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Title: Analytics-Con-301 Exam Experience - Analytics-Con-301 Free Dumps
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Salesforce Analytics-Con-301 Exam Syllabus Topics:
TopicDetails
Topic 1
  • IT Management: This domain measures skills related to managing Tableau environments. It includes planning server upgrades, recommending deployment solutions (on-premise or cloud), and ensuring alignment between technical and business requirements for analytics infrastructure. It also involves troubleshooting and optimizing system performance relevant to Tableau Server and Cloud deployments.
Topic 2
  • Data Visualization: This section evaluates the Tableau Consultant¡¯s ability to design effective visual analytics solutions. It involves creating dashboards and visual reports that enhance user understanding, employing techniques like dynamic actions and advanced chart types, and ensuring performance optimization for an interactive user experience.
Topic 3
  • Business Analysis: This section of the exam measures skills of Tableau Consultants focusing on evaluating the current state of analytics within an organization. It covers mapping business needs to Tableau capabilities, translating analytical requirements to best practices in Tableau, and recommending appropriate deployment options like Tableau Server or Tableau Cloud. It also includes evaluating existing data structures for supporting business needs and identifying performance risks and opportunities.

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Salesforce Certified Tableau Consultant Sample Questions (Q12-Q17):NEW QUESTION # 12
A client calculates the percent of total sales for a particular region compared to all regions.

The Sales percentage is inadvertently recalculated each time the filter is applied to the Region.

Which calculation should fix the automatic recalculation on the % of total field?
Answer: D
Explanation:
The problem:
The client wants:
Percent of total sales for each region compared to ALL regions,
even when Region is filtered.
However, the calculation currently behaves like a table calculation:
SUM([Sales]) / TOTAL(SUM([Sales]))
This recalculates the total after Region filters are applied, so removing a region changes the denominator.
Tableau Documentation - How to prevent recalculation:
To keep percent-of-total unchanged when filtering, Tableau's recommended method is to use FIXED LOD expressions to lock the granularity.
Two values must be fixed:
* Numerator: Sales for that specific region{ FIXED [Region] : SUM([Sales]) }
* Denominator: Total sales across all regions, independent of filters{ FIXED : SUM([Sales]) }(FIXED with no dimension = entire data set) Then compute the percentage:
{ FIXED [Region] : SUM([Sales]) } / { FIXED : SUM([Sales]) }
This ensures:
* The region sales remain accurate.
* The overall total remains constant, even if filters remove regions.
* Region filtering no longer recalculates percent-of-total.
Why the other options are incorrect:
A). {FIXED [Region]: SUM([Sales])} / SUM([Sales])
The denominator is still affected by filters # recalculates % of total.
B). {FIXED [Region]: SUM([Sales])} / { [Sales] }
{[Sales]} is not valid syntax and does not fix granularity.
D). {FIXED [Region]: SUM([Sales])}
This gives only the numerator - no percent-of-total calculation.
The only correct LOD solution is option C.
* Tableau LOD Expression Guide: FIXED for filter-independent calculations.
* Tableau Percent-of-Total Best Practices: use FIXED LOD to avoid recalculation when filters change.
* Order of Operations: FIXED LODs occur before dimension filters, keeping totals stable.

NEW QUESTION # 13
A consultant is creating a dashboard to report on hourly sales data. The data should be refreshed hourly and is used for timely decision-making, so it is important to alert dashboard viewers when data has not been refreshed.
Which feature of Tableau Catalog should the consultant use to ensure dashboard viewers understand this message?
Answer: A
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Tableau Catalog provides multiple features for communicating data quality and freshness.
Data Quality Warnings (DQWs) are part of Catalog's metadata management system and are specifically designed to inform users about data issues, including when data is stale.
There are two visibility levels:
1. Standard Visibility Data Quality Warning
* Appears subtly in metadata panels.
* Intended for non-critical issues.
* Does not guarantee the message will be seen by dashboard viewers.
2. High Visibility Data Quality Warning
* Designed for urgent, critical, and highly visible alerts.
* Displays a prominent warning indicator directly on connected dashboards, data sources, and workbooks.
* Tableau documentation states high-visibility warnings are used when users must be alerted, such as:
* Stale data
* Incomplete refreshes
* Data outages
Because the question emphasizes:
"important to alert dashboard viewers when data has not been refreshed" A standard warning is not strong enough, but a High Visibility Data Quality Warning is explicitly designed for this scenario.
Evaluation of the choices:
A). Standard Visibility Data Quality Warning - Not sufficient
It does not force dashboard users to notice the warning.
B). High Visibility Data Quality Warning - Correct
This option is specifically meant to notify users of critical freshness issues, making it the perfect match for the requirement.
C). Certified Data Source - Incorrect
Certification communicates trustworthiness, not freshness or alerts.
D). Lineage - Incorrect
Lineage shows data relationships and dependencies, not refresh warnings.
Conclusion
To alert viewers about stale data in hourly-refreshed dashboards, the consultant must use a High Visibility Data Quality Warning.
References From Tableau Catalog Documentation
* Description of Data Quality Warnings and their visibility levels.
* Definition of High Visibility DQWs as critical alerts shown to dashboard viewers.
* Catalog guidelines for stale data detection and communication.

NEW QUESTION # 14
A client needs to design row-level security (RLS) measures for their reports. The client does not currently have Tableau Data Management Add-on, and it may be an option in the future.
What should the consultant recommend as the safest and easiest way to manage for the long term?
Answer: B
Explanation:
For implementing row-level security (RLS) without the Tableau Data Management Add-on, the best approach is to integrate user filters into the published data source:
Creating User Filters on Published Data Source: This method involves defining user filters that apply directly to the data source before it is published to the Tableau Server. This ensures that any workbook or view leveraging this data source inherently respects the row-level security settings.
To implement this, create a calculated field in Tableau that defines the security logic, typically using a formula that references user functions (like USERNAME() or ISMEMBEROF()). Drag this field to the Filters shelf and configure it to match the security rules (who can see what data).
Once configured, publish the data source to Tableau Server with these filters in place. This approach centralizes security management, making it easier to maintain and update security policies as they are applied universally to all workbooks using this data source.
This strategy is safe as it reduces the risk of accidental data exposure through individual workbook misconfiguration and simplifies long-term maintenance of security policies.
References
This method follows Tableau's best practices for implementing row-level security as detailed in Tableau's security management resources. It ensures robust, maintainable security measures that scale with organizational needs without requiring additional add-ons.

NEW QUESTION # 15
A client has a dashboard that uses a bar chart to visualize sales by Sub-Category and a detail table that has all the orders for the products within Sub- Category. The table has more than 10,000 rows of data and is slow to load.
A consultant plans to add an action so when the client interacts with the bar chart, only the relevant data appears in the table.
What will provide the fastest rendering of the dashboard?
Answer: D
Explanation:
To optimize the dashboard rendering, particularly when dealing with a large dataset, a filter action is the most effective tool. Here's why the specified choice is optimal:
Add a filter action: This action creates a direct filter on the detail table based on the selection in the bar chart. It ensures that only data related to the selected sub-category is loaded into the table, significantly reducing load time and improving performance.
Set "Run action on" to Select: This setting means the filter action will be triggered as soon as the user selects a bar in the bar chart. Immediate activation of the filter ensures that the dashboard is interactive and responsive.
Set "Clearing the selection will" to Exclude all values: When the selection is cleared, this setting ensures that no data is shown, which avoids loading the entire dataset unnecessarily. This maintains performance when no sub-category is actively selected.
References
This strategy follows Tableau's performance best practices by using actions to limit the amount of data processed and rendered, as detailed in the Tableau User Guide and training materials on Dashboard Actions for optimizing large datasets.

NEW QUESTION # 16
A consultant builds a report where profit margin is calculated as SUM([Profit]) / SUM([Sales]). Three groups of users are organized on Tableau Server with the following levels of data access that they can be granted.
. Group 1: Viewers who cannot see any information on profitability
. Group 2: Viewers who can see profit and profit margin
. Group 3: Viewers who can see profit margin but not the value of profit Which approach should the consultant use to provide the required level of access?
Answer: A
Explanation:
The approach of using user filters to control access to data on profitability for Groups 2 and 3, combined with a calculated field that restricts the visibility of profit value to only Group 2, aligns with Tableau's best practices for managing content permissions. This method ensures that each group sees only the data they are permitted to view, with Group 1 not seeing any profitability information, Group 2 seeing both profit and profit margin, and Group 3 seeing only the profit margin without the actual profit values. This setup can be achieved through Tableau Server's permission capabilities, which allow for detailed control over what each user or group can see and interact with12.
References: The solution is based on the capabilities and permission rules that are part of Tableau Server's security model, as detailed in the official Tableau documentation12. These resources provide guidance on how to set up user filters and calculated fields to manage data access levels effectively.

NEW QUESTION # 17
......
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