TDA-C01 EXAM BRAINDUMPS & TDA-C01 QUIZ QUESTIONS & TDA-C01 VALID BRAINDUMPS

TDA-C01 Exam Braindumps & TDA-C01 Quiz Questions & TDA-C01 Valid Braindumps

TDA-C01 Exam Braindumps & TDA-C01 Quiz Questions & TDA-C01 Valid Braindumps

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Tableau is a leading data visualization and analysis software that helps organizations in gaining significant insights from large and complex datasets. Certified Tableau Data Analysts command a higher salary than non-certified ones in the job market, and this certification serves to validate their skills and knowledge to potential employers.

Tableau TDA-C01 exam is a computer-based exam that consists of 36 multiple-choice and multiple-response questions. TDA-C01 exam has a time limit of 120 minutes, and candidates must score at least 70% to pass. TDA-C01 Exam covers a range of topics, including data connection, data preparation, analytics, visualization, and dashboard creation. TDA-C01 exam is designed to test a candidate's ability to use Tableau to analyze data and create meaningful insights that can be used to make data-driven decisions. Passing the TDA-C01 exam demonstrates that a candidate has the skills and knowledge required to use Tableau effectively for data analysis and visualization.

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Tableau Certified Data Analyst Sample Questions (Q40-Q45):

NEW QUESTION # 40
You have the following line chart that shows the average sales by month.

Which month had the biggest increase in swage sales compared to me previous month in 2019?

  • A. August
  • B. December
  • C. October
  • D. November

Answer: D

Explanation:
Based on the line chart provided, each point on the line chart represents the average sales for a given month, and the percentage values indicate the change in average sales compared to the previous month. The month of November shows a significant increase from the month of October, indicated by a large upward spike on the chart and a high percentage increase.


NEW QUESTION # 41
You have the following line chart that shows the average sales by month.

Which month had the biggest increase in swage sales compared to me previous month in 2019?

  • A. August
  • B. December
  • C. November
  • D. October

Answer: D

Explanation:
To find the month that had the biggest increase in average sales compared to the previous month in 2019, you need to compare the slopes of the line segments between each pair of months. The steeper the slope, the greater the increase. Based on the line chart, October had the steepest slope, meaning it had the biggest increase in average sales compared to September in 2019. Reference: https://help.tableau.com/current/pro/desktop/en-us/buildexamples_line.htm https://www.mathsisfun.com/algebra/line-equation-slope.html


NEW QUESTION # 42
You plan to create a Tableau subscription for several users.
Which two formats can you use? Choose two.

  • A. Microsoft Excel
  • B. PDF
  • C. Microsoft PowerPoint
  • D. Image

Answer: B,D


NEW QUESTION # 43
You have the following dataset in Microsoft Excel.

You are using interpreter to cleans the dataset. Data interpreter provides the following results.

How many rows of data will be ingested into Tableau as values?

  • A. 0
  • B. 1
  • C. 2
  • D. 3

Answer: C

Explanation:
Data interpreter is a feature that helps you clean and structure your data in Excel before importing it into Tableau. It detects and removes any headers, footers, subtotals, or other elements that are not part of the actual data. It also splits any merged cells and fills in any missing values. In this case, data interpreter provides the following results:
It removes the first three rows and the last two rows that contain headers and footers.
It splits the merged cells in column A and fills in the missing values with "Fiction" or "Non-Fiction".
It does not remove or change any other rows or cells.
Therefore, the number of rows of data that will be ingested into Tableau as values is 17, which is the number of rows left after removing the headers and footers. Reference: https://help.tableau.com/current/pro/desktop/en-us/importing_cleaning_up_data.htm https://help.tableau.com/current/pro/desktop/en-us/importing_data_interpreter.htm


NEW QUESTION # 44
You have the Mowing two tables that contains data about the books in a library.


Both tables are incomplete so there are books missing from the tables.
You need to combine the tables. The solution must ensure that all the data is retained Which type of join should you use?

  • A. Full outer join
  • B. Inner join
  • C. Right join
  • D. left join

Answer: A

Explanation:
To combine the two tables that contain data about books in a library and ensure that all the data is retained, you should use a full outer join. A full outer join is a type of join that returns all rows from both tables, regardless of whether there is a match or not. If there is no match, null values are filled in for the missing fields.
To perform a full outer join, you need to do the following steps:
Connect to both tables as your data sources in Tableau. You can use either live or extract connections.
Drag one table to the canvas and drop it on top of another table. This will create a join between them based on a common field.
Click on the join icon between the tables and select Full Outer Join from the drop-down list. This will change the join type to full outer join and show all rows from both tables.
Optionally, you can add or remove join clauses by clicking on Add or Remove buttons next to each clause. You can also change or rename fields by clicking on them.
The other types of joins are not correct for this scenario. An inner join returns only the rows that have a match in both tables, which will exclude any books that are missing from either table. A left join returns all rows from the left table and only the matching rows from the right table, which will exclude any books that are only in the right table. A right join returns all rows from the right table and only the matching rows from the left table, which will exclude any books that are only in the left table. References:
https://help.tableau.com/current/pro/desktop/en-us/joining_tables.htm
https://help.tableau.com/current/pro/desktop/en-us/join_types.htm
When combining two datasets that are both incomplete and where it's important to retain all data from both sources, a full outer join is appropriate. This type of join ensures that all records from both tables are included in the combined dataset, even if there are no matching records in the other table.


NEW QUESTION # 45
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