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| Section | Weight | Objectives |
|---|---|---|
| Understanding Tableau Concepts | 15% | - Core Tableau Concepts
|
| Exploring and Analyzing Data | 37% | - Analyze Data
|
| Connecting to and Preparing Data | 23% | - Prepare Data
|
| Sharing Insights | 25% | - Dashboards and Stories
|
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NEW QUESTION # 88
If you see the following Filter, then you're working with _______________________ Larger image
Answer: C
Explanation:
Dates in Tableau will behave differently depending on whether they are a Datepart (blue) or a Datevalue (green). This affects how the axes display/behave and also how visualisations such as line charts will display. The difference essentially boils down to Dateparts behaving like a dimension as opposed to a measure which is how Datevalues behave. This means that Dateparts behave like discrete categories on the view whereas Datevalues are more like continuous numeric values.
Dateparts are discrete and they behave the same as dimension filters. If all dates are used on the filter then each individual date will be a datepart that can be selected/excluded. This is the same for each level of date, if datepart months is placed on filters January to December will be tick-able options in the filter. This also means that conditions and top/bottom filters can be applied to datepart filters like any other dimension filter.
Datevalues placed on filters behave like measure filters. A min and a max date can be set and there is a relative dates option which allows you to choose things like only show the previous 3 months or years etc.
NEW QUESTION # 89
You need to display the complete list of potential data connections when you connect to a server. What action should you perform?
Answer: A
Explanation:
To display the complete list of potential data connections when connecting to a server in Tableau, you should select "More" under the "To a Server" option. This action will provide a comprehensive list of server types and data sources that Tableau can connect to.
NEW QUESTION # 90
If you are working with a huge dataset, which of the following are strong reasons to use a context filter?
Answer: A,D
Explanation:
By default, all filters that you set in Tableau are computed independently. That is, each filter accesses all rows in your data source without regard to other filters. However, you can set one or more categorical filters as context filters for the view. You can think of a context filter as being an independent filter (Option stating - To create a dependent filter eliminated here). Any other filters that you set are defined as dependent filters because they process only the data that passes through the context filter.
You may create a context filter to:
1) Improve performance - If you set a lot of filters or have a large data source, the queries can be slow. You can set one or more context filters to improve performance.
2) Create a dependent numerical or top N filter - You can set a context filter to include only the data of interest, and then set a numerical or a top N filter.
For example, suppose you're in charge of breakfast products for a large grocery chain. Your task is to find the top 10 breakfast products by profitability for all stores. If the data source is very large, you can set a context filter to include only breakfast products. Then you can create a top 10 filter by profit as a dependent filter, which would process only the data that passes through the context filter.
NEW QUESTION # 91
Yes or No: The number of marks will increase when you increase the number of Dimensions in a view
Answer: B
Explanation:
Explanation
Of course! As an example, see below:
1) Using the Sample Superstore data, let's plot a bar chart showing the Sales for each Category:
Observe that we have 3 marks - Each bar in a bar chart is called a mark. Similarly, each point in a scatter plot is also a mark , and so on for all charts.
1 row by 3 columns means that clearly on the y-axis (Sales), we have only a single mark - a single continous axis, but 3 different marks (Technology, Furniture and Office supplies ) on the x-axis.
2) Now let's add subcategory to the view as well (another dimension):
Observe that the number of marks has increased - i.e the number of Bars.
Also, notice we now have 1 row and 17 columns. Simply because 1 row = Sales (on the y-axis), and on the x-axis, we have 17 different columns (i.e product sub categories!!)
NEW QUESTION # 92
Which of the following is an example of a Date Part?
Answer: D
Explanation:
All answers except November are examples of Date Values (continuous in nature).
See below:
You can see that the option in Green symbolizes our correct answer, i.e only a Month. In our case that month is November (the correct answer).
All other options are combinations of a year with one other value (like a month, quarter, or day). So this is how by looking at an option you can know if its a date part or date value!
NEW QUESTION # 93
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