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Salesforce Analytics-Con-301 考試大綱：

主題	簡介
主題 1	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.
主題 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.
主題 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.
主題 4	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.
主題 5	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.

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最新的 Salesforce Consultant Analytics-Con-301 免費考試真題 (Q98-Q103):問題 #98
A client requests a published Tableau data source that is connected to SQL Server. The client needs to leverage the multiple tables option to create an extract. The extract will include partial data from the SQL Server data source.
Which action will reduce the amount of data in the extract?

• A. Set up the extract as an incremental refresh.
• B. Define the filters by using custom SQL.
• C. Use an extract filter.
• D. Aggregate the extract to the visible dimensions.
  

答案：C
解題說明：
Using an extract filter is an effective way to reduce the amount of data in a Tableau extract. Extract filters allow you to specify a subset of the data to include, which can significantly decrease the size of the extract by excluding unnecessary data. This is particularly useful when you only need partial data from a larger SQL Server data source.
References: The recommendation to use extract filters to reduce data size is supported by Tableau's best practices for optimizing extracts. These practices suggest keeping the extract's data set short through filtering1. Additionally, discussions in the Tableau Community confirm that hiding fields and using extract filters before extracting data can help reduce the extract size2.
When dealing with large datasets in SQL Server and needing to create a manageable extract in Tableau, using an extract filter is the most direct and effective method to limit the data included:
Extract Filter: This involves setting filters that apply directly when the data is extracted from the source. This means that only the data meeting the specified criteria will be extracted and loaded into Tableau, significantly reducing the size of the extract.
To apply an extract filter, in the Data Source page in Tableau, drag the fields you want to filter by to the Filters shelf. Then, configure the desired filter criteria. When you create the extract, choose the option to "Add Filters to Extract" and select the configured filters. This ensures that only the data that meets these conditions is extracted from the SQL Server.
This approach not only minimizes the data volume but also speeds up performance in Tableau because it processes a smaller subset of the full dataset.
References
This procedure is described in detail in Tableau's help documentation on managing extracts and optimizing performance by using extract filters, which is recommended for scenarios involving large datasets or when specific subsets of data are required for analysis.

問題 #99
A client has a Tableau Cloud deployment. Currently, dashboards are available only to internal users.
The client needs to embed interactive Tableau visualizations on their public website.
Data is < 5,000 rows, updated infrequently via manual refresh.
Cost is a priority.
Which product should the client use?

• A. Tableau Server licensed per core
• B. Tableau Cloud licensed per user
• C. Tableau Embedded Analytics
• D. Tableau Public
  

答案：D
解題說明：
Comprehensive and Detailed Explanation From Exact Extract:
Tableau documentation explains:
Tableau Public
* Free platform.
* Allows public sharing and embedding of fully interactive dashboards.
* Ideal for small datasets and infrequent updates.
* Does not require user-based licensing.
* Embedding is unrestricted because all content is publicly visible.
This perfectly matches the scenario:
# Public-facing website
# Low cost priority
# Small dataset
# Manual, infrequent updates
Why the other options are incorrect:
A). Tableau Cloud (per user)
* Requires paid licenses.
* Does not allow unrestricted public embedding without expensive add-ons.
* Designed for secure internal use, not public web-wide embedding.
C). Tableau Embedded Analytics
* A paid embedding solution requiring proper licensing.
* Designed for large-scale, secure, programmatic embedding - too costly for this use case.
D). Tableau Server (per core)
* Requires server infrastructure & licensing.
* Far more expensive than Tableau Public.
Thus, Tableau Public is the correct, cost-effective solution.
* Tableau Public documentation describing free embedding for public websites.
* Comparison guides showing Tableau Cloud/Server require licensing for embedding.
* Public vs. Enterprise Tableau deployment best practices.

問題 #100
A client collects information about a web browser customers use to access their website. They then visualize the breakdown of web traffic by browser version.
The data is stored in the format shown below in the related table, with a NULL BrowserID stored in the Site Visitor Table if an unknown browser version accesses their website.

The client uses "Some Records Match" for the Referential Integrity setting because a match is not guaranteed.
The client wants to improve the performance of
the dashboard while also getting an accurate count of site visitors.
Which modifications to the data tables and join should the consultant recommend?

• A. Continue to use NULL as the BrowserID in the Site Visitor Table and change the Referential Integrity to "All Records Match."
• B. Continue to use NULL as the BrowserID in the Site Visitor Table and leave the Referential Integrity set to "Some Records Match."
• C. Add an "Unknown" option to the Browser Table, reference its BrowserID in the Site Visitor Table, and change the Referential Integrity to "All Records Match."
• D. Add an "Unknown" option to the Browser Table, reference its BrowserID in the Site Visitor Table, and leave the Referential Integrity set to
  "Some Records Match."
  

答案：C
解題說明：
To improve the performance of a Tableau dashboard while maintaining accurate counts, particularly when dealing with unknown or NULL BrowserIDs in the data tables, the following steps are recommended:
* Modify the Browser Table: Add a new row to the Browser Table labeled "Unknown," assigning it a unique BrowserID, e.g., 0 or 4.
* Update the Site Visitor Table: Replace all NULL BrowserID entries with the BrowserID assigned to the "Unknown" entry. This ensures every record in the Site Visitor Table has a valid BrowserID that corresponds to an entry in the Browser Table.
* Change Referential Integrity Setting: Change the Referential Integrity setting from "Some Records Match" to "All Records Match." This change assumes all records in the primary table have corresponding records in the secondary table, which improves query performance by allowing Tableau to make optimizations based on this assumption.
References:
Handling NULL Values: Replacing NULL values with a valid unknown option ensures that all data is included in the analysis, and integrity between tables is maintained, thereby optimizing the performance and accuracy of the dashboard.

問題 #101
A consultant is designing a dashboard that will be consumed on desktops, tablets, and phones. The consultant needs to implement a dashboard design that provides the best user experience across all the platforms.
Which approach should the consultant take to achieve these results?

• A. Build one dashboard and set the size to Automatic.
• B. Build one dashboard and fix the size of the dashboard.
• C. Build one dashboard that has desktop, tablet, and phone layouts, and fix the size of the layouts.
• D. Build one dashboard for each type of device and fix the size of the layouts.
  

答案：C
解題說明：
For a consultant designing a dashboard to be consumed across multiple device types, the best approach is:
* Multi-device Layout: Tableau provides the capability to design device-specific layouts within a single dashboard. This feature allows the dashboard to adapt its layout to best fit the screen size and orientation of desktops, tablets, and phones.
* Fixed Size Layouts: By fixing the size of each layout, the consultant can ensure that the dashboard appears consistent and maintains the intended design elements and user experience across devices.
Fixed sizes prevent components from resizing in ways that could disrupt the dashboard's readability or functionality.
* Implementation: In Tableau, you can create these layouts by selecting 'Device Preview' and adding custom layouts for each device type. Here, you define the dimensions and the positioning of sheets and controls tailored to each device's typical viewing mode.
References
This approach leverages Tableau's device designer capabilities, which are specifically designed to optimize dashboards for multiple viewing environments, ensuring a seamless user experience regardless of the device used. This functionality is well documented in Tableau's official guides on creating and managing device- specific dashboards.

問題 #102
A Tableau consultant is tasked with creating a line graph that shows daily temperature fluctuations. The below set of data to use to create a dashboard.
How should the consultant manipulate the data to support the business need?

• A. Pivot the data before the requested visualization can be created.
• B. Create a Level of Detail (LOD) calculation that will aggregate the data at the requested daily level.
• C. Request a new set of data that is aggregated to the day level.
  

答案：C
解題說明：
The business requirement is:
"Create a line graph that shows daily temperature fluctuations."
The dataset provided contains:
* Only 5 rows, one per month
* Two aggregated columns: Avg High Temp and Avg Low Temp
* No daily values in the dataset
Tableau's documentation states that:
* Tableau cannot generate artificial granularity that does not exist in the underlying data.
* LOD calculations cannot create detail that isn't present in the source. They can only roll up or fix existing grain; they cannot fabricate lower-grain data.
* Pivoting only reshapes data; it does not create missing days or introduce new rows.
* When the visualization requires detail that the dataset does not contain, the correct solution is to obtain data at the required level of granularity.
Because the dataset contains monthly averages, it is impossible to show day-to-day fluctuations without having the actual daily temperatures.
Therefore, the only way to support the business need is to request daily-level data from the data provider.
Why the other options are incorrect:
A). Pivot the data
Pivoting would convert the dataset from wide format to long format (e.g., "Avg High Temp" and "Avg Low Temp" into a single "Temperature Type" field).
This does not add daily rows, so the required daily line graph still cannot be built.
C). Create an LOD calculation
LOD expressions cannot create new lower-level detail.
They only aggregate or fix existing detail.
Because the dataset contains only monthly values, an LOD cannot generate daily temperatures.
* Tableau granularity and data modeling guidance stating that detail must exist in the data to be visualized.
* LOD expression documentation explaining that LODs cannot create lower granularity than the source data.
* Pivoting documentation explaining pivots reshape fields but do not generate new rows or finer-grain data.

問題 #103
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