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| Section | Objectives |
|---|---|
| Troubleshooting and Optimization | - Performance tuning strategies - Common server issues and resolution |
| User and Content Management | - Permissions and content governance - User provisioning and authentication - Projects, workbooks, and data sources management |
| Server Architecture and Administration | - Installation and configuration concepts - Tableau Server architecture components - Server processes and services management |
| Data Sources and Extract Management | - Live vs extract data strategies - Data connections and published data sources - Extract refresh scheduling |
| Security and Permissions | - Permission models and roles - Authentication methods (SAML, Active Directory, etc.) - Row-level security |
| Server Monitoring and Maintenance | - Backup and restore procedures - Performance monitoring and logs - Upgrades and patch management |
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NEW QUESTION # 81
Which two statements are advantages of published data sources in comparison to embedded data sources? (Choose two.)
Answer: A,D
Explanation:
In Tableau, data sources can be embedded (stored within a workbook) or published (stored separately on Tableau Server). Let's define these and analyze the advantages:
Embedded Data Source: The connection details and any extract are bundled in the .twb or .twbx file.
Each workbook manages its own copy.
Published Data Source: The connection or extract is hosted on Tableau Server, reusable across multiple workbooks.
Now, let's evaluate the options:
Option C (Centralized data management is easier): Correct. Published data sources allow:
Single source of truth: One data source can serve multiple workbooks, ensuring consistency.
Unified updates: Refresh schedules, permissions, and metadata (e.g., calculated fields) are managed in one place via the Server UI.
Governance: Administrators can control access and monitor usage centrally. In contrast, embedded data sources require individual updates per workbook, leading to duplication and management overhead.
Option D (Storage space is conserved and resource usage during data refreshes is optimized):
Correct. With published data sources:
Storage: A single extract on the Server (e.g., a .hyper file) is shared across workbooks, avoiding redundant copies stored in each embedded workbook.
Refreshes: One refresh job updates the shared extract, reducing CPU and memory usage compared to multiple refreshes for duplicate embedded extracts. Embedded data sources replicate extracts, increasing disk space and refresh load.
NEW QUESTION # 82
What should you do to disable table recommendations for popular data sources and tables to users?
Answer: C
Explanation:
Table recommendations in Tableau Server suggest popular tables and data sources to users when they create new content in the web authoring environment. This feature is enabled by default but can be disabled at the site level.
Option A (Disable the option using the site Settings page): Correct. A site administrator can disable table recommendations by navigating to the site's Settings > General page in the Tableau Server web interface and unchecking the option "Enable table recommendations." This prevents users on that site from seeing these suggestions, offering a straightforward UI-based solution.
Option B (Use the command: tsm configuration set -k recommendations.enabled -v false): Incorrect. There is no recommendations.enabled key in the TSM configuration settings. This feature is managed per site, not server-wide via TSM.
Option C (Publish data sources only to projects with permissions locked): Incorrect. Locking permissions restricts access but doesn't disable the recommendation feature itself. Users with access would still see recommendations.
Option D (Disable the option using the server Settings page): Incorrect. Table recommendations are a site- specific setting, not a server-wide setting. The server Settings page (via TSM) controls global configurations, not this feature.
Reference: Tableau Server Documentation - "Manage Site Settings" (https://help.tableau.com/current/server/en-us/site_settings.htm).
NEW QUESTION # 83
You are the server administrator of a single-node Tableau Server installation. The server hosts five schedules that each execute once a day: Weekday 3:00 PM Extract Refresh, Weekday 5:00 PM Subscription, Weekday
2:00 AM Extract Refresh, Weekday 7:00 AM Extract Refresh, and Weekday 8:00 AM Subscription. The schedules are scheduled to execute during periods when Tableau Server is least active. The busiest period for your server is immediately after the workday begins at 9:00 AM. The office of the CEO reports that every morning at 9:00 AM, they access the views in a particular workbook. The data for these views is refreshed by a task associated with the 7:00 AM schedule. The CEO reports that the data in the views is only being refreshed about 70% of the time. What should you do to attempt to resolve the CEO's problem?
Answer: B
Explanation:
In Tableau Server, schedules manage tasks like extract refreshes and subscriptions. Each task within a schedule has a priority value (ranging from 1 to 100, where 1 is the highest priority and 100 is the lowest).
Tasks with higher priority (lower numbers) are executed before tasks with lower priority (higher numbers) when queued by the Backgrounder process. If the Backgrounder is overloaded or delayed, lower-priority tasks may not complete on time, leading to inconsistent refreshes.
In this scenario:
The 7:00 AM Extract Refresh task is critical for the CEO's workbook, but the data is only refreshed 70% of the time by 9:00 AM.
The server has a single node, meaning a single Backgrounder process handles all tasks. With five schedules (some overlapping in the early morning), contention or delays could prevent the 7:00 AM task from completing reliably before 9:00 AM.
Option C (Set the priority of this task to 1): Correct. Setting the task priority to 1 ensures it has the highest priority among all queued tasks. This increases the likelihood that the Backgrounder executes it promptly at 7:
00 AM, completing the refresh before the CEO accesses the workbook at 9:00 AM. You can adjust task priority in the Tableau Server web interface under Schedules > Tasks > Edit Priority.
Option A (Set the default priority of this schedule to 50): Incorrect. The default priority for schedules is already 50, and this option refers to the schedule's default, not the specific task. It wouldn't address the contention issue.
Option B (Set the priority for all other tasks to 50): Incorrect. This keeps all tasks at the default priority (50), leaving the 7:00 AM task without a relative advantage. It doesn't prioritize the CEO's task.
Option D (Set the priority of this task to 100): Incorrect. Priority 100 is the lowest, which would deprioritize the task, making the refresh even less reliable.
Reference: Tableau Server Documentation - "Manage Schedules and Tasks" (https://help.tableau.com/current
/server/en-us/schedule_manage.htm).
NEW QUESTION # 84
Several Tableau Server users published workbooks that have large extracts. After several weeks of use, the users abandoned the workbooks. What should you do to identify the abandoned workbooks?
Answer: A
Explanation:
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.
Reference: Tableau Server Documentation - "Administrative Views" (https://help.tableau.com/current/server
/en-us/adminview_stale_content.htm).
NEW QUESTION # 85
What is the minimum required free hard disk space recommended for a Tableau Server installation in production?
Answer: B
Explanation:
Tableau Server has specific hardware requirements for production environments to ensure stability and performance. The minimum recommended free disk space for a production installation is 50 GB.
This accounts for:
The installation itself (approximately 1-2 GB).
Space for log files, temporary files, and extracts managed by the File Store and Data Engine.
Room for backups and operational overhead.
The full minimum hardware recommendations for a single-node production deployment are:
8 CPU cores (2.0 GHz or faster).
32 GB RAM.
50 GB free disk space (on the system drive, typically C: on Windows).
Option B (50 GB): Correct. This matches Tableau's official recommendation for production environments.
NEW QUESTION # 86
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