Salesforce Tableau Data Analyst Exam Questions 2026
Salesforce Tableau Data Analyst Exam Questions 2026
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Question 1:
Which Tableau function allows you to write calculations involving date and time components, such as extracting the year or quarter?
A. DATENAME
B. DATEADD
C. DATEPART
D. DATEDIFF
Answer: C
Explanation:
✅ DATEPART The DATEPART function in Tableau allows you to extract a specific part of a date or datetime field as an integer. This includes components like year, quarter, month, day, hour, minute, second, etc. For example, DATEPART('year', [Order Date]) would return the year from the ‘Order Date’ field.
❌ DATENAME The DATENAME function in Tableau returns the name of a specified date part as a string. For example, DATENAME('month', [Order Date]) would return “January”, “February”, etc. While it deals with date components, it returns a string rather than an integer, which might not be ideal for all types of calculations.
❌ DATEADD The DATEADD function in Tableau is used to add a specified interval to a date or datetime field. For example, DATEADD('month', 1, [Order Date]) would return the date one month after the ‘Order Date’. It modifies the date, not extracts a component.
❌ DATEDIFF The DATEDIFF function in Tableau returns the difference between two dates based on a specified date part. For example, DATEDIFF('month', [Order Date], [Ship Date]) would return the number of months between the ‘Order Date’ and ‘Ship Date’. It calculates a difference, not extracts a component from a single date.
Question 2:
What does changing default field properties in Tableau include?
A. Renaming columns
B. Modifying sorting options
C. Creating aliases
D. Converting between dimensions and measures
Answer: B
Explanation:
Modifying sorting options is included in changing default field properties in Tableau.
When you modify sorting options, you are changing the default way data is sorted within a specific field. This can be done by:
Ascending or descending order: Sorting data from smallest to largest or vice versa.
Custom sort order: Defining a specific order for categorical data.
Sorting by another field: Sorting data based on the values of another field.
This allows you to control the presentation of data in your visualizations and analyses.
Question 3:
You are creating a Tableau report to rank products based on their sales performance. The dataset includes a Product field and a Sales field. You want each product to have a unique rank based on its sales. Which custom table calculation should you use to assign a rank to each product?
A. RANK([Sales])
B. INDEX()
C. SIZE()
D. WINDOW_SUM([Sales])
Answer: A
Explanation:
RANK([Sales])
RANK([Sales]): This function calculates the rank of each product based on its sales value.
Ranks can be assigned in various ways (e.g., ascending, descending, dense rank) depending on the specific ranking method chosen within the RANK function.
INDEX(): This function returns the row number within a partition. It doesn’t directly rank based on a specific field value.
SIZE(): This function returns the number of rows in a partition. It doesn’t provide a rank based on any specific field.
WINDOW_SUM([Sales]): This function calculates the sum of sales within a specified window. It doesn’t directly assign ranks.
By using RANK([Sales]), you can effectively order your products based on their sales performance and gain insights into which products are selling best.
Question 4:
In a Tableau dashboard, you are working with a dataset that includes a numerical Customer Rating field ranging from 1 to 5. You want to ensure that every time this field is used in a visualization, it is sorted in descending order by default. What should you do to achieve this?
A. Manually sort the ‘Customer Rating‘ field in descending order each time it is used in a visualization
B. Change the default sorting of the ‘Customer Rating‘ field to descending in its field properties
C. Create a calculated field that inverses the ‘Customer Rating‘ values for sorting purposes
D. Use a filter on the ‘Customer Rating‘ field to display values in descending order
Answer: B
Explanation:
Change the default sorting of the ‘Customer Rating‘ field to descending in its field properties.
This is the most efficient and direct way to achieve the desired outcome. By changing the default sort order in the field properties, you ensure that every time the “Customer Rating” field is used in a visualization, it will automatically be sorted in descending order without any manual intervention.
Here’s why the other options are less ideal:
Manually sort the ‘Customer Rating‘ field in descending order each time it is used in a visualization: This is tedious and defeats the purpose of having a default setting. You would have to repeat this process every single time you use the field.
Create a calculated field that inverses the ‘Customer Rating‘ values for sorting purposes: While this could work, it’s an unnecessarily complex solution. It adds an extra calculated field to your data and makes the sorting logic less clear. Changing the default sort order in the field properties is much simpler and cleaner.
Use a filter on the ‘Customer Rating‘ field to display values in descending order: A filter is used to include or exclude data, not to sort it. While you can technically combine a filter with a sort, it’s still not the most direct or efficient way to set a default sort order. The filter would also affect which values are displayed, which is likely not what you intend.
Question 5:
You want to create a filter in Tableau to exclude certain product categories from the analysis. Which filter setting should you configure?
A. Top N
B. Wildcard
C. Include
D. Exclude
Answer: D
Explanation:
To create a filter in Tableau that excludes certain product categories from your analysis, you should configure the Exclude filter setting. This option allows you to specify which categories you want to remove from your data view, ensuring that only the desired categories are included in your analysis.
Using the Exclude filter is particularly useful when you want to maintain the context of your analysis while omitting specific data points or categories that are not relevant to your current focus.
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Question 6:
When connecting to a relational database, what is the purpose of using stored procedures in Tableau?
A. To enhance data security
B. To automate complex data transformations
C. To create custom calculations
D. To optimize query performance
Answer: D
Explanation:
The primary purpose of using stored procedures in Tableau when connecting to a relational database is to optimize query performance.
Stored procedures reside on the database server and are pre-compiled. When Tableau calls a stored procedure, the database can execute it very efficiently. This is generally faster than Tableau sending complex SQL queries directly, especially for complex transformations or aggregations.
While stored procedures can enhance data security (by encapsulating data access logic and preventing direct access to tables) and can be used for complex data transformations, these are not their primary purpose in the context of Tableau. Tableau itself provides features for custom calculations, so that’s not the main reason to use stored procedures. The key benefit in Tableau is performance.
Question 7:
How can you create a calculated field that calculates the moving average of a measure in Tableau?
A. Apply the WINDOW_AVG() function
B. Create a table calculation with a running total
C. Implement a calculated field with a moving average formula
D. Utilize the MOVING_AVG() function
Answer: A
Explanation:
The most direct and accurate way to create a calculated field for a moving average in Tableau is to:
Apply the WINDOW_AVG() function
Here’s why:
WINDOW_AVG():
This is a table calculation function in Tableau specifically designed for calculating moving averages (and other window-based aggregations).
It allows you to specify the range of values to include in the average (e.g., the previous N rows).
Here’s why the other options are less appropriate:
Create a table calculation with a running total:
While you could use a running total as a component in a more complex moving average calculation, it’s not the direct way to achieve a moving average.
Implement a calculated field with a moving average formula:
While you could write a complex calculated field to manually calculate a moving average, it’s unnecessary when WINDOW_AVG() handles it directly and more efficiently.
Utilize the MOVING_AVG() function:
There is no built in MOVING_AVG() function in tableau.
Question 8:
Scenario: You are tasked with analyzing customer feedback data stored in a CSV file. The data includes columns for customer IDs, satisfaction scores, and comments. Your goal is to filter out comments with a length less than 10 characters and create a visualization based on the remaining data.
Question: What is the most appropriate step in Tableau to filter out comments with a length less than 10 characters?
A. Use the “Filter“ shelf and set a condition on the comment length
B. Create a calculated field to count the characters in each comment and then filter the data
C. Use the “Data Source“ tab to edit the CSV file and remove rows with short comments
D. Apply a default filter on the comment length during the data connection process
Answer: A
Explanation:
Use the “Filter“ shelf and set a condition on the comment length:
This is a direct and efficient method. Tableau’s filtering capabilities allow you to create conditions based on string lengths. You can create a filter on the comment field with a condition that the length must be greater than or equal to 10.
Create a calculated field to count the characters in each comment and then filter the data:
This is also a valid approach. You can create a calculated field using the LEN() function to determine the length of each comment. Then, you can use the filter shelf to filter based on this calculated field. While correct, it is an extra step compared to the first option.
Use the “Data Source“ tab to edit the CSV file and remove rows with short comments:
This is not a recommended approach. Modifying the source data directly is generally discouraged. It’s better to perform data transformations within Tableau so that the original data remains intact. Also, this is not possible when the data source is a database.
Apply a default filter on the comment length during the data connection process:
Tableau does not have default filter option during the data connection process based on the length of a string.
Therefore, the most appropriate step in Tableau is:
Use the “Filter“ shelf and set a condition on the comment length
This is the most straightforward and efficient way to achieve the desired filtering.
Question 9:
Scenario: You have a dataset containing sales information, and the “Date“ field is in a string format (e.g., “2023-05-15“). You need to convert this string into a date format for proper time-based analysis.
Question: What Tableau transformation technique is suitable for converting a string date into a date format?
A. Use the “PARSE“ function in Tableau to convert the string to a date
B. Apply the “Pivot“ feature to transform the string into separate date components
C. Utilize the “Group“ function to aggregate the data based on the string date
D. Manually edit the string date format in the data source tab
Answer: A
Explanation:
In Tableau, when you have a string-formatted date (e.g., "2023-05-15"), and you want to convert it into a proper date data type, you should use either:
DATE() function: Converts a recognizable string into a date.
DATEPARSE() function: Parses a custom-formatted string into a date using a specific format (more precise control than DATE()).
Since the option says “PARSE” function, it’s referring to DATEPARSE() or similar logic, which is valid and commonly used.
❌ Incorrect Options:
“Pivot” feature: Used to reshape data (e.g., columns to rows), not for parsing dates.
“Group” function: Used for grouping values (e.g., categorizing), not for transforming strings into dates.
“Manually edit the string date format in the data source tab”: Not scalable or ideal for transforming data for analysis — it’s more of a workaround.
Question 10:
You want to visualize the trend in monthly sales data. Which quick table calculation should you use in Tableau?
A. Percent of total
B. Percentile
C. Moving average
D. Running total
Answer: C
Explanation:
✅ Moving average The Moving average quick table calculation is the most suitable for visualizing the trend in monthly sales data. It smooths out fluctuations in the data by calculating the average sales over a specified number of preceding months, highlighting the underlying direction of the sales figures over time.
❌ Percent of total The Percent of total quick table calculation shows the contribution of each month’s sales to the overall total sales. While useful for understanding the relative importance of each month, it does not directly visualize the trend of sales over time.
❌ Percentile The Percentile quick table calculation displays the percentile rank of each month’s sales value within the entire dataset. This helps in understanding how each month’s sales compares to others but does not illustrate the trend of sales progression.
❌ Running total The Running total quick table calculation shows the cumulative sum of sales over the months. While it indicates growth over time, it doesn’t effectively visualize the trend in the rate of sales or identify potential peaks and troughs as clearly as a moving average.
For a full set of 460 questions. Go to
https://skillcertpro.com/product/tableau-data-analyst-exam-questions/
SkillCertPro offers detailed explanations to each question which helps to understand the concepts better.
It is recommended to score above 85% in SkillCertPro exams before attempting a real exam.
SkillCertPro updates exam questions every 2 weeks.
You will get life time access and life time free updates
SkillCertPro assures 100% pass guarantee in first attempt.