{"id":23338,"date":"2026-09-28T05:08:20","date_gmt":"2026-09-28T05:08:20","guid":{"rendered":"https:\/\/www.examlabs.com\/certification\/?p=23338"},"modified":"2026-09-28T05:08:20","modified_gmt":"2026-09-28T05:08:20","slug":"tableau-tda-c01-practice-test-questions-and-exam-dumps-part10-q181-20","status":"publish","type":"post","link":"https:\/\/www.examlabs.com\/certification\/tableau-tda-c01-practice-test-questions-and-exam-dumps-part10-q181-20\/","title":{"rendered":"Tableau TDA-C01 Practice Test Questions and Exam Dumps Part10 Q181-20"},"content":{"rendered":"

View Full <\/b>Tableau TDA-C01 Exam Dumps<\/b><\/a> and Practice Test Dumps<\/b><\/h2>\n

 <\/p>\n

Question 181.<\/b><\/p>\n

An analyst wants to compare Sales and Profit across products while also identifying outliers and potential relationships between the two measures. Which visualization is most appropriate?<\/b><\/p>\n

    \n
  1. <\/b> Scatter plot<\/span>
    \n<\/span>2.<\/b> Highlight table<\/span>
    \n<\/span>3.<\/b> Histogram<\/span>
    \n<\/span>4.<\/b> Pie chart<\/span><\/li>\n<\/ol>\n

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

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

    A scatter plot is ideal for examining the relationship between two continuous measures. Placing Sales on one axis and Profit on the other creates one position for each product or other selected dimension member. The analyst can then identify clusters, positive or negative relationships, and unusual outliers. Additional fields can be placed on Color, Size, Detail, or Tooltip to provide more context. A highlight table compares values across categorical intersections, while a histogram shows the distribution of a single continuous measure. Pie charts are more suitable for simple part-to-whole analysis and are less effective for studying relationships between two quantitative variables.<\/span><\/p>\n

    Question 182.<\/b><\/p>\n

    Which Tableau feature can be added to a scatter plot to help estimate the statistical relationship between Sales and Profit?<\/b><\/p>\n

      \n
    1. <\/b> Reference band<\/span>
      \n<\/span>2.<\/b> Trend line<\/span>
      \n<\/span>3.<\/b> Pages shelf<\/span>
      \n<\/span>4.<\/b> Bin<\/span><\/li>\n<\/ol>\n

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

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

      A trend line models the relationship between measures shown in a visualization, especially a scatter plot. Tableau can fit supported models and display information that helps analysts assess the direction and strength of the relationship. For example, a trend line can indicate whether Profit generally increases as Sales increase. However, analysts should not interpret correlation as proof of causation. A reference band displays a range on an axis, the Pages shelf allows users to step through field values, and bins group continuous numeric values into discrete ranges. When the goal is to analyze the general association between two measures, a trend line is the appropriate analytics feature.<\/span><\/p>\n

      Question 183.<\/b><\/p>\n

      An analyst wants to identify how many orders fall into Sales ranges of 0\u2013100, 100\u2013200, and 200\u2013300. Which Tableau combination is most appropriate?<\/b><\/p>\n

        \n
      1. <\/b> Group and pie chart<\/span>
        \n<\/span>2.<\/b> Set and line chart<\/span>
        \n<\/span>3.<\/b> Bin and histogram<\/span>
        \n<\/span>4.<\/b> Hierarchy and map<\/span><\/li>\n<\/ol>\n

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

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

        Bins divide a continuous numeric measure into ranges of a specified size. A histogram then displays the frequency of records that fall into those ranges. For example, creating a Sales bin with a size of 100 can produce ranges such as 0\u201399, 100\u2013199, and 200\u2013299 depending on the values and boundaries. A histogram allows the analyst to see the overall distribution, including concentration, skew, and unusual values. Groups combine dimension members, sets define subsets, and hierarchies enable drill-down. For examining how frequently observations occur within numeric intervals, the combination of bins and a histogram is the natural Tableau solution.<\/span><\/p>\n

        Question 184.<\/b><\/p>\n

        Which Tableau chart is most appropriate for comparing actual Sales against a target and showing performance ranges such as poor, acceptable, and excellent?<\/b><\/p>\n

          \n
        1. <\/b> Scatter plot<\/span>
          \n<\/span>2.<\/b> Filled map<\/span>
          \n<\/span>3.<\/b> Pie chart<\/span>
          \n<\/span>4.<\/b> Bullet graph<\/span><\/li>\n<\/ol>\n

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

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

          A bullet graph is designed for performance-versus-target analysis. It can display an actual measure, a target or reference value, and qualitative ranges that indicate performance levels such as poor, acceptable, and excellent. Bullet graphs are compact and often more effective than gauges because they communicate performance against a benchmark with less visual space. Scatter plots compare quantitative relationships, filled maps display geographic measures, and pie charts show composition. When a dashboard needs to communicate both current performance and how that performance compares with a target and meaningful ranges, a bullet graph is usually an appropriate visualization choice.<\/span><\/p>\n

          Question 185.<\/b><\/p>\n

          An analyst wants to display a target line at $500,000 Sales across a bar chart. Which Tableau feature should be used?<\/b><\/p>\n

            \n
          1. <\/b> Reference line<\/span>
            \n<\/span>2.<\/b> Set action<\/span>
            \n<\/span>3.<\/b> Extract filter<\/span>
            \n<\/span>4.<\/b> Forecast<\/span><\/li>\n<\/ol>\n

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

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

            A reference line adds a benchmark value directly to a visualization. It can be based on a constant, average, median, parameter, or other supported value. In this case, a constant reference line at $500,000 allows users to immediately compare each bar with the target. Tableau also lets analysts control the scope of the line, such as the whole table, pane, or cell, depending on the view. A set action changes set membership, an extract filter controls what data is stored in an extract, and forecasting estimates future values. For showing a fixed benchmark across a chart, a reference line is the appropriate feature.<\/span><\/p>\n

            Question 186.<\/b><\/p>\n

            Which Tableau feature should be used when an analyst wants to display a shaded band between a lower target of $400,000 and an upper target of $600,000?<\/b><\/p>\n

              \n
            1. <\/b> Forecast<\/span>
              \n<\/span>2.<\/b> Reference band<\/span>
              \n<\/span>3.<\/b> Group<\/span>
              \n<\/span>4.<\/b> Trend line<\/span><\/li>\n<\/ol>\n

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

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

              A reference band shades the area between two values on an axis. This makes it useful for showing acceptable operating ranges, target zones, confidence-like thresholds, or tolerance limits. In this example, the analyst can define a lower boundary of $400,000 and an upper boundary of $600,000. Viewers can then quickly see which marks fall below, within, or above the preferred range. A reference line represents one benchmark rather than a bounded interval. Forecasting predicts future values, groups combine members, and trend lines model relationships. For a visual range with lower and upper boundaries, a reference band is the correct Tableau feature.<\/span><\/p>\n

              Question 187.<\/b><\/p>\n

              Which Tableau calculation would return the total Sales for each Region regardless of whether Sub-Category is added to the view?<\/b><\/p>\n

                \n
              1. <\/b> RUNNING_SUM(SUM([Sales]))<\/span>
                \n<\/span>2.<\/b> { INCLUDE [Sub-Category] : SUM([Sales]) }<\/span>
                \n<\/span>3.<\/b> { FIXED [Region] : SUM([Sales]) }<\/span>
                \n<\/span>4.<\/b> RANK(SUM([Sales]))<\/span><\/li>\n<\/ol>\n

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

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

                A FIXED level-of-detail expression calculates an aggregation using the dimensions explicitly specified in the expression. <\/span>{ FIXED [Region] : SUM([Sales]) }<\/span> therefore calculates regional Sales totals at the Region level, regardless of whether additional dimensions such as Sub-Category are displayed in the visualization. The result can still be affected by certain earlier filters, especially context filters, according to Tableau\u2019s order of operations. INCLUDE adds dimensions to the calculation level, while RUNNING_SUM and RANK are table calculations based on marks in the view. FIXED is therefore the correct approach when the calculation should remain at a defined Region-level granularity.<\/span><\/p>\n

                Question 188.<\/b><\/p>\n

                A view contains Region and Customer Name, but the analyst wants to calculate a Region-level total that ignores Customer Name. Which LOD expression type is most suitable?<\/b><\/p>\n

                  \n
                1. <\/b> INCLUDE<\/span>
                  \n<\/span>2.<\/b> FIXED only<\/span>
                  \n<\/span>3.<\/b> Table calculation<\/span>
                  \n<\/span>4.<\/b> EXCLUDE<\/span><\/li>\n<\/ol>\n

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

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

                  An EXCLUDE LOD expression removes one or more dimensions from the level of detail used for a calculation. If both Region and Customer Name are displayed but the analyst wants Sales calculated at the Region level, an expression that excludes Customer Name can return the broader regional total while preserving customer-level marks in the view. INCLUDE would add dimensions rather than remove them. FIXED could also define a Region-level calculation explicitly, but when the intent is specifically to ignore a dimension already present in the view, EXCLUDE directly expresses that requirement. This is useful when comparing detailed members with their parent-level totals.<\/span><\/p>\n

                  Question 189.<\/b><\/p>\n

                  Which LOD expression type allows an analyst to calculate at a finer level of detail than the dimensions currently shown in the view?<\/b><\/p>\n

                    \n
                  1. <\/b> INCLUDE<\/span>
                    \n<\/span>2.<\/b> EXCLUDE<\/span>
                    \n<\/span>3.<\/b> FIXED only<\/span>
                    \n<\/span>4.<\/b> RANK<\/span><\/li>\n<\/ol>\n

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

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

                    INCLUDE LOD expressions add one or more dimensions to the calculation’s level of detail, even when those dimensions are not displayed in the visualization. For example, a Region-level view can include Customer ID in an LOD expression so Tableau performs a customer-level calculation before aggregating the result back to Region. This is useful for metrics such as average customer sales, where the intermediate calculation must happen at a finer grain than the visible view. EXCLUDE removes dimensions, while FIXED defines an explicit level that is more independent of the current view. RANK is a table calculation rather than an LOD expression.<\/span><\/p>\n

                    Question 190.<\/b><\/p>\n

                    An analyst wants to display each Category’s Sales as a percentage of total Sales. Which quick table calculation should be used?<\/b><\/p>\n

                      \n
                    1. <\/b> Running Total<\/span>
                      \n<\/span>2.<\/b> Percent of Total<\/span>
                      \n<\/span>3.<\/b> Difference<\/span>
                      \n<\/span>4.<\/b> Rank<\/span><\/li>\n<\/ol>\n

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

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

                      Percent of Total calculates each mark’s value as a share of the total value within the current table-calculation partition. Applied to SUM(Sales) by Category, it can show the percentage of total Sales attributable to each category. Analysts should verify the Compute Using setting because the denominator depends on the partitioning and addressing of the calculation. Running Total accumulates values over marks, Difference compares one mark with another, and Rank assigns positions based on measure values. Percent of Total is commonly used in bar charts, text tables, and other views where viewers need to understand relative contribution rather than only absolute values.<\/span><\/p>\n

                      Question 191.<\/b><\/p>\n

                      Which quick table calculation should be used to display cumulative Sales over a sequence of months?<\/b><\/p>\n

                        \n
                      1. <\/b> Rank<\/span>
                        \n<\/span>2.<\/b> Difference<\/span>
                        \n<\/span>3.<\/b> Running Total<\/span>
                        \n<\/span>4.<\/b> Percent of Total<\/span><\/li>\n<\/ol>\n

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

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

                        Running Total accumulates a measure as Tableau moves through the addressing dimension. If Month is arranged chronologically, applying Running Total to SUM(Sales) displays cumulative sales through each month. This is useful for year-to-date performance, progress tracking, and cumulative growth analysis. The analyst should verify the Compute Using configuration so Tableau accumulates across Month and resets the total at the correct partition boundaries, such as Year if multiple years are shown. Difference shows period-to-period changes, Rank assigns an ordering, and Percent of Total calculates relative contribution. For cumulative values across time, Running Total is the appropriate quick table calculation.<\/span><\/p>\n

                        Question 192.<\/b><\/p>\n

                        An analyst wants to retrieve the Sales value from the immediately previous month within a table calculation. Which function is most appropriate?<\/b><\/p>\n

                          \n
                        1. <\/b> INDEX()<\/span>
                          \n<\/span>2.<\/b> SIZE()<\/span>
                          \n<\/span>3.<\/b> WINDOW_SUM()<\/span>
                          \n<\/span>4.<\/b> LOOKUP()<\/span><\/li>\n<\/ol>\n

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

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

                          LOOKUP()<\/span> returns the value of an expression from another row at a specified relative offset in the current table-calculation partition. For example, <\/span>LOOKUP(SUM([Sales]),-1)<\/span> can return the Sales value from the preceding month when the calculation is addressed across Month in chronological order. This is useful for custom period-over-period calculations, difference logic, and comparisons with prior values. INDEX returns sequential position, SIZE returns the number of marks in the partition, and WINDOW_SUM aggregates across a range of marks. When the requirement is to retrieve the value from a neighboring mark directly, LOOKUP is the appropriate table-calculation function.<\/span><\/p>\n

                          Question 193.<\/b><\/p>\n

                          Which Tableau function returns the number of marks in the current table-calculation partition?<\/b><\/p>\n

                            \n
                          1. <\/b> SIZE()<\/span>
                            \n<\/span>2.<\/b> INDEX()<\/span>
                            \n<\/span>3.<\/b> FIRST()<\/span>
                            \n<\/span>4.<\/b> RANK()<\/span><\/li>\n<\/ol>\n

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

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

                            SIZE()<\/span> returns the number of marks or rows in the current table-calculation partition. It is useful in advanced calculations that depend on partition length, such as custom pagination logic, conditional labeling, or determining whether a partition contains enough observations for a calculation. The result can change when dimensions are added or removed from the view or when the Compute Using configuration changes. <\/span>INDEX()<\/span> returns the current mark\u2019s sequential position, FIRST returns an offset to the first row, and RANK assigns a position according to measure values. SIZE is specifically intended to report the number of marks in the active partition.<\/span><\/p>\n

                            Question 194.<\/b><\/p>\n

                            Which Tableau function returns the sequential position of the current mark within a partition, starting with 1?<\/b><\/p>\n

                              \n
                            1. <\/b> SIZE()<\/span>
                              \n<\/span>2.<\/b> INDEX()<\/span>
                              \n<\/span>3.<\/b> LAST()<\/span>
                              \n<\/span>4.<\/b> WINDOW_COUNT()<\/span><\/li>\n<\/ol>\n

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

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

                              INDEX()<\/span> returns the current mark’s sequential position within a table-calculation partition. The first mark is assigned 1, the next 2, and so on. The result depends on the current ordering and partitioning of the view, so changing sorting or the Compute Using configuration can change INDEX values. This function is frequently used for row numbering, controlling which marks are displayed, or creating more advanced table-calculation logic. SIZE returns the number of marks in the partition, while LAST returns an offset relative to the last row. INDEX is the correct function when simple sequential position is required.<\/span><\/p>\n

                              Question 195.<\/b><\/p>\n

                              Which Tableau data-combination operation should be used to stack records from yearly tables that have the same column structure?<\/b><\/p>\n

                                \n
                              1. <\/b> Relationship<\/span>
                                \n<\/span>2.<\/b> Join<\/span>
                                \n<\/span>3.<\/b> Union<\/span>
                                \n<\/span>4.<\/b> Blend<\/span><\/li>\n<\/ol>\n

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

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

                                A union combines rows from multiple tables with similar structures. If separate tables contain records for 2024, 2025, and 2026 with the same columns, a union can stack them vertically into one combined dataset. Tableau matches columns based on names and compatible structures, though analysts should inspect the resulting fields for mismatches. A join combines columns horizontally based on matching keys. Relationships preserve separate logical tables and define how they interact during analysis. Data blending combines aggregated data from separate sources. When the goal is to append similar records from multiple tables into one longer table, a union is the correct operation.<\/span><\/p>\n

                                Question 196.<\/b><\/p>\n

                                Which join returns every row from both tables, including unmatched rows from either side?<\/b><\/p>\n

                                  \n
                                1. <\/b> Inner join<\/span>
                                  \n<\/span>2.<\/b> Left join<\/span>
                                  \n<\/span>3.<\/b> Right join<\/span>
                                  \n<\/span>4.<\/b> Full outer join<\/span><\/li>\n<\/ol>\n

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

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

                                  A full outer join returns all matching rows as well as unmatched rows from both the left and right tables. Where no match exists, fields from the other table are null. This can be useful when analysts need complete visibility into records from both tables, including unmatched entities. An inner join returns only matching rows, while a left join preserves every row from the left table and a right join preserves every row from the right. Analysts should carefully evaluate the resulting row counts because full outer joins can increase data volume and may expose data-quality issues such as missing or inconsistent join keys.<\/span><\/p>\n

                                  Question 197.<\/b><\/p>\n

                                  What is one major advantage of using relationships instead of physical joins when tables have different levels of detail?<\/b><\/p>\n

                                    \n
                                  1. <\/b> Relationships allow the tables to preserve their own granularity until Tableau determines how to query them<\/span>
                                    \n<\/span>2.<\/b> Relationships always generate one physical table in advance<\/span>
                                    \n<\/span>3.<\/b> Relationships eliminate the need for common fields<\/span>
                                    \n<\/span>4.<\/b> Relationships prevent all null values<\/span><\/li>\n<\/ol>\n

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

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

                                    Relationships connect logical tables without immediately flattening them into a single physical table. Each table can retain its own level of detail, and Tableau determines how to combine the data based on the fields required by the current visualization. This can reduce unwanted duplication of measures that might occur with physical joins between tables of different granularities. Relationships still require meaningful fields that define how the tables relate, and they do not prevent nulls or data-quality issues. Their key benefit is flexible, context-sensitive querying while preserving the logical structure and grain of the underlying tables.<\/span><\/p>\n

                                    Question 198.<\/b><\/p>\n

                                    Which Tableau connection type stores data in Tableau’s optimized extract format rather than querying the original source for every interaction?<\/b><\/p>\n

                                      \n
                                    1. <\/b> Live connection<\/span>
                                      \n<\/span>2.<\/b> Extract<\/span>
                                      \n<\/span>3.<\/b> Relationship<\/span>
                                      \n<\/span>4.<\/b> Published calculation<\/span><\/li>\n<\/ol>\n

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

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

                                      An extract stores data in Tableau’s optimized extract format and can provide efficient analytical query performance. Because the data is stored separately from the original source, interactions can often be handled without sending every query back to the underlying system. Extracts can be refreshed to incorporate new or changed data, and refresh schedules can be configured in supported publishing environments. A live connection queries the source system as needed. Relationships describe data-model connections rather than storage methods, and published calculations are unrelated to connection type. Extracts are particularly useful when source performance, network conditions, or offline analysis make a local optimized copy desirable.<\/span><\/p>\n

                                      Question 199.<\/b><\/p>\n

                                      A source table receives new rows every day, and existing rows rarely change. Which Tableau extract strategy may reduce refresh time?<\/b><\/p>\n

                                        \n
                                      1. <\/b> Disable refreshes entirely<\/span>
                                        \n<\/span>2.<\/b> Always rebuild the workbook manually<\/span>
                                        \n<\/span>3.<\/b> Use an incremental refresh with a suitable incremental field<\/span>
                                        \n<\/span>4.<\/b> Convert all dimensions to measures<\/span><\/li>\n<\/ol>\n

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

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

                                        An incremental refresh can add only new records to an existing extract when Tableau can identify those records using an appropriate incremental field, such as an increasing identifier or timestamp. This can be much faster than rebuilding a large extract from scratch each time. The approach works best when historical rows remain stable and the data source primarily receives new records. If existing records are updated or deleted frequently, a full refresh may still be needed to keep the extract accurate. Disabling refreshes would make the data stale, while field-role changes do not address refresh performance. Incremental refresh is therefore appropriate for append-oriented source data.<\/span><\/p>\n

                                        Question 200.<\/b><\/p>\n

                                        A dashboard contains a summary bar chart and a detailed table. The analyst wants clicking a bar to restrict the table to only the selected category. Which feature should be configured?<\/b><\/p>\n

                                          \n
                                        1. <\/b> Highlight action<\/span>
                                          \n<\/span>2.<\/b> Parameter action<\/span>
                                          \n<\/span>3.<\/b> URL action<\/span>
                                          \n<\/span>4.<\/b> Filter action<\/span><\/li>\n<\/ol>\n

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

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

                                          A filter action uses the selected mark or marks from one worksheet to restrict data in another worksheet. In this case, selecting a Category bar can pass the category value to the detailed table so that only matching rows remain visible. This is a common dashboard pattern because it lets a summary visualization act as an interactive filter control. A highlight action keeps unrelated marks visible and merely emphasizes matching data, while a parameter action updates a parameter value. URL actions open external resources. When the target worksheet should actually be reduced to records associated with the selected mark, a filter action is the correct Tableau feature.<\/span><\/p>\n

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

                                          View Full Tableau TDA-C01 Exam Dumps and Practice Test Dumps   Question 181. An analyst wants to compare Sales and Profit across products while also identifying outliers and potential relationships between the two measures. Which visualization is most appropriate? Scatter plot 2. Highlight table 3. Histogram 4. Pie chart Correct Answer: 1 Explanation: A scatter […]<\/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\/23338"}],"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=23338"}],"version-history":[{"count":1,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/23338\/revisions"}],"predecessor-version":[{"id":23339,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/23338\/revisions\/23339"}],"wp:attachment":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/media?parent=23338"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/categories?post=23338"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/tags?post=23338"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}