Explanation:

Correct Solution: Explorer (can publish) (Option A)
The Explorer (can publish) site role is the perfect fit because it grants sales users exactly what they need while minimizing licensing costs:

Access published visualizations from anywhere (including outside the network) – Yes
Edit existing visualizations – Yes (they can open workbooks and save overwrites or new versions to the server)
Create new visualizations based on published data sources – Yes (they can connect to any published data source and publish new workbooks)
Lowest possible license cost for these capabilities – Yes (significantly cheaper than Creator licenses)

This role is designed precisely for power business users who need full interactivity and publishing rights without the overhead (and cost) of a full Creator license.

Why Site Administrator (Option B) Is Overkill and Expensive
Site Administrator gives complete control over the entire Tableau site (user management, projects, settings, etc.). It is unnecessary, risky from a governance standpoint, and still consumes a Creator-level license.

Why Viewer (Option C) Is Insufficient
Viewer is read-only. Users can view dashboards and interact with filters/actions, but they cannot edit or create new content and cannot publish anything — failing two of the four requirements.

Why Creator (Option D) Is Unnecessarily Expensive
Creator provides everything (including Desktop access, ability to build from live connections or new extracts, etc.), but it is the most expensive license tier. Since the sales users only need to work with published data sources and published visualizations, Explorer (can publish) delivers the same end-user experience at a lower cost.

In summary, when the goal is to enable editing, creating, and publishing from published content outside the network while minimizing licensing costs, the Analytics-Con-301 answer is always A – Explorer (can publish). This is Tableau’s official cost-optimization recommendation for this exact scenario.

Explanation:

✅ Complete Steps / Explanation
1. Open the workbook and immediately start performance recording
On the virtual machine desktop, open Tableau Desktop.
Before opening the workbook, start the recording:
Go to Help → Settings and Performance → Start Performance Recording.
Now open the workbook:
Open NYC Property Transactions.twbx from the desktop.
✔ This satisfies the requirement: "Ensure that you start the recording as soon as you open the workbook."

2. Open the required dashboard
In the workbook, navigate to the Property Transactions dashboard.
Reset all filters:
For each filter on the dashboard, choose Show All Values.
This ensures the performance recording represents a full load of the dashboard.

3. Stop the performance recording and save it
When the dashboard is fully loaded:
Go to Help → Settings and Performance → Stop Performance Recording.
Tableau automatically opens a new workbook titled Performance Recording.
Save it:
Go to File → Save As
Save the file in: C:\CC\Data\
Use the default file name unless instructed otherwise.

4. Create a new worksheet in the Performance Recording workbook
You are now inside the automatically generated Performance Recording workbook.
Create the required bar chart:
Click New Worksheet (bottom tab).
From the Performance Recording data source, identify these fields:
Command Name
Elapsed Time (ms)
Worksheet
Build the bar chart:
Drag Command Name to Rows.
Drag Elapsed Time (ms) to Columns.
Drag Worksheet to Color on the Marks card.
Set Mark Type = Bar.
This results in a bar chart showing how much time each command spent, grouped by worksheet, which is exactly what the task requires:
“Create a bar chart to show the elapsed time of each command name by worksheet, to show how each sheet … contributes to the overall load time.”

5. Save again
On the Performance Recording workbook:
Go to File → Save.
Ensure the file is saved in: C:\CC\Data\

✅ Final Answer (Short Version)
Start performance recording → open workbook → open dashboard → reset filters → stop recording → save in C:\CC\Data\ → create worksheet in performance recording workbook → build bar chart (Command Name vs. Elapsed Time, colored by Worksheet) → save again.

Explanation:

This approach correctly separates the two distinct security requirements: Row/Data-Level Access and View/Visualization-Level Access.

Control Data Access (Group 1 vs. Groups 2 & 3):
Group 1 (Viewers who cannot see any information on profitability) must be prevented from seeing any data rows related to profit.
A User Filter (a calculated field using the ISMEMBEROF() function placed on the Filter Shelf) is the standard and most straightforward Tableau method for implementing Row-Level Security (RLS).
The consultant applies a User Filter to the data source that only allows Groups 2 and 3 to see the underlying data required to calculate profit and profit margin. Group 1's data is filtered out entirely.

Control Visibility within the View (Group 2 vs. Group 3):
Group 3 (Viewers who can see profit margin but not the value of profit) must see the aggregated margin but not the component raw profit value.
This is achieved by creating a calculated field for the raw [Profit] measure that only returns a value for Group 2:

IF ISMEMBEROF("Group 2") THEN [Profit] ELSE NULL END

Explanation:

The Performance Recording shows "Executing Query" as the bottleneck. This points to an inefficient filter that forces Tableau to scan a large portion of the extract.

The Problem:
A Relative Date filter (e.g., "Last 3 Months") is a complex, non-indexed filter. To apply it, Tableau must evaluate every single row in the "Created Date" column (containing 5 years of data) to see if it meets the relative condition. This is computationally expensive on a large dataset.
The Solution:
Replacing it with a simple filter on YEAR([Created Date]) is much more efficient. This creates a discrete, integer-based filter that can be optimized by Tableau's query engine, drastically reducing the query execution time.

Why Others Are Wrong:
A. Changing Dropdown to Custom List:
This is a UI change that has no impact on query performance.
C. Replacing TOTAL() with Grand Total:
TOTAL() is a table calculation that computes after the query. It is not the cause of the slow "Executing Query" step.
D. Replacing SUM() with WINDOW_SUM():
WINDOW_SUM() is also a table calculation that operates on the results of the query. It does not affect the initial data retrieval speed.

Explanation:

For an in-place upgrade of a single-node Tableau Server (from 2023.1 to the latest version, such as 2025.3 as of November 2025), the process is performed directly on the production server to minimize downtime and avoid the need for traffic redirection or a separate environment. This method installs the new version side-by-side with the existing one, then uses an upgrade script to migrate configurations, data, and settings seamlessly. Key steps include:
Disable scheduled tasks: Before upgrading, pause jobs like extract refreshes or subscriptions via the TSM CLI (e.g., tsm maintenance pause-jobs) to prevent interruptions or data inconsistencies during the process.
Run the upgrade script: After installing the new version's setup program (which detects and prepares the existing installation), execute tsm maintenance upgrade --file as an administrator. This handles the core migration, including repository data and services.
Confirm and test: Restart services with tsm start, then validate functionality (e.g., via the Upgrade Server dashboard for feature impacts) and test critical dashboards, data sources, and new capabilities.
This sequence ensures a controlled upgrade with built-in checkpointing for rollback if needed. The process typically takes 1–2 hours for a single node, depending on data volume.

Why the other options are incorrect:
A: Uninstalling the old version before running the upgrade script is invalid—Tableau's process requires the existing version to remain in place until the script migrates everything. Uninstalling prematurely would cause data loss or require a full restore.

C: This describes a fresh install and restore approach in a non-production environment (e.g., cloning via backup/restore), not an in-place upgrade. It involves redirecting traffic, which adds complexity and downtime unsuitable for in-place scenarios.

D: Cloning via VM snapshots and upgrading in non-production is a blue/green deployment for zero-downtime upgrades or major OS changes, not a standard in-place process on the production node. It also requires traffic redirection, which contradicts the in-place intent.

Reference:
Tableau Help: Upgrading Tableau Server (Single-Node)
Best Practices: Upgrade Planning Checklist
2025 Release Notes: What's New in Tableau Server (includes upgrade impact filters)