{"id":22943,"date":"2026-09-26T09:55:08","date_gmt":"2026-09-26T09:55:08","guid":{"rendered":"https:\/\/www.examlabs.com\/certification\/?p=22943"},"modified":"2026-09-26T09:55:08","modified_gmt":"2026-09-26T09:55:08","slug":"splunk-splk-1004-practice-test-questions-and-exam-dumps-part13-q241-260","status":"publish","type":"post","link":"https:\/\/www.examlabs.com\/certification\/splunk-splk-1004-practice-test-questions-and-exam-dumps-part13-q241-260\/","title":{"rendered":"Splunk SPLK-1004 Practice Test Questions and Exam Dumps Part13 Q241-260"},"content":{"rendered":"

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

 <\/p>\n

Question 241.<\/b><\/p>\n

Which Splunk search command is most appropriate when an analyst wants to calculate the number of events for each combination of <\/b>host<\/b> and <\/b>status<\/b>?<\/b><\/p>\n

    \n
  1. <\/b> stats count BY host status<\/span>
    \n<\/span>2.<\/b> table host status<\/span>
    \n<\/span>3.<\/b> dedup host status<\/span>
    \n<\/span>4.<\/b> sort host status<\/span><\/li>\n<\/ol>\n

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

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

    The <\/span>stats<\/span> command performs statistical aggregation and can group results by one or more fields. The search <\/span>stats count BY host status<\/span> produces one row for every unique combination of <\/span>host<\/span> and <\/span>status<\/span>, together with the number of events belonging to that combination. This is a common SPL pattern when analyzing event distributions across multiple dimensions. The <\/span>table<\/span> command would simply display fields without aggregation, while <\/span>dedup<\/span> would remove duplicate combinations rather than count them. <\/span>sort<\/span> only changes result order. Therefore, <\/span>stats<\/span> is the most appropriate command when grouped counts are required.<\/span><\/p>\n

    Question 242.<\/b><\/p>\n

    An analyst wants to preserve every original event while adding the total event count for each user to the corresponding events. Which command should be used?<\/b><\/p>\n

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

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

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

      The <\/span>eventstats<\/span> command calculates aggregate statistics and then writes the calculated values back to the original events. For example, <\/span>eventstats count AS user_events BY user<\/span> adds a <\/span>user_events<\/span> field to every event for that user. This allows analysts to retain event-level detail while also having group-level context available for filtering or comparison. The regular <\/span>stats<\/span> command would collapse the events into one row per user, which would remove the original event details. <\/span>timechart<\/span> is designed for time-based aggregation, while <\/span>top<\/span> identifies the most common values rather than attaching aggregates to individual events.<\/span><\/p>\n

      Question 243.<\/b><\/p>\n

      Which Splunk function can return the first value associated with the earliest event time within each statistical group?<\/b><\/p>\n

        \n
      1. <\/b> min()<\/span>
        \n<\/span>2.<\/b> first()<\/span>
        \n<\/span>3.<\/b> earliest()<\/span>
        \n<\/span>4.<\/b> initial()<\/span><\/li>\n<\/ol>\n

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

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

        The <\/span>earliest()<\/span> statistical function returns the value of a specified field from the earliest event in the group based on event time. This is useful when analysts need to determine the initial state, first user, first action, or first observed value associated with an entity. It is important to distinguish <\/span>earliest()<\/span> from <\/span>min()<\/span>. The <\/span>min()<\/span> function returns the smallest value numerically or lexicographically, regardless of when the event occurred. In timeline-oriented investigations, <\/span>earliest()<\/span> is the appropriate function when the chronological first occurrence is what matters.<\/span><\/p>\n

        Question 244.<\/b><\/p>\n

        Which Splunk function should be used to retrieve the field value associated with the most recent event in each group?<\/b><\/p>\n

          \n
        1. <\/b> max()<\/span>
          \n<\/span>2.<\/b> last()<\/span>
          \n<\/span>3.<\/b> current()<\/span>
          \n<\/span>4.<\/b> latest()<\/span><\/li>\n<\/ol>\n

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

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

          The <\/span>latest()<\/span> function returns the field value associated with the most recent event according to event time. It is commonly used with <\/span>stats<\/span> when analysts want to determine the latest status, state, owner, or observed value for an entity. For example, <\/span>stats latest(status) BY host<\/span> can return the most recently observed status for each host. This differs from <\/span>max()<\/span>, which simply returns the greatest value. A field’s highest value is not necessarily its most recent one. Therefore, <\/span>latest()<\/span> is the correct choice when recency rather than magnitude is the requirement.<\/span><\/p>\n

          Question 245.<\/b><\/p>\n

          Which Splunk command is designed to display statistical values over time and automatically use <\/b>_time<\/b> as the time dimension?<\/b><\/p>\n

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

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

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

            The <\/span>timechart<\/span> command is specifically designed for producing time-based statistical results. It automatically uses <\/span>_time<\/span> as the x-axis or time dimension and groups events into time buckets. Analysts can specify functions such as <\/span>count<\/span>, <\/span>avg<\/span>, <\/span>sum<\/span>, or percentiles and can separate series using a <\/span>BY<\/span> field. This makes <\/span>timechart<\/span> particularly suitable for trend charts and dashboards. Although <\/span>stats<\/span> and <\/span>chart<\/span> can also calculate aggregates, they do not automatically organize results into time intervals in the same way. <\/span>table<\/span> only formats fields and does not perform time aggregation.<\/span><\/p>\n

            Question 246.<\/b><\/p>\n

            Which SPL pattern is best for calculating the average response time for each application?<\/b><\/p>\n

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

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

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

              The search <\/span>stats avg(response_time) BY application<\/span> calculates the arithmetic mean of the <\/span>response_time<\/span> field separately for each application. The <\/span>stats<\/span> command performs the aggregation, the <\/span>avg()<\/span> function calculates the mean, and the <\/span>BY application<\/span> clause creates one result row per application. A <\/span>table<\/span> command would show raw values without calculating averages, while <\/span>dedup<\/span> would remove duplicates. <\/span>top<\/span> is designed to identify frequent values rather than calculate averages. This <\/span>stats<\/span> pattern is one of the most common methods for comparing numerical performance metrics across categories in Splunk.<\/span><\/p>\n

              Question 247.<\/b><\/p>\n

              Which command is most suitable for returning the 10 most frequently observed source IP addresses?<\/b><\/p>\n

                \n
              1. <\/b> rare src_ip limit=10<\/span>
                \n<\/span>2.<\/b> stats dc(src_ip)<\/span>
                \n<\/span>3.<\/b> top src_ip limit=10<\/span>
                \n<\/span>4.<\/b> head 10 src_ip<\/span><\/li>\n<\/ol>\n

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

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

                The <\/span>top<\/span> command returns the most frequently occurring values of a field and typically includes their counts and percentages. Using <\/span>top src_ip limit=10<\/span> returns the ten source IP addresses that appear most frequently in the result set. The <\/span>rare<\/span> command does the opposite by returning least common values. <\/span>dc(src_ip)<\/span> returns only the number of unique source IPs rather than ranking them, while <\/span>head<\/span> simply returns the first rows according to current result order. Therefore, <\/span>top<\/span> is the most direct and appropriate command for this frequency-based requirement.<\/span><\/p>\n

                Question 248.<\/b><\/p>\n

                Which Splunk command returns the least frequently occurring values of a field?<\/b><\/p>\n

                  \n
                1. <\/b> top<\/span>
                  \n<\/span>2.<\/b> sort<\/span>
                  \n<\/span>3.<\/b> dedup<\/span>
                  \n<\/span>4.<\/b> rare<\/span><\/li>\n<\/ol>\n

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

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

                  The <\/span>rare<\/span> command identifies the least frequently occurring values in one or more fields, usually returning count and percentage information as well. It is often used during exploratory analysis to identify unusual or uncommon values such as rare user agents, hosts, process names, or error codes. The <\/span>top<\/span> command identifies the most frequent values instead. <\/span>sort<\/span> only changes the order of existing results, while <\/span>dedup<\/span> removes repeated values. Because rarity can sometimes highlight unexpected behavior, the <\/span>rare<\/span> command is useful as a starting point for anomaly investigation, though a rare value is not automatically suspicious.<\/span><\/p>\n

                  Question 249.<\/b><\/p>\n

                  Which Splunk command can be used to remove duplicate results based on a field such as <\/b>session_id<\/b>?<\/b><\/p>\n

                    \n
                  1. <\/b> dedup<\/span>
                    \n<\/span>2.<\/b> distinct<\/span>
                    \n<\/span>3.<\/b> uniqfield<\/span>
                    \n<\/span>4.<\/b> stats only<\/span><\/li>\n<\/ol>\n

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

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

                    The <\/span>dedup<\/span> command removes duplicate search results according to one or more specified fields. For example, <\/span>dedup session_id<\/span> keeps one result for each unique session ID. The event that is retained depends on the current result order, so analysts may sort the data first when they need the newest or oldest event for each unique value. <\/span>dedup<\/span> does not remove events from the underlying Splunk index; it only affects the current search output. This makes it useful for producing unique entity lists or selecting one representative event per identifier.<\/span><\/p>\n

                    Question 250.<\/b><\/p>\n

                    An analyst needs to compare the field <\/b>response_time<\/b> with a calculated field named <\/b>threshold<\/b> and keep only events where the response time is greater. Which command is most appropriate?<\/b><\/p>\n

                      \n
                    1. <\/b> search<\/span>
                      \n<\/span>2.<\/b> where<\/span>
                      \n<\/span>3.<\/b> table<\/span>
                      \n<\/span>4.<\/b> rename<\/span><\/li>\n<\/ol>\n

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

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

                      The <\/span>where<\/span> command is ideal for field-to-field comparisons and other expression-based filtering. A search such as <\/span>| where response_time > threshold<\/span> evaluates the two field values for each event and keeps only those where the condition is true. Standard <\/span>search<\/span> syntax is excellent for many direct field-value conditions but is less natural for comparisons between two fields. <\/span>table<\/span> simply formats selected fields, and <\/span>rename<\/span> changes field names. Because <\/span>where<\/span> uses eval-style expressions, it is also useful for arithmetic, Boolean logic, and function-based conditions.<\/span><\/p>\n

                      Question 251.<\/b><\/p>\n

                      Which Splunk function can be used to determine whether a field value begins with a specific pattern using a regular expression?<\/b><\/p>\n

                        \n
                      1. <\/b> like()<\/span>
                        \n<\/span>2.<\/b> replace()<\/span>
                        \n<\/span>3.<\/b> match()<\/span>
                        \n<\/span>4.<\/b> substr()<\/span><\/li>\n<\/ol>\n

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

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

                        The <\/span>match()<\/span> function evaluates a field value against a regular expression and returns a Boolean result. For example, <\/span>match(user,”^svc_”)<\/span> returns true when the username begins with the prefix <\/span>svc_<\/span>. This function is useful inside <\/span>eval<\/span>, <\/span>where<\/span>, <\/span>if()<\/span>, and <\/span>case()<\/span> expressions. The <\/span>like()<\/span> function supports SQL-style wildcard matching rather than regular expressions, while <\/span>replace()<\/span> modifies string content. <\/span>substr()<\/span> extracts text by position. When the requirement specifically calls for regular-expression pattern testing, <\/span>match()<\/span> is the appropriate function.<\/span><\/p>\n

                        Question 252.<\/b><\/p>\n

                        Which Splunk function should be used to test whether a string contains a pattern using <\/b>%<\/b> as a wildcard?<\/b><\/p>\n

                          \n
                        1. <\/b> match()<\/span>
                          \n<\/span>2.<\/b> regex()<\/span>
                          \n<\/span>3.<\/b> searchmatch()<\/span>
                          \n<\/span>4.<\/b> like()<\/span><\/li>\n<\/ol>\n

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

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

                          The <\/span>like()<\/span> function supports SQL-style pattern matching. The percent sign <\/span>%<\/span> represents zero or more characters, while an underscore <\/span>_<\/span> can represent a single character. For example, <\/span>like(message,”%timeout%”)<\/span> evaluates to true when the field contains the text <\/span>timeout<\/span> anywhere in the value. This is simpler than writing a regular expression when only straightforward wildcard matching is needed. The <\/span>match()<\/span> function is more appropriate for regular expressions. <\/span>like()<\/span> is commonly used in <\/span>where<\/span>, <\/span>eval<\/span>, <\/span>if()<\/span>, or <\/span>case()<\/span> expressions where Boolean pattern testing is required.<\/span><\/p>\n

                          Question 253.<\/b><\/p>\n

                          Which Splunk command is best for converting a comma-separated field into multiple separate results, one for each value?<\/b><\/p>\n

                            \n
                          1. <\/b> Use <\/span>makemv<\/span> and then <\/span>mvexpand<\/span>
                            \n<\/span>2.<\/b> Use <\/span>table<\/span> and then <\/span>dedup<\/span>
                            \n<\/span>3.<\/b> Use <\/span>stats<\/span> and then <\/span>sort<\/span>
                            \n<\/span>4.<\/b> Use <\/span>rename<\/span> and then <\/span>fields<\/span><\/li>\n<\/ol>\n

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

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

                            A comma-separated field is initially a single string. The <\/span>makemv<\/span> command can convert the string into a multivalue field by splitting on the specified delimiter. After that, <\/span>mvexpand<\/span> creates a separate result for each value in the multivalue field. For example, a field containing <\/span>admin,user,auditor<\/span> can become three separate results. This combination is useful when analysts need to count or analyze each element independently. <\/span>table<\/span>, <\/span>dedup<\/span>, <\/span>stats<\/span>, and <\/span>rename<\/span> do not provide the same two-step conversion from delimited text to individual result rows.<\/span><\/p>\n

                            Question 254.<\/b><\/p>\n

                            Which function returns the number of values stored 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 each event. For example, <\/span>eval role_count=mvcount(roles)<\/span> creates a field that indicates how many values exist in <\/span>roles<\/span>. This differs from <\/span>count()<\/span>, which is generally used in statistical aggregations to count events or populated field values across events. The <\/span>dc()<\/span> function counts distinct values across an aggregation group, while <\/span>values()<\/span> returns the unique values themselves. <\/span>mvcount()<\/span> is specifically designed for inspecting the size of a multivalue field within an individual result.<\/span><\/p>\n

                              Question 255.<\/b><\/p>\n

                              Which function can combine values from two multivalue fields into a single multivalue field?<\/b><\/p>\n

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

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

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

                                The <\/span>mvappend()<\/span> function combines multiple values or multivalue fields into one multivalue field. For example, an analyst can merge values from two related fields and then process the combined set using functions such as <\/span>mvcount()<\/span>, <\/span>mvindex()<\/span>, or <\/span>mvjoin()<\/span>. The <\/span>mvjoin()<\/span> function converts a multivalue field into a single delimited string, while <\/span>split()<\/span> converts a delimited string into a multivalue field. <\/span>mvindex()<\/span> retrieves specific values by position. Therefore, <\/span>mvappend()<\/span> is the correct choice when multiple value sets need to be combined.<\/span><\/p>\n

                                Question 256.<\/b><\/p>\n

                                Which command should be used when an analyst wants to write current search results into a summary index for later analysis?<\/b><\/p>\n

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

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

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

                                  The <\/span>collect<\/span> command writes search results into a Splunk index and is commonly associated with summary indexing. This can be useful when expensive searches are run periodically and their summarized results need to be stored for faster future reporting or dashboards. Summary indexing reduces the need to repeatedly process large quantities of raw historical data. <\/span>outputlookup<\/span> writes tabular search results to a lookup rather than an index, while <\/span>inputlookup<\/span> reads lookup data. <\/span>append<\/span> simply adds results from another search. Therefore, <\/span>collect<\/span> is the command intended for writing results into a summary index.<\/span><\/p>\n

                                  Question 257.<\/b><\/p>\n

                                  Which Splunk command can retrieve information such as event counts and first or last reporting times for hosts or sourcetypes without performing a normal raw-event search?<\/b><\/p>\n

                                    \n
                                  1. <\/b> metadata<\/span>
                                    \n<\/span>2.<\/b> fieldsummary<\/span>
                                    \n<\/span>3.<\/b> table<\/span>
                                    \n<\/span>4.<\/b> transaction<\/span><\/li>\n<\/ol>\n

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

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

                                    The <\/span>metadata<\/span> command retrieves information from Splunk’s index metadata for hosts, sources, or sourcetypes. It can provide values such as total event counts, first observed time, and last observed time without retrieving full raw events. This makes it efficient for checking data-source activity and identifying hosts or feeds that may have stopped reporting. <\/span>fieldsummary<\/span> describes fields in a current result set, while <\/span>table<\/span> only formats data. <\/span>transaction<\/span> groups related events. Because the information is already available in index metadata, the <\/span>metadata<\/span> command is often a faster choice for source-health checks.<\/span><\/p>\n

                                    Question 258.<\/b><\/p>\n

                                    Which command provides descriptive information about fields in the current result set, including distinct counts and sample values?<\/b><\/p>\n

                                      \n
                                    1. <\/b> metadata<\/span>
                                      \n<\/span>2.<\/b> fieldsummary<\/span>
                                      \n<\/span>3.<\/b> stats<\/span>
                                      \n<\/span>4.<\/b> fields<\/span><\/li>\n<\/ol>\n

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

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

                                      The <\/span>fieldsummary<\/span> command produces descriptive statistics and metadata for fields in the current result set. It can show information such as distinct counts, null counts, numerical properties, and example values. This is particularly useful when analysts are exploring unfamiliar data and want to understand which fields are available and how those fields behave. The <\/span>metadata<\/span> command focuses specifically on indexed hosts, sources, or sourcetypes. <\/span>stats<\/span> performs explicit aggregation chosen by the user, while <\/span>fields<\/span> only controls which fields remain available. <\/span>fieldsummary<\/span> is therefore a valuable exploratory command for understanding data structure.<\/span><\/p>\n

                                      Question 259.<\/b><\/p>\n

                                      Which Splunk knowledge object is best when a derived field should be created automatically at search time using an <\/b>eval<\/b> expression?<\/b><\/p>\n

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

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

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

                                        A calculated field is a knowledge object that automatically creates a derived field at search time using an <\/span>eval<\/span> expression. It is appropriate when the same calculation is required repeatedly for a particular set of data. Instead of adding the same <\/span>eval<\/span> command manually to every search, the calculated field can be defined once and made available according to its scope and permissions. Search macros are better for reusable SPL fragments, event types categorize matching events, and tags provide labels. Therefore, calculated fields are the best choice when the requirement is a reusable derived field rather than reusable search logic.<\/span><\/p>\n

                                        Question 260.<\/b><\/p>\n

                                        A high-volume dashboard repeatedly performs grouped statistical analysis on data represented by an accelerated data model. Which Splunk command is generally the most efficient choice when the required fields are supported?<\/b><\/p>\n

                                          \n
                                        1. <\/b> transaction<\/span>
                                          \n<\/span>2.<\/b> map<\/span>
                                          \n<\/span>3.<\/b> join<\/span>
                                          \n<\/span>4.<\/b> tstats<\/span><\/li>\n<\/ol>\n

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

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

                                          The <\/span>tstats<\/span> command performs statistical searches using indexed fields and accelerated data-model summaries. When the required fields are available through the data model, <\/span>tstats<\/span> can be significantly faster than conventional searches that retrieve and parse large volumes of raw events. This makes it especially useful for high-volume dashboards, Common Information Model-aligned reporting, and other repeated analytical workloads. Commands such as <\/span>transaction<\/span>, <\/span>map<\/span>, and <\/span>join<\/span> can be much more expensive and are designed for different purposes. Although <\/span>tstats<\/span> cannot replace every standard SPL search, it is typically the preferred option when accelerated data and compatible fields are available.<\/span><\/p>\n

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

                                          View Full Splunk SPLK-1004 Exam Dumps and Practice Test Dumps   Question 241. Which Splunk search command is most appropriate when an analyst wants to calculate the number of events for each combination of host and status? stats count BY host status 2. table host status 3. dedup host status 4. sort host status Correct […]<\/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\/22943"}],"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=22943"}],"version-history":[{"count":1,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/22943\/revisions"}],"predecessor-version":[{"id":22944,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/22943\/revisions\/22944"}],"wp:attachment":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/media?parent=22943"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/categories?post=22943"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/tags?post=22943"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}