Explanation

The correct answer is A. Connect to an API. A Web Data Connector (WDC) in Tableau is a customized connector built using HTML and JavaScript that allows Tableau Desktop or Tableau Server to retrieve data from web-based sources, most commonly REST APIs or other web services. The WDC acts as a bridge between Tableau and the web data source, enabling the analyst to pull data that is not otherwise accessible through native connectors. This makes option A the correct answer.

Why the other options are incorrect:

B. Capture website traffic
– Capturing website traffic requires external analytics tools such as Google Analytics or Adobe Analytics. A Web Data Connector only retrieves data exposed by a web service or API; it does not monitor or capture live website traffic on its own.

C. Scrape data from an HTML table
– While a WDC could theoretically be built to parse HTML, its primary purpose is to connect to structured data via web APIs. Tableau also provides native HTML table scraping capabilities through the "Data from Web" connector, which is separate from the WDC framework.

D. Publish data to the company website
– Web Data Connectors are used for importing data into Tableau, not for publishing or pushing data out to external websites. Publishing Tableau content is handled through Tableau Server, Tableau Cloud, or Tableau Public.

Reference:

Tableau Desktop Specialist and Tableau Data Analyst content areas on Connecting to Data — specifically the Web Data Connector (WDC) framework, its use in connecting to REST APIs and web services, and its distinction from other data acquisition methods such as native web c

Explanation

The analyst needs to perform an "anti-join" operation, which keeps records from the primary table that have no matching records in the secondary table. In Tableau Prep's join dialog, this is explicitly called a "Left only" join. This join type starts with a Left Join (keeping all records from Table1) and then filters out any records that successfully matched with Table2, leaving only the non-matching rows where all fields from Table2 will be null.

✅ Correct Option

🟢 B. Left only:
This is the specific join type designed for this "anti-join" scenario. It keeps all records from the left table (Table1) that do not have a match in the right table (Table2). For these unmatched records, all columns from Table2 will be populated with null values, exactly as the analyst requires.

❌ Incorrect Options

🔴 A. Inner:
An inner join would return only the records where there is a match between Table1 and Table2. This is the opposite of what is needed, as it would exclude the non-matching records from Table1.

🔴 C. Left:
A standard left join keeps all records from Table1, regardless of whether they match with Table2. Matching records show Table2 data, and non-matching records show nulls. However, this includes both matching and non-matching records, while the requirement is for only the non-matching ones.

🔴 D. Full outer:
A full outer join returns all records from both tables. It will include matching records, non-matching records from Table1, and non-matching records from Table2. This includes much more data than the specified requirement.

🔴 E. Union:
A union is used to stack rows from tables with similar structures on top of each other. It is not a join and cannot be used to horizontally combine tables based on matching keys, making it irrelevant for this scenario.

Summary
A "Left only" join is the precise tool for filtering a primary table to rows that have no corresponding data in a secondary table, resulting in null values for all secondary table fields.

Reference
Tableau Help: Combine Your Data

Explanation

The correct answer is C. A discrete dimension. A Top N filter in Tableau is designed to return the top or bottom N values based on a measure, but the filter itself is applied to a dimension. For example, to show the top 10 products by sales, the analyst would create a Top N filter on the Product Name dimension, ranked by the SUM(Sales) measure. The dimension must be discrete because it represents the categorical members being ranked and filtered. This makes option C the correct answer.

Why the other options are incorrect:

A. A table calculation
– A Top N filter is created through the filter dialog on a field, not directly from a table calculation. While table calculations like RANK can be used in some filtering scenarios, the Top N filter feature itself operates on a dimension field.

B. A continuous measure
– Continuous measures produce axes, not discrete categorical members that can be ranked and selected as "top N." The Top N filter ranks dimension members, not continuous measure values.

D. A set
– A set can be used to create a top N condition (e.g., a Top N set), but the question asks which field type the Top N filter should use. The Top N filter is applied to a discrete dimension, not to a set. Sets are a separate feature from the Top N filter option in the filter dialog.

Reference:

Tableau Desktop Specialist and Tableau Data Analyst content areas on Filtering — specifically creating Top N filters, applying them to discrete dimensions, and ranking dimension members by an associated measure.

Explanation

The correct answer is C. A discrete dimension. A Top N filter in Tableau is designed to return the top or bottom N values based on a measure, but the filter itself is applied to a dimension. For example, to show the top 10 products by sales, the analyst would create a Top N filter on the Product Name dimension, ranked by the SUM(Sales) measure. The dimension must be discrete because it represents the categorical members being ranked and filtered. This makes option C the correct answer.

Why the other options are incorrect:

A. A table calculation
– A Top N filter is created through the filter dialog on a field, not directly from a table calculation. While table calculations like RANK can be used in some filtering scenarios, the Top N filter feature itself operates on a dimension field.

B. A continuous measure
– Continuous measures produce axes, not discrete categorical members that can be ranked and selected as "top N." The Top N filter ranks dimension members, not continuous measure values.

D. A set
– A set can be used to create a top N condition (e.g., a Top N set), but the question asks which field type the Top N filter should use. The Top N filter is applied to a discrete dimension, not to a set. Sets are a separate feature from the Top N filter option in the filter dialog.

Reference:

Tableau Desktop Specialist and Tableau Data Analyst content areas on Filtering — specifically creating Top N filters, applying them to discrete dimensions, and ranking dimension members by an associated measure.

Explanation:

To create a histogram on the Quantity field by using bin size of 3, you need to do the following steps:

➡️ Open the link to Book1 found on the desktop. This will open the Tableau workbook that uses the Superstore data source.

➡️ Click on the Histogram tab at the bottom of the workbook to open the Histogram worksheet. You will see a blank worksheet with no marks.

➡️ Right-click on Quantity in the Measures pane and select Create Bins from the menu. This will open a dialog box that allows you to create bins for the Quantity field. Bins are groups of values that are treated as one unit in a histogram.

➡️ Enter 3 in the Size of bins text box. This will set the bin size to 3, which means that each bin will contain values that are 3 units apart. For example, one bin will contain values from 0 to 2, another bin will contain values from 3 to 5, and so on.

➡️ Click OK to create the bins. You will see a new field named Quantity (bin) in the Measures pane with a # sign next to it.

➡️ Drag Quantity (bin) from the Measures pane to Columns on the worksheet. This will create a histogram that shows the distribution of Quantity by bins. You will see bars that represent the frequency or count of values in each bin.

Optionally, you can adjust the width, color, and labels of the bars by using the options on the Marks card. You can also add filters, tooltips, or annotations to enhance your histogram.

🔗 Reference:
➡️ Create Bins from a Continuous Measures