Explanation:

The correct answer is B. Create an extract filter on Store Number and select Condition by field Sales. The requirement is to extract only the data for stores whose total sales are $500 or more. Since a store may have multiple rows of sales transactions in the dataset, the filter must first aggregate sales by store and then apply the threshold. In Tableau, this is achieved by creating an extract filter on the Store Number dimension and selecting the Condition option, then choosing By field and setting the aggregation to SUM(Sales) with the condition "At least 500". This ensures that the extract includes all rows for stores that meet the total sales threshold, while excluding rows for stores that fall below it. This makes option B the correct answer.

Why the other options are incorrect:

A. Create an extract filter on Sales and set the At least value
– Filtering directly on the Sales measure applies the $500 threshold to individual sales transactions, not to the total sales per store. This would exclude individual rows with sales under $500 even if the store's total exceeds $500, which does not meet the requirement.

C. Create an extract filter on Store Number and select Top by field Sales
– The "Top" filter returns a specified number of top-ranked stores (e.g., Top 10 by Sales), not stores meeting a minimum sales threshold. It cannot express the condition "at least $500 in total sales."

D. Create an extract filter on Store Number and select Top by field Store Number
– This filters by the highest Store Numbers rather than by sales values, which is entirely unrelated to the requirement of extracting stores with $500 or more in total sales.

Reference:
Tableau Desktop Specialist and Tableau Data Analyst content areas on Extracts and Data Filtering — specifically creating extract filters, using the Condition filter option on a dimension, applying "By field" aggregation (SUM), and filtering based on aggregated measure thresholds.

Explanation

The correct answers are B. Machines, E. Copiers, and H. Accessories. The "Rank" column is a table calculation using the RANK function, which is partitioned by Category (as evidenced by the rankings restarting at 1 for each Category). When the "Phones" sub-category is filtered out, it is removed from the Technology partition. Phones currently holds Rank 1 in Technology with sales of 330,007. Removing it causes the remaining Technology sub-categories to shift up by one position: Machines moves from Rank 2 to Rank 1, Accessories moves from Rank 3 to Rank 2, and Copiers moves from Rank 4 to Rank 3. These three sub-categories will have changed Rank values. This makes options B, E, and H the correct answers.

Why the other options are incorrect:

A. Chairs
– Chairs belongs to the Furniture category and holds Rank 1 within that partition. Filtering out Phones, which is in the Technology partition, does not affect the Furniture partition, so Chairs retains its Rank of 1.

C. Furnishings
– Furnishings belongs to the Furniture category and holds Rank 4. Its rank is determined solely within the Furniture partition and is unaffected by the removal of Phones from the Technology partition.

D. Bookcases
– Bookcases belongs to the Furniture category and holds Rank 3. Since the Furniture partition is untouched by the filter on Phones, Bookcases retains its Rank of 3.

F. Tables
– Tables belongs to the Furniture category and holds Rank 2. Its rank remains unchanged because the filter only impacts the Technology partition.

G. Phones
– Phones is the sub-category being filtered out, so it will no longer appear in the view and will not have a Rank value at all. It cannot be one of the sub-categories whose Rank changes, since it is removed entirely.

Reference:

Tableau Desktop Specialist and Tableau Data Analyst content areas on Table Calculations — specifically the RANK function, partitioning and addressing dimensions, and how filtering out a dimension member affects rank values within a partition.

Explanation

Tableau Online is a secure, organization-owned cloud site where workbooks need to be actively uploaded from Tableau Desktop so that colleagues can view, interact with, or edit them through a browser. The publishing process also handles permissions, projects, data-source embedding, and extract refreshes in one seamless step.

Correct Option

✅ D. From the Server menu, select Publish Workbook
This is the direct and complete way to get a workbook onto Tableau Online. Choosing Server → Publish Workbook opens a dialog that lets you select your Tableau Online site, pick the target project, set permissions, decide whether to embed credentials or publish the data source separately, and include extracts. After clicking Publish, the entire workbook (dashboards, sheets, stories, formatting) instantly becomes available online.

Incorrect Options

❌ A. From the Server menu → Tableau Public → Save to Tableau Public
Tableau Public is a completely separate free service designed for openly sharing visualizations with the entire internet. Workbooks published there are visible to anyone in the world, downloadable, and cannot be made private later. This has nothing to do with Tableau Online and would expose potentially sensitive data.

❌ B. From the File menu → Export Packaged Workbook
This only saves a .twbx file to your local drive. It bundles everything into a single package for sharing via email or file transfer, but the workbook never reaches Tableau Online automatically. Someone would still need to manually upload the .twbx through the web interface, losing the smoother publishing features (permissions, scheduling, version history).

❌ C. From the Server menu → Publish Data Source
Publishing the data source alone uploads only the connection or extract, not the visualizations. Colleagues on Tableau Online would see the data source in their list, but your carefully designed dashboards, layouts, calculated fields in the workbook context, and formatting would remain on your computer and never appear online.

Reference
Simple Steps to Publish a Workbook

Explanation

To create a calculated dimension that classifies subjects into Sciences or Humanities, Tableau provides logical functions like IF/ELSEIF and CASE. These allow testing multiple conditions and assigning values accordingly. Using functions that rely solely on string matching (like CONTAINS or ENDSWITH) may not cover all required cases accurately. The goal is to map exact subject names to their corresponding category consistently.

✔️ Correct Options

A. IF / ELSEIF
This function checks each condition in order. It explicitly assigns:
'Science' → Sciences
'Computer Science' → Sciences
'English' or 'Social Studies' → Humanities
This ensures all subjects are mapped correctly. The order of ELSEIF statements allows multiple conditions to be checked sequentially.

D. CASE
The CASE function directly maps exact values of [Subject] to categories:
'Computer Science' & 'Science' → Sciences
'English' & 'Social Studies' → Humanities
It is concise, readable, and avoids errors that can occur with complex nested IF statements.

❌ Incorrect Options

B. IIF with CONTAINS
This checks if the string contains “Science” and then assigns values. However, it incorrectly maps “Science” subjects to Humanities instead of Sciences and cannot handle multiple subjects like Social Studies or English. It is not suitable for exact matching of multiple conditions.

C. IF ENDSWITH
Using ENDSWITH only evaluates whether the subject ends with a specific string. “Science” and “Computer Science” would be handled differently, and English or Social Studies would be incorrectly classified. This method does not fully satisfy the mapping rules.

📎 Reference
Tableau Help – Logical Functions (IF, ELSEIF, CASE) https://help.tableau.com/current/pro/desktop/en-us/functions_functions.htm

Explanation

The correct answer is D. All the data sources to which the analyst has access within the site. When a Data Analyst signs in to a site on Tableau Server from Tableau Desktop and searches for a published data source, Tableau displays only the data sources that the analyst has been granted permission to access within that specific site. Visibility is governed by project-level permissions and the user's site role. The analyst will not see data sources in projects they cannot access, nor will they see data sources from other sites on the same server. This makes option D the correct answer.

Why the other options are incorrect:

A. All the data sources published to Tableau Server within the unlocked projects to which the analyst has access
– This option is partially correct in spirit but overly narrow. It implies visibility is limited to "unlocked" projects, which is not the determining factor. Access is governed by permissions (such as View or Connect), not by whether a project is locked or unlocked. Option D is the more accurate and complete answer.

B. All the data sources published to Tableau Server
– This is incorrect because users cannot see all data sources on the server. Visibility is restricted by site membership and project permissions. A user only sees content within the sites and projects they have access to.

C. All the data sources published to the site
– This is incorrect because not all data sources on a site are visible to every user. Access is further restricted by project-level permissions. A user may be a member of the site but still lack access to specific projects or data sources.

Reference:

Tableau Desktop Specialist and Tableau Data Analyst content areas on Connecting to Data and Permissions — specifically signing in to a Tableau Server site from Tableau Desktop, searching for published data sources, and permission-based visibility governed by project permissions and site roles.