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NEW QUESTION # 76
You have the following Map.
You need the map to appear as shown in the following visualization.
What should you do?
Answer: A
Explanation:
To create a map visualization that shows the concentration of data points in different locations, you need to change the mark type to Density. This will create a heatmap that uses color and size to indicate the density of the data points. You can also adjust the intensity and transparency of the density marks to suit your needs. References: The information is based on the following sources:
* Create Heatmaps that Show Trends or Density in Tableau
* Build a Simple Map - Tableau
NEW QUESTION # 77
You have a dataset that contains people and the awards they have won.
Which formula should you use to get the number of different types of awards that have been won?
Answer: B
Explanation:
To get the number of different types of awards that have been won, you should use the formula COUNTD ({Award}). This formula will return the count of distinct values in the Award field, which are the different types of awards that have been won.
The other options are not correct for this scenario. COVAR({Award}) is not a valid function in Tableau. MAX ({Award}) will return the maximum value in the Award field, which may not be a type of award. INDEX() will return the index or rank of each row in a partition, which is not related to the types of awards. References:
https://help.tableau.com/current/pro/desktop/en-us/functions_functions_aggregate.htm#COUNTD https://help.
tableau.com/current/pro/desktop/en-us/functions_functions_tablecalculation.htm#INDEX The COUNTD function in Tableau calculates the number of distinct (unique) items in a field. When looking to get the number of different types of awards that have been won, the COUNTD([Award]) function will count each unique award name only once, regardless of how many times it appears in the dataset. This will return the total number of unique award types.
NEW QUESTION # 78
A Data Analyst has the following dataset.
The analyst needs to analyze the relationship between math scores and history scores as shown in the following exhibit.
Which three actions should the analyst perform? Choose three.
Answer: B,D,E
Explanation:
A scatter plot requires two quantitative measures on opposing axes, so Math Score should be placed on Rows and History Score on Columns. To produce one mark for each student rather than a single aggregated mark, Student ID should be placed on Detail on the Marks card. The exhibit also contains a linear model line, which is created by dragging Trend Line from the Analytics pane onto the view. A reference line is different: it marks a fixed or computed value on an axis and does not model the relationship between two measures.
Adding both score fields to the Marks card is also unnecessary because their positions are already defined by the Rows and Columns shelves. These steps follow Tableau's standard scatter-plot and trend-line workflow.
NEW QUESTION # 79
You have the following:
Overall Rank and Rank are calculated fields that use the RANK function.
You filter out the sub-category where [Ovorall Rank] - 1.
For which three the sub-categories will the value of Rank change? Choose three.
Answer: A,C,D
Explanation:
In Tableau, the RANK function assigns a rank to each row within a partition of the data, based on the value of the field being ranked. It is important to understand that the rank is recalculated whenever the underlying data or the partitioning changes.
In the given scenario, the Overall Rank is based on the Sales figures, while the Rank (presumably) is based on the Sales within the Category. When filtering on the condition where [Overall Rank] - 1, it means we are excluding the sub-category that has an Overall Rank of 2.
Looking at the data:
* Furnishings has an Overall Rank of 8, which does not meet the filter condition ([Overall Rank] - 1).
Therefore, its rank remains the same.
* Tables have an Overall Rank of 3. When the sub-category with an Overall Rank of 2 is removed (Chairs in this case), Tables move up in the overall ranking. However, since Tables are the top-ranked within the Furniture category, their Rank within the category would remain unchanged at 1.
* Chairs have an Overall Rank of 2, which meets the filter condition and thus will be removed from the view. We cannot determine the change in Rank for Chairs because they are filtered out.
* Accessories have an Overall Rank of 5. If any sub-category with a higher Overall Rank (1 to 4) is removed, the rank of Accessories within the Technology category could change because it is currently ranked 3 in its category. With the removal of Phones (Overall Rank 1), the Rank of Accessories could potentially increase.
* Copiers have an Overall Rank of 6, which does not meet the filter condition. Therefore, its rank remains the same.
* Machines have an Overall Rank of 4. If we remove Phones (Overall Rank 1), Machines will move up in the overall ranking and potentially within the Technology category as well, changing its Rank from 2 to 1.
* Phones have an Overall Rank of 1, which does not meet the filter condition of being Overall Rank 2.
Therefore, its rank remains the same.
* Bookcases have an Overall Rank of 7, which does not meet the filter condition. Therefore, its rank remains the same.
Based on this analysis, when the sub-category with an Overall Rank of 2 (Chairs) is removed, the Rank value will change for Tables, Accessories, and Machines, as they will move up in the overall ranking within their respective categories. However, it's important to note that while Tables will move up in the overall ranking, their rank within the Furniture category would not change as they are already at the top. The rank changes for Accessories and Machines are due to the removal of Phones, which is ranked higher overall and within the Technology category.
NEW QUESTION # 80
You have a data source that contains data tor every city in the Unites States. The following is a sample of the data.
You need to use the City dimension to create a dynamic filter that snows the cities that have a population greater than one million Which type of filter should you use?
Answer: B
Explanation:
To use the City dimension to create a dynamic filter that shows the cities that have a population greater than one million, you should use a condition filter. A condition filter is a type of filter that shows only the values that meet a specified condition based on a measure or a calculation. You can create a condition filter by dragging a dimension to the Filters shelf and selecting Condition from the dialog box. Then you can enter a formula or choose an option that defines your condition.
In this case, you want to create a condition filter based on Population, which is a measure. You can drag City to the Filters shelf and select Condition from the dialog box. Then you can choose By field from the tab and select Population > Sum > Greater than > 1000000 from the drop-down lists. This will create a condition filter that shows only the cities that have a sum of population greater than one million.
The other options are not correct for this scenario. A general filter is not a specific type of filter, but a term that refers to any type of filter in Tableau. A wildcard filter is a type of filter that shows only the values that match a specified pattern or string, such as "New*" or "*ton". A top filter is a type of filter that shows only the top or bottom N values of a measure or dimension based on a ranking or an aggregation. References:
https://help.tableau.com/current/pro/desktop/en-us/filtering.htm https://help.tableau.com/current/pro/desktop
/en-us/filtering_condition.htm https://help.tableau.com/current/pro/desktop/en-us/filtering_wildcard.htm
https://help.tableau.com/current/pro/desktop/en-us/filtering_topn.htm
NEW QUESTION # 81
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