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Title: Pass Guaranteed Salesforce - Analytics-Con-301 - Salesforce Certified Tableau Co [Print This Page]
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Title: Pass Guaranteed Salesforce - Analytics-Con-301 - Salesforce Certified Tableau Co
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Salesforce Analytics-Con-301 Exam Syllabus Topics:
TopicDetails
Topic 1
  • 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.
Topic 2
  • Business Consulting: For Tableau Consultants, this section involves designing and troubleshooting calculations and workbooks to meet advanced analytical use cases. It covers selecting appropriate chart types, applying Tableau¡¯s order of operations in calculations, building interactivity into dashboards, and optimizing workbook performance by resolving resource-intensive queries and other design-related issues.
Topic 3
  • Data Analysis: This domain targets Tableau Consultants to plan and prepare data connections effectively. It includes recommending data transformation strategies, designing row-level security (RLS) data structures, and implementing advanced data connections such as Web Data Connectors and Tableau Bridge. Skills in specifying granularity and aggregation strategies for data sources across Tableau products are emphasized.
Topic 4
  • 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 5
  • 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.

Salesforce Certified Tableau Consultant Sample Questions (Q100-Q105):NEW QUESTION # 100
A consultant wants to improve the performance of reports by moving calculations to the data layer and materializing them in the extract.
Which calculation should the consultant use?
Answer: A
Explanation:
END
Explanation:
To improve performance by moving calculations to the data layer and materializing them in the extract, the consultant should choose calculations that benefit from pre-computation and significantly reduce the load during query time:
Aggregation-Level Calculation: The formula SUM([Profit])/SUM([Sales]) calculates a ratio at an aggregate level, which is ideal for pre-computation. Materializing this calculation in the extract means that the complex division operation is done once and stored, rather than being recalculated every time the report is accessed.
Performance Improvement: By pre-computing this aggregate ratio, Tableau can utilize the pre-calculated fields directly in visualizations, which speeds up report loading and interaction times as the heavy lifting of data processing is done during the data preparation stage.
References:
Materialization in Extracts: This concept involves pre-calculating and storing complex aggregations or calculations within the Tableau data extract itself, improving performance by reducing the computational load during visualization rendering.

NEW QUESTION # 101
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: C
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 # 102
A client notices that while creating calculated fields, occasionally the new fields are created as strings, integers, or Booleans. The client asks a consultant if there is a performance difference among these three data types.
What should the consultant tell the customer?
Answer: A
Explanation:
In Tableau, the performance of calculated fields can vary based on the data type used. Calculations involving integers and Booleans are generally faster than those involving strings. This is because numerical operations are typically more efficient for a computer to process than string operations, which can be more complex and time-consuming. Therefore, when performance is a consideration, it is advisable to use integers or Booleans over strings whenever possible.
References: The performance hierarchy of data types in Tableau calculations is documented in resources that discuss best practices for optimizing Tableau performance1.

NEW QUESTION # 103
A data analyst sets up a calculation to filter a dashboard so that it displays only the users' information. The dashboard will then be published to Tableau Cloud.
The data analyst plans to use the following calculation to filter the data: USERNAME() = [Correct Answer] Which column in the table below should the data analyst reference in the calculation?

Answer: A
Explanation:
When dashboards are published to Tableau Cloud, the function USERNAME() returns the user's Tableau Cloud username, which is the email address associated with their Tableau Cloud account.
Tableau documentation states:
* On Tableau Cloud, the value returned by USERNAME() is always the user's email address.
* Row-Level Security (RLS) is typically implemented using a comparison of USERNAME() to an email field in the data source.
* For secure filtering, the field compared to USERNAME() must match the authentication identity exactly.
Looking at the provided table:
* "Abbreviated Name" contains short custom codes like "SMiller," which do not match Tableau Cloud usernames.
* "Lower Case Name" contains names like "sean miller," which also do not match Tableau Cloud usernames.
* "Email" contains the full email address for each user, such as "Sean.Miller@superstore.com," which is the only field that corresponds to what USERNAME() returns in Tableau Cloud.
Therefore, the correct field to reference is Email.
* Tableau Cloud authentication documentation stating USERNAME() returns the user's email address.
* Row-Level Security setup guidance recommending the comparison USERNAME() = [Email Field].
* Tableau security practices indicating only the email column will match USERNAME() values on Tableau Cloud.

NEW QUESTION # 104
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: C
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 # 105
......
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