>4.<\/b> top bytes BY host<\/span><\/li>\n<\/ol>\n

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

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

    The <\/span>stats<\/span> command is designed for aggregation, and <\/span>sum(bytes)<\/span> calculates the total value of the <\/span>bytes<\/span> field for each group. By adding <\/span>BY host application<\/span>, Splunk produces one result for every unique combination of host and application. This is useful when analysts want to understand traffic volumes or resource consumption across multiple dimensions. The <\/span>table<\/span> command only displays raw fields and performs no aggregation. <\/span>dedup<\/span> removes duplicate combinations but does not total values, while <\/span>top<\/span> identifies frequently occurring values rather than calculating sums. Therefore, <\/span>stats sum(bytes) BY host application<\/span> directly satisfies the requirement and produces a concise statistical result.<\/span><\/p>\n

    Question 262.<\/b><\/p>\n

    Which Splunk command should an analyst use to calculate the average duration for each user and then add that average back to every original event for the same user?<\/b><\/p>\n

      \n
    1. <\/b> stats<\/span>
      \n<\/span>2.<\/b> eventstats<\/span>
      \n<\/span>3.<\/b> chart<\/span>
      \n<\/span>4.<\/b> timechart<\/span><\/li>\n<\/ol>\n

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

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

      The <\/span>eventstats<\/span> command calculates an aggregate value and then adds the calculated field to the original events instead of replacing them. For example, <\/span>eventstats avg(duration) AS user_avg BY user<\/span> calculates an average duration for each user and attaches that value to every event belonging to that user. This makes it easy to compare an individual event with the user’s overall average. The <\/span>stats<\/span> command would collapse the results into one row per user, while <\/span>chart<\/span> and <\/span>timechart<\/span> also transform the event set. <\/span>eventstats<\/span> is therefore the best choice when the analyst needs both event-level detail and a grouped aggregate value in the same results.<\/span><\/p>\n

      Question 263.<\/b><\/p>\n

      Which Splunk function is most appropriate for calculating the number of unique destination ports observed for each source IP?<\/b><\/p>\n

        \n
      1. <\/b> count()<\/span>
        \n<\/span>2.<\/b> values()<\/span>
        \n<\/span>3.<\/b> dc()<\/span>
        \n<\/span>4.<\/b> list()<\/span><\/li>\n<\/ol>\n

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

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

        The <\/span>dc()<\/span> function calculates the distinct count of a field. A search such as <\/span>stats dc(dest_port) AS unique_ports BY src_ip<\/span> returns the number of unique destination ports contacted by each source IP. This is different from <\/span>count()<\/span>, which counts every populated occurrence and therefore includes repeated ports. <\/span>values()<\/span> returns the actual distinct values but not just the number, while <\/span>list()<\/span> can preserve duplicate values. Distinct counting is useful when measuring diversity of behavior, such as the number of unique users, destinations, ports, hosts, or applications associated with an entity. For this requirement, <\/span>dc()<\/span> provides exactly the requested count of unique destination ports.<\/span><\/p>\n

        Question 264.<\/b><\/p>\n

        Which Splunk command can calculate a running average over the most recent five events while preserving each event in the result set?<\/b><\/p>\n

          \n
        1. <\/b> stats<\/span>
          \n<\/span>2.<\/b> eventstats<\/span>
          \n<\/span>3.<\/b> accum<\/span>
          \n<\/span>4.<\/b> streamstats<\/span><\/li>\n<\/ol>\n

          Correct Answer: 4<\/b><\/p>\n

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

          The <\/span>streamstats<\/span> command performs calculations incrementally as results move through the search pipeline. By specifying a window, such as <\/span>window=5<\/span>, an analyst can calculate a moving average across the current event and a defined number of recent events while still preserving each original result. This is useful for short-term trend analysis, anomaly detection, and smoothing noisy values. <\/span>stats<\/span> would collapse the event set, while <\/span>eventstats<\/span> would attach a fixed group-level average rather than a moving one. <\/span>accum<\/span> is mainly intended for cumulative sums. Because the requirement involves a rolling calculation over a defined number of events, <\/span>streamstats<\/span> is the appropriate command.<\/span><\/p>\n

          Question 265.<\/b><\/p>\n

          An analyst wants to categorize events as <\/b>low<\/b>, <\/b>medium<\/b>, or <\/b>high<\/b> based on several response-time thresholds. Which SPL function is generally most maintainable?<\/b><\/p>\n

            \n
          1. <\/b> case()<\/span>
            \n<\/span>2.<\/b> coalesce()<\/span>
            \n<\/span>3.<\/b> split()<\/span>
            \n<\/span>4.<\/b> mvappend()<\/span><\/li>\n<\/ol>\n

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

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

            The <\/span>case()<\/span> function evaluates a series of condition-value pairs from left to right and returns the value associated with the first true condition. It is particularly useful for multi-level classifications such as low, medium, and high response times. For example, an analyst can define one condition for high values, another for medium values, and a final default condition for low values. Nested <\/span>if()<\/span> functions could produce the same result, but they often become harder to read as the number of conditions increases. <\/span>coalesce()<\/span> handles null values, while <\/span>split()<\/span> and <\/span>mvappend()<\/span> are multivalue functions. Therefore, <\/span>case()<\/span> is usually the clearest and most maintainable option for several thresholds.<\/span><\/p>\n

            Question 266.<\/b><\/p>\n

            Which Splunk command can be used to sort results by <\/b>duration<\/b> from the largest value to the smallest?<\/b><\/p>\n

              \n
            1. <\/b> sort + duration<\/span>
              \n<\/span>2.<\/b> sort – duration<\/span>
              \n<\/span>3.<\/b> top duration<\/span>
              \n<\/span>4.<\/b> reverse duration<\/span><\/li>\n<\/ol>\n

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

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

              The <\/span>sort<\/span> command orders search results based on one or more fields. A minus sign indicates descending order, so <\/span>sort – duration<\/span> places the largest duration values first. This is useful before applying commands such as <\/span>head<\/span> when an analyst wants to identify the highest values. A plus sign would sort in ascending order. The <\/span>top<\/span> command ranks values by frequency, not by numeric magnitude, while <\/span>reverse<\/span> simply reverses the current result order without sorting according to a field. Because sorting large result sets can consume resources, analysts should reduce the dataset first where practical. For descending duration values, <\/span>sort – duration<\/span> is the correct syntax.<\/span><\/p>\n

              Question 267.<\/b><\/p>\n

              Which Splunk command is best for identifying the 20 events with the highest value of a numeric field named <\/b>latency<\/b>?<\/b><\/p>\n

                \n
              1. <\/b> top latency limit=20<\/span>
                \n<\/span>2.<\/b> rare latency limit=20<\/span>
                \n<\/span>3.<\/b> sort – latency | head 20<\/span>
                \n<\/span>4.<\/b> dedup latency | tail 20<\/span><\/li>\n<\/ol>\n

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

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

                To identify the 20 largest numeric values, the analyst should sort the results by <\/span>latency<\/span> in descending order and then keep the first 20 records. The pattern <\/span>sort – latency | head 20<\/span> directly accomplishes this. The <\/span>top<\/span> command ranks values according to how frequently they occur, which is not the same as finding the largest numeric values. <\/span>rare<\/span> finds least frequent values, while <\/span>dedup<\/span> removes repeated field values and would change the result set unnecessarily. This distinction is important: frequency ranking and numeric ranking answer different questions. For identifying the highest individual latency events, sorting followed by <\/span>head<\/span> is the correct approach.<\/span><\/p>\n

                Question 268.<\/b><\/p>\n

                Which Splunk command is most appropriate for replacing null values in the field <\/b>department<\/b> with the text <\/b>Unknown<\/b>?<\/b><\/p>\n

                  \n
                1. <\/b> replace<\/span>
                  \n<\/span>2.<\/b> eval<\/span>
                  \n<\/span>3.<\/b> coalesce<\/span>
                  \n<\/span>4.<\/b> fillnull<\/span><\/li>\n<\/ol>\n

                  Correct Answer: 4<\/b><\/p>\n

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

                  The <\/span>fillnull<\/span> command replaces null field values with a specified replacement value. For example, <\/span>fillnull value=”Unknown” department<\/span> causes missing <\/span>department<\/span> values to appear as <\/span>Unknown<\/span> in the results. This is useful for reports and dashboards where blank values may be confusing or difficult to group. The <\/span>replace<\/span> command is generally used to substitute existing values that match a pattern rather than specifically handling nulls. <\/span>eval<\/span> can perform similar logic, but <\/span>fillnull<\/span> is more direct for this requirement. Analysts should select replacement values carefully so they are clearly understood as representing missing information rather than legitimate source data.<\/span><\/p>\n

                  Question 269.<\/b><\/p>\n

                  Which Splunk function returns the difference between the maximum and minimum values in a numeric field?<\/b><\/p>\n

                    \n
                  1. <\/b> range()<\/span>
                    \n<\/span>2.<\/b> delta()<\/span>
                    \n<\/span>3.<\/b> stdev()<\/span>
                    \n<\/span>4.<\/b> variance()<\/span><\/li>\n<\/ol>\n

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

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

                    The <\/span>range()<\/span> statistical function returns the difference between the largest and smallest values in the specified field. For example, <\/span>stats range(response_time) BY host<\/span> shows the spread between the fastest and slowest observed response times for each host. This can provide a quick measure of variability. <\/span>delta<\/span> calculates differences between sequential results rather than across an entire group. <\/span>stdev()<\/span> calculates standard deviation, and variance measures dispersion around the mean. Although range is a relatively simple measure because it depends only on two extreme values, it can still be useful for quickly assessing whether a field has a narrow or wide spread.<\/span><\/p>\n

                    Question 270.<\/b><\/p>\n

                    Which Splunk command calculates the difference between successive values in an ordered result set?<\/b><\/p>\n

                      \n
                    1. <\/b> accum<\/span>
                      \n<\/span>2.<\/b> delta<\/span>
                      \n<\/span>3.<\/b> streamstats only<\/span>
                      \n<\/span>4.<\/b> range<\/span><\/li>\n<\/ol>\n

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

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

                      The <\/span>delta<\/span> command calculates the difference between the current value of a numeric field and the value in a preceding result. This makes it useful for analyzing changes in counters, timestamps, response values, or resource measurements over time. Because the result depends on sequence, analysts should ensure the data is sorted correctly before using the command. <\/span>accum<\/span> creates a cumulative sum, while <\/span>range()<\/span> calculates the spread between minimum and maximum values over a set. <\/span>streamstats<\/span> can also support sequential calculations, but <\/span>delta<\/span> is specifically designed for straightforward event-to-event differences. Therefore, it is the most direct choice for comparing successive values.<\/span><\/p>\n

                      Question 271.<\/b><\/p>\n

                      Which Splunk function should be used to convert a textual timestamp into epoch time so it can be compared numerically with <\/b>_time<\/b>?<\/b><\/p>\n

                        \n
                      1. <\/b> strftime()<\/span>
                        \n<\/span>2.<\/b> now()<\/span>
                        \n<\/span>3.<\/b> strptime()<\/span>
                        \n<\/span>4.<\/b> relative_time()<\/span><\/li>\n<\/ol>\n

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

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

                        The <\/span>strptime()<\/span> function parses a textual date or time according to a specified format and converts it into epoch time. Once the value is numeric, it can be compared directly with <\/span>_time<\/span>, used in arithmetic, or passed into other time functions. For example, an analyst might convert <\/span>2026-09-26 14:30:00<\/span> using the appropriate format string before calculating a duration. <\/span>strftime()<\/span> performs the reverse conversion, turning epoch time into formatted text. <\/span>now()<\/span> returns the current epoch time, while <\/span>relative_time()<\/span> modifies an existing epoch timestamp. Therefore, <\/span>strptime()<\/span> is the appropriate function for converting a text timestamp into epoch form.<\/span><\/p>\n

                        Question 272.<\/b><\/p>\n

                        Which Splunk function converts an epoch timestamp into a formatted human-readable date and time?<\/b><\/p>\n

                          \n
                        1. <\/b> strptime()<\/span>
                          \n<\/span>2.<\/b> relative_time()<\/span>
                          \n<\/span>3.<\/b> tostring()<\/span>
                          \n<\/span>4.<\/b> strftime()<\/span><\/li>\n<\/ol>\n

                          Correct Answer: 4<\/b><\/p>\n

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

                          The <\/span>strftime()<\/span> function converts an epoch timestamp into a formatted date and time string. Analysts specify a formatting pattern to control how the result appears, such as year, month, day, hour, minute, and second. This is commonly used in tables and reports where raw epoch numbers would not be user-friendly. The <\/span>strptime()<\/span> function performs the reverse operation by parsing a formatted date string into epoch time. <\/span>relative_time()<\/span> shifts or snaps epoch timestamps according to relative expressions, while <\/span>tostring()<\/span> is a general conversion function and does not provide the same date-specific formatting control.<\/span><\/p>\n

                          Question 273.<\/b><\/p>\n

                          Which Splunk function is best suited for snapping the current time to the beginning of the current day?<\/b><\/p>\n

                            \n
                          1. <\/b> relative_time()<\/span>
                            \n<\/span>2.<\/b> strftime()<\/span>
                            \n<\/span>3.<\/b> earliest()<\/span>
                            \n<\/span>4.<\/b> floor()<\/span><\/li>\n<\/ol>\n

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

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

                            The <\/span>relative_time()<\/span> function can modify an epoch timestamp according to relative-time notation. For example, <\/span>relative_time(now(),”@d”)<\/span> snaps the current time to the beginning of the current day. It can also shift timestamps backward or forward by units such as minutes, hours, days, weeks, or months. This makes it useful for constructing custom time boundaries and calculating periods relative to now or another timestamp. <\/span>strftime()<\/span> only formats a timestamp, while <\/span>earliest()<\/span> is a statistical function and <\/span>floor()<\/span> performs numeric rounding. Therefore, <\/span>relative_time()<\/span> is the correct choice for snapping a timestamp to a defined time boundary.<\/span><\/p>\n

                            Question 274.<\/b><\/p>\n

                            An analyst has a field containing the string <\/b>admin,user,auditor<\/b> and wants to convert it into a multivalue field. Which function can accomplish this within <\/b>eval<\/b>?<\/b><\/p>\n

                              \n
                            1. <\/b> mvjoin()<\/span>
                              \n<\/span>2.<\/b> split()<\/span>
                              \n<\/span>3.<\/b> mvcount()<\/span>
                              \n<\/span>4.<\/b> mvindex()<\/span><\/li>\n<\/ol>\n

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

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

                              The <\/span>split()<\/span> function separates a single string into a multivalue field based on a specified delimiter. For example, <\/span>eval roles=split(roles,”,”)<\/span> converts the comma-separated string into three individual values: <\/span>admin<\/span>, <\/span>user<\/span>, and <\/span>auditor<\/span>. Once converted, the field can be processed using other multivalue functions such as <\/span>mvcount()<\/span>, <\/span>mvindex()<\/span>, and <\/span>mvjoin()<\/span>. The <\/span>mvjoin()<\/span> function performs the reverse transformation by combining multivalue elements into a single string. <\/span>mvcount()<\/span> counts values, while <\/span>mvindex()<\/span> retrieves specific positions. Therefore, <\/span>split()<\/span> is the appropriate choice for converting delimited text into a multivalue field.<\/span><\/p>\n

                              Question 275.<\/b><\/p>\n

                              Which Splunk function returns a specific value from a multivalue field based on its position?<\/b><\/p>\n

                                \n
                              1. <\/b> mvcount()<\/span>
                                \n<\/span>2.<\/b> mvjoin()<\/span>
                                \n<\/span>3.<\/b> mvindex()<\/span>
                                \n<\/span>4.<\/b> mvappend()<\/span><\/li>\n<\/ol>\n

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

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

                                The <\/span>mvindex()<\/span> function retrieves one or more values from a multivalue field using positional indexes. For example, <\/span>mvindex(roles,0)<\/span> returns the first element of the <\/span>roles<\/span> field. Negative indexes may also be used to count from the end. This is useful when the position of an element has meaning or when only a selected part of a multivalue field is required. <\/span>mvcount()<\/span> returns the number of values, <\/span>mvjoin()<\/span> converts all values into one delimited string, and <\/span>mvappend()<\/span> combines multiple values or multivalue fields. Therefore, <\/span>mvindex()<\/span> is the correct function for positional retrieval.<\/span><\/p>\n

                                Question 276.<\/b><\/p>\n

                                Which Splunk command expands each element of a multivalue field into its own result row?<\/b><\/p>\n

                                  \n
                                1. <\/b> makemv<\/span>
                                  \n<\/span>2.<\/b> nomv<\/span>
                                  \n<\/span>3.<\/b> split<\/span>
                                  \n<\/span>4.<\/b> mvexpand<\/span><\/li>\n<\/ol>\n

                                  Correct Answer: 4<\/b><\/p>\n

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

                                  The <\/span>mvexpand<\/span> command takes a multivalue field and produces a separate result for each of its elements. Other fields from the original result are duplicated as necessary. This is useful when analysts need to analyze, count, filter, or visualize individual multivalue elements separately. For example, if an event contains three roles, <\/span>mvexpand roles<\/span> generates three result rows. <\/span>makemv<\/span> converts delimited text into a multivalue field, while <\/span>nomv<\/span> turns a multivalue field into a single-value representation. Because expansion can greatly increase result counts, analysts should use <\/span>mvexpand<\/span> carefully on large datasets or fields with many elements.<\/span><\/p>\n

                                  Question 277.<\/b><\/p>\n

                                  Which Splunk command can be used to enrich events with geographical fields based on an IP address?<\/b><\/p>\n

                                    \n
                                  1. <\/b> iplocation<\/span>
                                    \n<\/span>2.<\/b> geostats<\/span>
                                    \n<\/span>3.<\/b> lookup only<\/span>
                                    \n<\/span>4.<\/b> metadata<\/span><\/li>\n<\/ol>\n

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

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

                                    The <\/span>iplocation<\/span> command adds geographic information based on an IP address field. Depending on the address and available location data, it may add fields such as country, region, city, latitude, and longitude. This is useful for analyzing the geographic origin of clients, connections, or other IP-related activity. The resulting latitude and longitude values can then be used with geographic visualizations or with <\/span>geostats<\/span>. The <\/span>geostats<\/span> command performs geographic aggregation but does not itself determine location from an IP address. <\/span>metadata<\/span> retrieves index metadata. Therefore, <\/span>iplocation<\/span> is the correct command for IP-based geographic enrichment.<\/span><\/p>\n

                                    Question 278.<\/b><\/p>\n

                                    Which Splunk command is designed to aggregate events geographically using latitude and longitude data?<\/b><\/p>\n

                                      \n
                                    1. <\/b> iplocation<\/span>
                                      \n<\/span>2.<\/b> geostats<\/span>
                                      \n<\/span>3.<\/b> timechart<\/span>
                                      \n<\/span>4.<\/b> chart<\/span><\/li>\n<\/ol>\n

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

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

                                      The <\/span>geostats<\/span> command performs statistical aggregation based on geographic coordinates, making the results suitable for map visualizations. It is often used after latitude and longitude fields have been added through <\/span>iplocation<\/span> or another source. Analysts can calculate counts, averages, sums, or other metrics across geographic regions and display them on maps. <\/span>iplocation<\/span> determines geographic information from an IP address but does not itself perform the geographic statistical aggregation. <\/span>timechart<\/span> focuses on time-series analysis, while <\/span>chart<\/span> provides general categorical aggregation. When the goal is location-based aggregation for mapping, <\/span>geostats<\/span> is the appropriate Splunk command.<\/span><\/p>\n

                                      Question 279.<\/b><\/p>\n

                                      Which Splunk command can retrieve a previously completed search job if its search ID is known and the job is still available?<\/b><\/p>\n

                                        \n
                                      1. <\/b> metadata<\/span>
                                        \n<\/span>2.<\/b> history<\/span>
                                        \n<\/span>3.<\/b> loadjob<\/span>
                                        \n<\/span>4.<\/b> collect<\/span><\/li>\n<\/ol>\n

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

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

                                        The <\/span>loadjob<\/span> command retrieves the results of a previously completed search job using its search ID. This is useful when an expensive search has already run and the analyst wants to reuse its results instead of executing the search again. The job must still exist according to Splunk’s search-job retention settings, and the user must have appropriate permissions to access it. <\/span>metadata<\/span> retrieves information from index metadata, while <\/span>collect<\/span> writes results into an index. Reusing existing search results through <\/span>loadjob<\/span> can save processing time and support workflows in which multiple analyses build on a previously completed search.<\/span><\/p>\n

                                        Question 280.<\/b><\/p>\n

                                        A dashboard repeatedly searches a large historical dataset to calculate the same grouped metrics. Which approach is generally best for improving performance when the use case supports it?<\/b><\/p>\n

                                          \n
                                        1. <\/b> Add more wildcard terms to each search<\/span>
                                          \n<\/span>2.<\/b> Increase the dashboard refresh frequency<\/span>
                                          \n<\/span>3.<\/b> Add <\/span>transaction<\/span> to every panel<\/span>
                                          \n<\/span>4.<\/b> Use an appropriate summary, acceleration, or precomputed-data strategy<\/span><\/li>\n<\/ol>\n

                                          Correct Answer: 4<\/b><\/p>\n

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

                                          Repeatedly scanning a large historical dataset for the same calculations can consume significant search resources and slow dashboard performance. Splunk provides several optimization strategies, including summary indexing, accelerated data models, and <\/span>tstats<\/span> queries where appropriate. These approaches allow frequently requested historical metrics to be obtained from smaller or optimized data structures instead of repeatedly processing all raw events. The best method depends on required fields, freshness, accuracy, data model design, and operational maintenance. Adding wildcards or <\/span>transaction<\/span> would generally increase processing rather than reduce it, and refreshing more frequently would add even more load. Precomputation or acceleration is usually the better design for repeated historical summaries.<\/span><\/p>\n

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

                                          View Full Splunk SPLK-1004 Exam Dumps and Practice Test Dumps   Question 261. Which Splunk command is most appropriate when an analyst needs to calculate the total number of bytes for each combination of host and application? stats sum(bytes) BY host application 2. table host application bytes 3. dedup host application 4. top bytes BY […]<\/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\/22945"}],"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=22945"}],"version-history":[{"count":1,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/22945\/revisions"}],"predecessor-version":[{"id":22946,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/22945\/revisions\/22946"}],"wp:attachment":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/media?parent=22945"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/categories?post=22945"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/tags?post=22945"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}