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>> Latest Analytics-Con-301 Dumps Questions <<
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NEW QUESTION # 62
A client has a published data source in Tableau Server and they want to revert to the previous version of the data source. The solution must minimize the impact on users.
What should the consultant do to accomplish this task?
Answer: C
Explanation:
To minimize the impact on users when reverting to a previous version of a published data source in Tableau Server, the consultant should use the built-in revision history feature. By selecting a previous version from the revision history and clicking 'Restore', the data source will revert to that version without the need for a full server backup restoration or manual recreation of the data source. This process is quick and has the least amount of disruption to users.
References: The functionality and process for reverting to a previous version of a data source are outlined in Tableau's official documentation on working with content revisions1. This feature is part of Tableau Server's capabilities to manage and maintain data sources effectively21.
NEW QUESTION # 63
A business analyst needs to create a view in Tableau Desktop that reports data from both Excel and MSSQL Server.
Which two features should the business analyst use to create the view? Choose two.
Answer: A,D
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
To combine Excel and SQL Server data in the same logical data model, Tableau offers two supported capabilities:
Relationships
* Recommended modern method for combining tables from multiple sources.
* Supports cross-database relationships between Excel and SQL Server.
* Maintains separate physical layers but integrates data at query time.
Cross-Database Joins
* Allows joining data from different databases in the physical layer.
* Fully supported for Excel + MS SQL Server.
* Useful when granular row-level merging is needed.
Why the other options are incorrect:
C). Data Blending
* Legacy feature, used only when no direct combination is possible.
* Tableau recommends relationships instead.
* Produces separate queries and may lose row-level detail.
D). Union
* Requires tables to have equivalent structure.
* Cannot union Excel with SQL Server unless identical column structure exists.
* Not appropriate for most mixed-source reporting.
Therefore, the correct techniques are Relationships and Cross-Database Joins.
* Tableau data modeling documentation recommending Relationships for multi-source modeling.
* Cross-database join support list including Excel + SQL Server.
NEW QUESTION # 64
A client is working in Tableau Prep and has a field named Orderld that is compiled by country, year, and an order number as shown in the following table.
What should the consultant use to transform the table in the most efficient manner?
Answer: C
Explanation:
To transform the Orderld field in Tableau Prep, the Split option is the most efficient and straightforward method. Here's how you can apply it:
* In Tableau Prep, drag your dataset into the flow.
* Click on the Orderld field in the workspace to select it.
* Look for the option in the toolbar that says "Split" and select it.
* Choose "Automatic Split" if the delimiters (such as hyphens) are consistent; Tableau Prep should automatically detect the hyphen as the delimiter and split the Orderld into multiple new fields.
* The dataset should now show new columns: one for the country code (CA, FR, US), one for the year (2017), and one for the order number (152156, 152157, etc.).
The Split option works effectively here because it automatically identifies and uses the hyphen as the delimiter to divide the original Orderld into the desired components without manual specification of conditions or writing any formulas.
References
This procedure is based on the standard functionalities provided in Tableau Prep for splitting a field into multiple columns based on a delimiter, as described in the Tableau Prep user guide.
NEW QUESTION # 65
A new Tableau user created a simple dashboard on Tableau Server using supply chain data. Now, the user wants to know if they created the dashboard in accordance with specific performance best practices.
Which approach should the consultant recommend for the client to make this determination?
Answer: C
Explanation:
The Workbook Optimizer is a tool specifically designed to evaluate a workbook against performance best practices. It provides feedback on key design characteristics and offers concrete guidance on how to improve workbook performance. This tool is beneficial for both new and experienced Tableau users to ensure their dashboards are optimized for performance1.
References: The Workbook Optimizer's functionality is detailed in Tableau's official documentation, which explains how it assesses workbooks against a set of rules derived from best practices1. Additionally, the Performance Recording feature in Tableau Desktop and Server can be used to identify performance issues, but the Workbook Optimizer gives a more comprehensive analysis of the workbook's adherence to best practices23.
NEW QUESTION # 66
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 # 67
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