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NEW QUESTION # 67
A client wants to provide sales users with the ability to perform the following tasks:
* Access published visualizations and published data sources outside the company network.
* Edit existing visualizations.
* Create new visualizations based on published data sources.
. Minimize licensing costs.
Which site role should the client assign to the sales users?
Answer: D
Explanation:
The Explorer (can publish) site role in Tableau is designed for users who need to access, edit, and create visualizations based on published data sources, even when they are outside the company network. This role allows users to perform web editing and save their work, making it suitable for sales users who need these capabilities. It is also a cost-effective option as it does not require the full capabilities and associated costs of the Creator license.
References: The information about the Explorer (can publish) role and its capabilities can be found in the official Tableau documentation on site roles and permissions12. This role is appropriate for users who need to interact with published content and create new visualizations without the need for full site administration or advanced content creation tools that come with the Creator role3.
NEW QUESTION # 68
A client has a pipeline dashboard that takes a long time to load. The dashboard is connected to only one large data source that is an extract.
It contains two calculated fields:
. TOTAL([Opportunities])
* SUM([Value])
It also contains two filters:
. A Relative Date filter on Created Date, a Date field containing values from 5 years ago until today
. A Multiple Values (Dropdown) filter on Account Name, a String field containing 1,000 distinct values A consultant creates a Performance Recording to troubleshoot the issue, and finds out that the longest-running event is "Executing Query." Which step should the consultant take to resolve this issue?
Answer: D
Explanation:
To improve the loading time of the pipeline dashboard, which primarily suffers from long query execution times due to a comprehensive Relative Date filter:
Relative Date Filter Issue: The existing Relative Date filter on "Created Date" covers a broad range (5 years), leading to significant data processing overhead as it includes granular date calculations over a large dataset.
Optimized Approach: By replacing the Relative Date filter with a Multiple Values (Dropdown) filter based on YEAR([Created Date]), the filter granularity is reduced. Filtering by year simplifies the query by limiting the volume of data processed and reducing the complexity of the filter condition.
Implementation Benefit: This approach still provides the flexibility to view data across different years but does so by reducing the load on the database during query execution, which is critical for improving the performance of the dashboard.
References
This recommendation aligns with Tableau performance optimization strategies, specifically regarding the management of date filters to minimize their impact on query load, as discussed in Tableau performance tuning sessions and guides.
NEW QUESTION # 69
A client wants to grant a user access to a data source hosted on Tableau Server so that the user can create new content in Tableau Desktop. However, the user should be restricted to seeing only a subset of approved data.
How should the client set up the filter before publishing the hyper file so that the Desktop user follows the same row-level security (RLS) as viewers of the end content?
Answer: A
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Tableau's row-level security (RLS) is applied at the data source level so that all users who connect to the data source-whether through Tableau Desktop, Server, or Cloud-see only the data they are permitted to see.
According to Tableau documentation:
* A Data Source Filter is the correct method for enforcing consistent row-level security for all users.
* When a Data Source Filter is applied before publishing, it becomes part of the data source's metadata and is applied every time any user connects to the published source.
* This ensures that users creating new workbooks in Tableau Desktop are governed by the same RLS as users viewing published dashboards.
Context filters and extract filters do not provide secure RLS:
* A Context Filter only applies inside the workbook where it is created. It does not enforce security in Tableau Desktop when the data source is reused.
* An Extract Filter physically removes rows from the extract but does not enforce role-based filtering or dynamic RLS.
* "Apply Filter to All Using Related Data Sources" affects workbook behavior, not published data source security.
A Data Source Filter applied prior to publishing is Tableau's documented approach for secure, reusable row- level security.
* Row-Level Security implementation guidance describing Data Source Filters as the foundation of secure RLS.
* Tableau Server publishing workflow indicating that Data Source Filters travel with the published source.
* Documentation on why Context and Extract Filters do not enforce user-dependent row-level security.
NEW QUESTION # 70
A client has a data source that stores a time stamp for each time a user interacts with a product feature. They visualize 3 years of data at the daily level. As adoption has grown over the last 6 months, the dashboard performance has steadily decreased, despite connecting via a data extract that is set to refresh every hour.
A Tableau consultant needs to improve performance of the dashboard with the least impact to the visualization.
Which option meets these requirements without additional cost?
Answer: B
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The dataset contains timestamps for each individual user interaction. Growth in user adoption over 6 months means the number of rows has expanded significantly. Tableau's performance documentation states that large row-level datasets can cause performance degradation even when using extracts, especially when:
* The visualization is aggregated to a higher level (such as daily), and
* The underlying extract still contains much more granular data than needed.
Tableau recommends pre-aggregating data before it reaches Tableau Desktop, which reduces extract size, memory use, and query time. This improves performance without changing what the visualization displays.
Option D uses Tableau Prep, which is included with Tableau Creator licensing and therefore incurs no additional cost. Tableau Prep can aggregate raw timestamp data into daily totals per product feature, which matches the visualization's actual granularity. This results in:
* A dramatically smaller extract
* Faster queries
* No change to how the dashboard looks or functions
Option A would remove product features from the visualization, altering the dashboard content and reducing insight, which does not meet the requirement of minimal impact.
Option B requires purchasing an external ETL tool, which violates the requirement of no additional cost.
Option C reduces the number of extract refreshes but does not improve dashboard performance; the data would remain equally granular and equally slow.
Therefore, Tableau Prep aggregation is the correct solution that improves performance while maintaining the same visualization and incurring no additional cost.
* Tableau performance guidelines recommending pre-aggregation of highly granular datasets.
* Tableau Prep documentation stating it can be used to aggregate data before creation of extracts.
* Tableau's extract optimization guidance describing how reducing row counts improves query and visualization performance.
NEW QUESTION # 71
A consultant updates an IF-THEN calculation to use a newly created calculated field "Last Name" (parsed from "Full Name"). After the change, performance becomes noticeably worse.
Which two options should the consultant use to improve dashboard performance without altering functionality? Choose two.
Answer: B,C
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The performance degradation originates from string parsing inside Tableau ("last word of Full Name") and then feeding that calculated field into another row-level IF-THEN calculation.
This creates:
* Nested calculations
* High per-row evaluation load
* Slow extract query performance or slow live query generation
Tableau documentation recommends two best-practice approaches:
Solution 1: Precompute the "Last Name" field upstream (Option C)
When the parsing is performed in:
* The database
* ETL/ELT pipelines
* Tableau Prep
then Tableau Desktop receives a clean field with no runtime computation needed.
This significantly reduces row-level calculation burden.
Solution 2: Replace Quick Filters with Action Filters (Option A)
Quick filters are expensive because Tableau:
* Runs additional queries to populate filter controls
* Re-queries every time the filter changes
Action Filters run directly from the visualization and are far more performant.
This improves the overall dashboard performance without changing logic.
Why the other options are incorrect:
B). Calculate "Last Name" inside the IF THEN calculation
This makes the expression even more complex - worse performance.
D). Change to a CASE statement
CASE does not improve performance when the heavy part of the logic is the string parsing, not the IF-THEN structure.
Thus, A and C are the correct performance-improving choices.
* Performance guidance recommending upstream computation of string fields
* Filter optimization best practices encouraging Action Filters over Quick Filters
* Extract runtime cost reduction strategies
NEW QUESTION # 72
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