{"id":22953,"date":"2026-09-26T09:56:26","date_gmt":"2026-09-26T09:56:26","guid":{"rendered":"https:\/\/www.examlabs.com\/certification\/?p=22953"},"modified":"2026-09-26T09:56:26","modified_gmt":"2026-09-26T09:56:26","slug":"splunk-splk-1004-practice-test-questions-and-exam-dumps-part18-q341-360","status":"publish","type":"post","link":"https:\/\/www.examlabs.com\/certification\/splunk-splk-1004-practice-test-questions-and-exam-dumps-part18-q341-360\/","title":{"rendered":"Splunk SPLK-1004 Practice Test Questions and Exam Dumps Part18 Q341-360"},"content":{"rendered":"

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

 <\/p>\n

Question 341.<\/b><\/p>\n

An analyst wants to identify the number of unique destination IP addresses contacted by each source IP. Which SPL is most appropriate?<\/b><\/p>\n

    \n
  1. <\/b> stats dc(dest_ip) AS unique_destinations BY src_ip<\/span>
    \n<\/span>2.<\/b> stats count(dest_ip) AS unique_destinations BY src_ip<\/span>
    \n<\/span>3.<\/b> table src_ip dest_ip<\/span>
    \n<\/span>4.<\/b> dedup src_ip<\/span><\/li>\n<\/ol>\n

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

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

    The <\/span>dc()<\/span> function calculates a distinct count, so repeated contacts with the same destination IP are counted only once. The search <\/span>stats dc(dest_ip) AS unique_destinations BY src_ip<\/span> therefore produces one result per source IP and reports how many different destination IP addresses each source contacted. A regular <\/span>count(dest_ip)<\/span> would count every populated destination occurrence, including repeated connections to the same address. <\/span>table<\/span> would simply show raw field values without aggregation, while <\/span>dedup src_ip<\/span> would retain only one event per source and remove useful destination information. Distinct counts are commonly used when analysts want to measure variety rather than total volume.<\/span><\/p>\n

    Question 342.<\/b><\/p>\n

    Which Splunk command is best suited for creating a table that shows total bytes by <\/b>host<\/b> with separate columns for each <\/b>application<\/b> value?<\/b><\/p>\n

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

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

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

      The <\/span>chart<\/span> command is designed to create cross-tabulated statistical results where one grouping field can define rows and another can become separate columns. For example, <\/span>chart sum(bytes) BY host application<\/span> can produce one row per host with application values represented across columns. This format is useful for reports and visualizations that compare several categories side by side. The <\/span>stats<\/span> command can aggregate by both fields, but its result is typically row-oriented rather than presented as a matrix. <\/span>eventstats<\/span> preserves event-level detail, and <\/span>dedup<\/span> only removes repeated values. For a structured comparison with one field spread across columns, <\/span>chart<\/span> is generally the most convenient choice.<\/span><\/p>\n

      Question 343.<\/b><\/p>\n

      An analyst wants to create a time-series visualization showing the average transaction duration for each service. Which command is most appropriate?<\/b><\/p>\n

        \n
      1. <\/b> chart avg(duration) BY service<\/span>
        \n<\/span>2.<\/b> stats avg(duration) BY service<\/span>
        \n<\/span>3.<\/b> timechart avg(duration) BY service<\/span>
        \n<\/span>4.<\/b> top duration BY service<\/span><\/li>\n<\/ol>\n

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

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

        The <\/span>timechart<\/span> command is specifically designed to calculate statistics over time and automatically uses <\/span>_time<\/span> as the time dimension. A search such as <\/span>timechart avg(duration) BY service<\/span> produces a separate time series for each service and calculates the average transaction duration within each time bucket. This makes the result directly suitable for a line or area chart. <\/span>stats<\/span> and <\/span>chart<\/span> can calculate the same average by service, but they do not automatically organize the output across time intervals. <\/span>top<\/span> ranks frequent values and does not calculate a time-series average. For trend analysis, <\/span>timechart<\/span> is the correct command.<\/span><\/p>\n

        Question 344.<\/b><\/p>\n

        Which SPL function should an analyst use to return the current time as epoch seconds?<\/b><\/p>\n

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

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

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

          The <\/span>now()<\/span> function returns the current time in epoch seconds and is commonly used inside <\/span>eval<\/span> expressions. For example, <\/span>eval age=now()-_time<\/span> calculates how many seconds have passed since each event occurred. This is useful when measuring event age, checking expiration conditions, or creating dynamic time-based classifications. <\/span>latest()<\/span> is a statistical function that returns the value associated with the most recent event in a group. <\/span>relative_time()<\/span> modifies an epoch timestamp, and <\/span>strftime()<\/span> converts epoch time into formatted text. Therefore, <\/span>now()<\/span> is the direct function for obtaining the current epoch time during search processing.<\/span><\/p>\n

          Question 345.<\/b><\/p>\n

          Which Splunk function can be used to move the current time back by 24 hours or snap a timestamp to a specific time boundary?<\/b><\/p>\n

            \n
          1. <\/b> relative_time()<\/span>
            \n<\/span>2.<\/b> strptime()<\/span>
            \n<\/span>3.<\/b> latest()<\/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 modifies an epoch timestamp using Splunk relative-time notation. It can move a time forward or backward, such as subtracting a day, or snap it to boundaries such as the beginning of an hour, day, or week. For example, <\/span>relative_time(now(),”-24h”)<\/span> produces the epoch value corresponding to 24 hours ago, while <\/span>relative_time(now(),”@d”)<\/span> snaps the current time to the beginning of the day. <\/span>strptime()<\/span> parses a text timestamp, <\/span>latest()<\/span> is an aggregation function, and <\/span>floor()<\/span> performs numeric rounding. For dynamic time shifting and snapping, <\/span>relative_time()<\/span> is the correct function.<\/span><\/p>\n

            Question 346.<\/b><\/p>\n

            Which Splunk function converts a formatted timestamp string into epoch time?<\/b><\/p>\n

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

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

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

              The <\/span>strptime()<\/span> function parses a date or time string according to a specified format and converts it into epoch time. Once converted, the timestamp can be compared numerically with <\/span>_time<\/span>, used in duration calculations, or passed into additional time functions. For example, a field containing <\/span>2026-09-26 10:30:00<\/span> could be converted using a matching format string. The <\/span>strftime()<\/span> function performs the reverse conversion by turning epoch values into formatted text. <\/span>now()<\/span> returns the current epoch time, and <\/span>tostring()<\/span> is a general conversion function. For parsing text into epoch form, <\/span>strptime()<\/span> is the correct choice.<\/span><\/p>\n

              Question 347.<\/b><\/p>\n

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

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

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

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

                The <\/span>strftime()<\/span> function converts an epoch timestamp into a human-readable string according to a specified format. This is useful when displaying dates and times in tables, reports, and dashboard results. For example, analysts can transform <\/span>_time<\/span> into a format such as year-month-day and hour-minute. The original epoch value can still be preserved in another field if it is needed for calculations. <\/span>strptime()<\/span> converts in the opposite direction, from formatted text to epoch. <\/span>relative_time()<\/span> adjusts epoch timestamps, while <\/span>now()<\/span> returns the current epoch value. Therefore, <\/span>strftime()<\/span> is the appropriate function for formatted time display.<\/span><\/p>\n

                Question 348.<\/b><\/p>\n

                Which Splunk command is best suited for creating an automatic geographical context from an IP address field?<\/b><\/p>\n

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

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

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

                  The <\/span>iplocation<\/span> command enriches IP address values with geographic information such as country, region, city, latitude, and longitude where such information is available. This can help analysts understand the geographic distribution of connections or activity and can provide fields that are later used in geographic visualizations. <\/span>geostats<\/span> aggregates events using geographic coordinates but does not itself convert an IP address into location information. A manually configured lookup could also enrich an IP address, but <\/span>iplocation<\/span> is the dedicated command for IP-based geolocation. <\/span>metadata<\/span> retrieves index metadata and is unrelated to geographic enrichment.<\/span><\/p>\n

                  Question 349.<\/b><\/p>\n

                  Which Splunk command is designed to perform statistical aggregation using latitude and longitude fields for map visualizations?<\/b><\/p>\n

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

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

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

                    The <\/span>geostats<\/span> command aggregates data using geographic coordinates such as latitude and longitude and produces output suitable for map-based visualizations. It is commonly used after <\/span>iplocation<\/span> or another method has created geographic fields. Analysts can use functions such as <\/span>count<\/span>, <\/span>sum<\/span>, or <\/span>avg<\/span> within <\/span>geostats<\/span> to summarize activity by geographic region. The <\/span>iplocation<\/span> command performs geographic enrichment but does not create the statistical map aggregation itself. <\/span>chart<\/span> is a general transforming command, while <\/span>xyseries<\/span> reshapes data into a matrix. When map-oriented geographic aggregation is needed, <\/span>geostats<\/span> is the appropriate Splunk command.<\/span><\/p>\n

                    Question 350.<\/b><\/p>\n

                    Which command is most appropriate for showing only the first 25 results after an analyst has sorted events by descending duration?<\/b><\/p>\n

                      \n
                    1. <\/b> top 25<\/span>
                      \n<\/span>2.<\/b> head 25<\/span>
                      \n<\/span>3.<\/b> tail 25<\/span>
                      \n<\/span>4.<\/b> dedup 25<\/span><\/li>\n<\/ol>\n

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

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

                      The <\/span>head<\/span> command returns the first specified number of results in their current order. If the search first uses <\/span>sort – duration<\/span>, then <\/span>head 25<\/span> keeps the 25 events with the highest duration values. This differs from <\/span>top<\/span>, which ranks values by frequency rather than numerical magnitude. <\/span>tail 25<\/span> would return the final 25 rows in the current ordering, which would correspond to lower duration values after a descending sort. <\/span>dedup<\/span> removes repeated values rather than limiting the result count. The combination of <\/span>sort<\/span> followed by <\/span>head<\/span> is a common pattern when the requirement is to return the highest or lowest individual values.<\/span><\/p>\n

                      Question 351.<\/b><\/p>\n

                      Which command would return the final 10 rows of the current result set?<\/b><\/p>\n

                        \n
                      1. <\/b> head 10<\/span>
                        \n<\/span>2.<\/b> top limit=10<\/span>
                        \n<\/span>3.<\/b> tail 10<\/span>
                        \n<\/span>4.<\/b> rare limit=10<\/span><\/li>\n<\/ol>\n

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

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

                        The <\/span>tail<\/span> command returns the last specified number of results in the current ordering. For example, <\/span>tail 10<\/span> retains the final 10 rows. The meaning of those rows depends on how the results are ordered at the point where the command runs. Analysts may use <\/span>sort<\/span> before <\/span>tail<\/span> if they need the ten lowest or oldest values according to a particular field. <\/span>head<\/span> returns the first results, while <\/span>top<\/span> and <\/span>rare<\/span> rank field values by frequency. Because <\/span>tail<\/span> simply limits the result set according to its existing order, it is the correct command for returning the final rows.<\/span><\/p>\n

                        Question 352.<\/b><\/p>\n

                        An analyst wants to convert a field containing <\/b>alpha|beta|gamma<\/b> into a multivalue field using the pipe symbol as the delimiter. Which function is most appropriate?<\/b><\/p>\n

                          \n
                        1. <\/b> mvjoin()<\/span>
                          \n<\/span>2.<\/b> mvappend()<\/span>
                          \n<\/span>3.<\/b> makemv only<\/span>
                          \n<\/span>4.<\/b> split()<\/span><\/li>\n<\/ol>\n

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

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

                          The <\/span>split()<\/span> function can be used within an <\/span>eval<\/span> expression to convert a delimited string into a multivalue field. For example, <\/span>eval values=split(values,”|”)<\/span> converts <\/span>alpha|beta|gamma<\/span> into three separate values in the same field. Once converted, the multivalue field can be processed using functions such as <\/span>mvcount()<\/span>, <\/span>mvindex()<\/span>, <\/span>mvjoin()<\/span>, or expanded into multiple result rows using <\/span>mvexpand<\/span>. <\/span>mvjoin()<\/span> performs the reverse transformation by combining multivalue elements into one string. <\/span>mvappend()<\/span> combines values, while <\/span>makemv<\/span> is a command rather than the requested function. For function-based splitting within <\/span>eval<\/span>, <\/span>split()<\/span> is the correct choice.<\/span><\/p>\n

                          Question 353.<\/b><\/p>\n

                          Which Splunk function can merge two or more values into one multivalue field?<\/b><\/p>\n

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

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

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

                            The <\/span>mvappend()<\/span> function combines multiple values or multivalue fields into one resulting multivalue field. This can be useful when related values originate from different fields but need to be processed together. After combining them, analysts can count the elements, retrieve specific positions, expand them into individual rows, or join them into a display string. <\/span>mvjoin()<\/span> converts a multivalue field into a single delimited string, while <\/span>split()<\/span> creates a multivalue field from a delimited string. <\/span>mvindex()<\/span> retrieves selected elements. Therefore, <\/span>mvappend()<\/span> is the proper function when the goal is to combine several values into one multivalue field.<\/span><\/p>\n

                            Question 354.<\/b><\/p>\n

                            Which Splunk function returns the number of elements in a multivalue field?<\/b><\/p>\n

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

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

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

                              The <\/span>mvcount()<\/span> function returns the number of elements contained in a multivalue field for the current event. For example, if a field contains three values, <\/span>mvcount()<\/span> returns 3. This differs from the statistical <\/span>count()<\/span> function, which counts events or populated values across multiple results. <\/span>dc()<\/span> counts distinct values across an aggregation group, while <\/span>values()<\/span> returns the distinct values themselves. Understanding this difference is important because multivalue functions operate within a field in a single result, while statistical functions generally operate across many events. When the requirement is to measure how many values are present inside one multivalue field, <\/span>mvcount()<\/span> is the correct function.<\/span><\/p>\n

                              Question 355.<\/b><\/p>\n

                              Which Splunk function should be used to return the second value from a multivalue field?<\/b><\/p>\n

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

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

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

                                The <\/span>mvindex()<\/span> function retrieves specific values from a multivalue field using positional indexes. Because indexing begins at zero, the second value is typically referenced with index <\/span>1<\/span>. For example, <\/span>mvindex(roles,1)<\/span> returns the second role stored in the <\/span>roles<\/span> field. Negative index values can also be used to retrieve elements from the end of the multivalue field. <\/span>mvcount()<\/span> only returns the number of values, <\/span>mvappend()<\/span> combines values, and <\/span>mvjoin()<\/span> turns the entire multivalue field into a string. Therefore, <\/span>mvindex()<\/span> is the appropriate choice for selecting a specific multivalue element by position.<\/span><\/p>\n

                                Question 356.<\/b><\/p>\n

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

                                  \n
                                1. <\/b> makemv<\/span>
                                  \n<\/span>2.<\/b> split<\/span>
                                  \n<\/span>3.<\/b> nomv<\/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 expands a multivalue field so that each element becomes a separate result row. The other fields from the original event are copied to each resulting row. This can make it easier to count, filter, group, or visualize individual values that were originally stored together. For example, one result with four roles becomes four results after expanding the role field. <\/span>makemv<\/span> and <\/span>split()<\/span> create multivalue fields rather than expand them, while <\/span>nomv<\/span> converts a multivalue field back into a single-value representation. Because <\/span>mvexpand<\/span> can multiply the number of results substantially, it should be used with care on large datasets.<\/span><\/p>\n

                                  Question 357.<\/b><\/p>\n

                                  Which command is most appropriate when an analyst wants to save current search results into a lookup table for future searches?<\/b><\/p>\n

                                    \n
                                  1. <\/b> outputlookup<\/span>
                                    \n<\/span>2.<\/b> inputlookup<\/span>
                                    \n<\/span>3.<\/b> collect<\/span>
                                    \n<\/span>4.<\/b> lookup<\/span><\/li>\n<\/ol>\n

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

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

                                    The <\/span>outputlookup<\/span> command writes current search results into a lookup table so they can be reused later. This is useful for generating reference datasets, maintaining lists of assets or users, storing intermediate analytical results, or creating temporary comparison tables. Analysts should understand the overwrite or append behavior associated with their chosen options because an existing lookup may be modified. <\/span>inputlookup<\/span> reads lookup data, while <\/span>lookup<\/span> enriches current events by matching fields against a lookup. <\/span>collect<\/span> writes search results into an index, typically for summary indexing. Therefore, <\/span>outputlookup<\/span> is the correct command when the destination should be a lookup table.<\/span><\/p>\n

                                    Question 358.<\/b><\/p>\n

                                    Which command is best suited for starting a search directly from the contents of a lookup table?<\/b><\/p>\n

                                      \n
                                    1. <\/b> lookup<\/span>
                                      \n<\/span>2.<\/b> inputlookup<\/span>
                                      \n<\/span>3.<\/b> outputlookup<\/span>
                                      \n<\/span>4.<\/b> appendcols<\/span><\/li>\n<\/ol>\n

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

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

                                      The <\/span>inputlookup<\/span> command loads lookup records directly into the search pipeline and makes them the current result set. This means analysts can begin with a reference table rather than indexed events and then apply commands such as <\/span>where<\/span>, <\/span>stats<\/span>, <\/span>eval<\/span>, or <\/span>table<\/span>. This is useful when reviewing lookup content, validating a reference dataset, or comparing a lookup with other information. The regular <\/span>lookup<\/span> command enriches existing search results by matching values, while <\/span>outputlookup<\/span> saves current results into a lookup. <\/span>appendcols<\/span> combines result columns from a subsearch. For using a lookup as the primary starting dataset, <\/span>inputlookup<\/span> is the correct command.<\/span><\/p>\n

                                      Question 359.<\/b><\/p>\n

                                      Which knowledge object is best suited for reusing a complex SPL expression with optional arguments across many searches?<\/b><\/p>\n

                                        \n
                                      1. <\/b> Field alias<\/span>
                                        \n<\/span>2.<\/b> Event type<\/span>
                                        \n<\/span>3.<\/b> Search macro<\/span>
                                        \n<\/span>4.<\/b> Tag<\/span><\/li>\n<\/ol>\n

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

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

                                        A search macro stores reusable SPL and can accept arguments, allowing the same logic to be applied in many searches with different values. This reduces duplication and makes maintenance easier because the central macro definition can be updated without editing every dependent search manually. Macros can encapsulate filters, calculations, field normalization, or other reusable portions of SPL. A field alias provides an alternate field name, an event type classifies events according to search criteria, and a tag adds a descriptive label. When the requirement is reusable, parameterized SPL logic, a search macro is the most appropriate Splunk knowledge object.<\/span><\/p>\n

                                        Question 360.<\/b><\/p>\n

                                        A frequently refreshed dashboard uses a large historical dataset, and several panels calculate the same metrics repeatedly. Which approach will usually improve performance most effectively?<\/b><\/p>\n

                                          \n
                                        1. <\/b> Increase the number of <\/span>join<\/span> commands<\/span>
                                          \n<\/span>2.<\/b> Broaden the base search<\/span>
                                          \n<\/span>3.<\/b> Use <\/span>transaction<\/span> for all panels<\/span>
                                          \n<\/span>4.<\/b> Use efficient base searches and appropriate acceleration, summaries, or <\/span>tstats<\/span> where supported<\/span><\/li>\n<\/ol>\n

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

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

                                          Repeatedly processing large historical datasets for identical or similar calculations can consume substantial Splunk resources. The dashboard should use selective base searches and take advantage of optimized approaches when appropriate. These may include summary indexing, accelerated data models, <\/span>tstats<\/span>, shared base searches, or other precomputed strategies. The right choice depends on required fields, freshness, data volume, and dashboard design. Commands such as <\/span>join<\/span> and <\/span>transaction<\/span> can be expensive and should not be added merely to improve performance. Broadening a search would generally increase workload rather than reduce it. Optimizing the search architecture and avoiding repeated raw-event processing is usually the most effective way to improve dashboard responsiveness.<\/span><\/p>\n

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

                                          View Full Splunk SPLK-1004 Exam Dumps and Practice Test Dumps   Question 341. An analyst wants to identify the number of unique destination IP addresses contacted by each source IP. Which SPL is most appropriate? stats dc(dest_ip) AS unique_destinations BY src_ip 2. stats count(dest_ip) AS unique_destinations BY src_ip 3. table src_ip dest_ip 4. dedup src_ip […]<\/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\/22953"}],"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=22953"}],"version-history":[{"count":1,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/22953\/revisions"}],"predecessor-version":[{"id":22954,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/22953\/revisions\/22954"}],"wp:attachment":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/media?parent=22953"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/categories?post=22953"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/tags?post=22953"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}