Salesforce Analytics-Admn-201 Exam With Confidence Using Practice Dumps

Tableau Server uses a role-based licensing model with three primary license levels: Creator, Explorer, and Viewer. Each license level corresponds to specific site roles that define what users can do on the server.

Viewer License: This is the most restrictive license, allowing users to view and interact with published content (e.g., dashboards and visualizations) but not to create or publish new content.

Site Role: The Viewer license can only be associated with the Viewer site role. This role restricts users to viewing capabilities, aligning with the license’s purpose.

Option A (Creator): Incorrect. The Creator license is for users who can create, edit, and publish content using Tableau Desktop and the web interface. It corresponds to the Creator site role, not Viewer.

Option B (Explorer (can publish)): Incorrect. This is a variation of the Explorer license, which allows users to edit and publish content within limits. It’s more permissive than Viewer.

Option C (Viewer): Correct. The Viewer site role matches the Viewer license level perfectly.

Option D (Explorer): Incorrect. The Explorer license allows users to explore data and create content in the web interface, exceeding the Viewer license’s capabilities.

Abandoned workbooks—those no longer actively used—can clutter Tableau Server and consume resources (e.g., disk space for extracts). Identifying them efficiently requires leveraging built-in administrative tools rather than manual or destructive methods. Let’s explore this in depth:

Tableau Server Admin Views: Tableau provides pre-built administrative views to monitor server health, usage, and content. The Stale Content view, accessible under Server > Status > Administrative Views, is specifically designed to identify content (workbooks, data sources) that hasn’t been viewed or modified recently. It shows:

Content name, owner, and project.

Last viewed date and last modified date.

View count over a period.This view uses Repository data to track usage metrics, making it ideal for spotting abandoned workbooks.

Option A (Use the Stale Content administrative view): Correct. This is the most efficient and non-invasive method. You can filter by last viewed date (e.g., >30 days ago) to identify workbooks with large extracts that users have stopped accessing. From there, you can contact owners or delete the content if policy allows. It’s a server administrator’s go-to tool for content management.

Option B (Examine extract files in ProgramData/.../extract): Incorrect and impractical. The ProgramData/Tableau/Tableau Server/data/tabsvc/dataengine/extract directory stores .hyper extract files, but:

File names are cryptic (e.g., GUIDs), not tied directly to workbook names.

It doesn’t indicate usage or abandonment—only file presence and size.

Manual inspection is time-consuming and error-prone compared to the Stale Content view.

Option C (Delete all extracts and allow them to be re-generated): Incorrect and risky. Deleting extracts (e.g., via tsm maintenance cleanup) removes them without identifying usage. Regeneration only occurs if a schedule or user triggers it, potentially disrupting active users and losing historical data unnecessarily.

Option D (View all workbooks, and sort by the Modified date): Partially effective but inefficient. In the Tableau Server UI (e.g., under Content > Workbooks), you can sort by "Last Modified," but:

It doesn’t show viewership (a workbook might be modified recently but unused).

It’s manual and doesn’t scale for large deployments compared to the Stale Content view.

Why This Matters: The Stale Content view leverages Tableau’s metadata to provide actionable insights, saving time and reducing risk compared to manual or destructive alternatives. It’s part of Tableau’s governance toolkit.

Tableau Server’s minimum hardware recommendations for a production single-node deployment ensure reliable performance for small to medium workloads. As of the latest documentation:

CPU: 8 cores (2.0 GHz or higher) to handle concurrent users, rendering, and background tasks.

RAM: 32 GB to support in-memory processing (e.g., VizQL, Data Engine) and caching.

Disk Space: 50 GB free for installation, logs, extracts, and temporary files.

Let’s break it down:

Option C (8-Core CPU, 32 GB RAM, 50 GB free disk space): Correct. This matches Tableau’s official minimum for production:

8 cores ensure sufficient parallelism for processes like Backgrounder and VizQL.

32 GB RAM supports multiple users and extract refreshes.

50 GB disk space accommodates growth (initial install is ~1–2 GB, but logs and extracts expand).

Option A (4-Core, 16 GB RAM, 50 GB): Incorrect. Too low for production—4 cores and 16 GB RAM are below the threshold for reliable performance under load.

Option B (2-Core, 8 GB RAM, 15 GB): Incorrect. This is for non-production (e.g., trial) setups, insufficient for production stability.

Option D (4-Core, 64 GB RAM, 50 GB): Incorrect. 4 cores are inadequate, though 64 GB RAM exceeds the minimum (32 GB).

Why This Matters: Under-spec hardware can lead to slow performance, failed refreshes, or crashes in production—adhering to the minimum ensures stability.