真実的なAnalytics-Con-301受験記 &合格スムーズAnalytics-Con-301関連資格試験対応 |実用的なAnalytics-Con-301専門トレーリングSalesforce Certified Tableau Consultant

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Salesforce Analytics-Con-301 認定試験の出題範囲：

トピック出題範囲
トピック 1
  • 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.
トピック 2
  • 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.
トピック 3
  • 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.

>> Analytics-Con-301受験記 <<

Analytics-Con-301関連資格試験対応 & Analytics-Con-301専門トレーリング

社会と経済の発展につれて、多くの人はIT技術を勉強します。なぜならば、IT職員にとって、SalesforceのAnalytics-Con-301資格証明書があるのは肝心な指標であると言えます。自分の能力を証明するために、Analytics-Con-301試験に合格するのは不可欠なことです。弊社のAnalytics-Con-301真題を入手して、試験に合格する可能性が大きくなります。

Salesforce Certified Tableau Consultant 認定 Analytics-Con-301 試験問題 (Q90-Q95):

質問 # 90
A consultant wants to improve the performance of reports by moving calculations to the data layer and materializing them in the extract.
Which type of calculation is the consultant able to move?

正解：D

解説：
Comprehensive and Detailed Explanation From Exact Extract:
Tableau allows certain calculations to be materialized in extracts, meaning they are precomputed and stored inside the .hyper file to improve performance.
According to Tableau's extract documentation:
* Materializable calculations must be compatible with the extract engine and must not depend on dynamic, view-based, or post-query logic.
* Only row-level calculations and aggregation-level calculations without dependencies on runtime context can be materialized.
* Tableau cannot materialize any calculation containing:
* Table calculation functions
* Functions requiring post-aggregation logic
* View-dependent elements
* Parameters that need runtime evaluation
Evaluation of the choices:
A). A row-level calculation - Correct
Row-level calculations operate on each record individually before aggregation.
Tableau documentation specifies that these calculations can be pushed down into the extract and materialized because they do not depend on the visualization or user interaction.
Examples include concatenation, arithmetic, string manipulation, and row-based logic such as:
[Sales] * [Quantity] or IF [Region] = 'West' THEN 1 END
These can be precomputed inside the extract, improving performance.
B). A calculation that contains table calculation functions - Not allowed Table calculations (WINDOW_SUM, INDEX, RUNNING_SUM, RANK, etc.) depend on the table structure after aggregation and query execution.
Therefore, Tableau documentation states they cannot be materialized in extracts.
C). A calculation that contains parameters - Not allowed
Parameters are evaluated at runtime, meaning the user can change their value.
Because of this, Tableau cannot permanently compute and store such a calculation inside an extract.
D). A calculation that contains an aggregation - Generally not materialized Aggregated calculations often depend on query context and cannot always be materialized.
Only simple, context-free aggregations might be materialized, but Tableau explicitly warns that aggregations are not guaranteed candidates for extract materialization.
Thus, this is not the best answer compared to row-level logic.
Conclusion
Only row-level calculations meet Tableau's exact requirements for materialization in extracts.
References From Tableau Consultant Documentation
* Tableau Extract documentation describing materializable calculation types.
* Tableau guidance stating table calculations and parameter-dependent calculations cannot be materialized.
* Extract optimization guidelines describing row-level logic as eligible for materialization.


質問 # 91
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?

正解：C

解説：
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.


質問 # 92
An online sales company has a table data source that contains Order Date. Products ship on the first day of each month for all orders from the previous month.
The consultant needs to know the average number of days that a customer must wait before a product is shipped.
Which calculation should the consultant use?

正解：D

解説：
The correct calculation to determine the average number of days a customer must wait before a product is shipped is to first find the shipping date, which is the first day of the following month after the order date. This is done using DATETRUNC('month', DATEADD('month', 1, [Order Date])). Then, the average difference in days between the order date and the shipping date is calculated using AVG(DATEDIFF('day', [Order Date], [Calc1])). This approach ensures that the average wait time is calculated in days, which is the most precise measure for this scenario.
References: The solution is based on Tableau's date functions and their use in calculating differences between dates, which are well-documented in Tableau's official learning resources and consultant documents12.
To calculate the average waiting days from order placement to shipping, where shipping occurs on the first day of the following month:
Calculate Shipping Date (Calc1): Use the DATEADD function to add one month to the order date, then apply DATETRUNC to truncate this date to the first day of that month. This represents the shipping date for each order.
Calculate Average Wait Time (Calc2): Use DATEDIFF to calculate the difference in days between the original order date and the calculated shipping date (Calc1). Then, use AVG to average these differences across all orders, giving the average number of days customers wait before their products are shipped.
References:
Date Functions in Tableau: Functions like DATEADD, DATETRUNC, and DATEDIFF are used to manipulate and calculate differences between dates, crucial for creating metrics that depend on time intervals, such as customer wait times in this scenario.


質問 # 93
A Tableau Cloud client has requested a custom dashboard to help track which data sources are used most frequently in dashboards across their site.
Which two actions should the client use to access the necessary metadata? Choose two.

正解：B、C

解説：
To track which data sources are used most frequently across a site in Tableau Cloud, the client should use the GraphiQL engine and the Metadata API. The GraphiQL engine allows for interactive exploration of the metadata, making it easier to construct and test queries1. The Metadata API provides access to metadata and lineage of external assets used by the content published to Tableau Cloud, which is essential for tracking data source usage2.
References: The actions are based on the capabilities of the GraphiQL engine and the Metadata API as described in Tableau's official documentation and learning resources321.


質問 # 94
A client wants to report Saturday and Sunday regardless of the workbook's data source's locale settings.
Which calculation should the consultant recommend?

正解：A

解説：
The calculation DATEPART('iso-weekday', [Order Date])=1 or DATEPART('iso-weekday', [Order Date])=7 is recommended because the ISO standard considers Monday as the first day of the week (1) and Sunday as the last day (7). This calculation will correctly identify Saturdays and Sundays regardless of the locale settings of the workbook's data source, ensuring that the report includes these days as specified by the client.
References: The use of the 'iso-weekday' part in the DATEPART function is consistent with the ISO 8601 standard, which is independent of locale settings. This approach is supported by Tableau's documentation on date functions and their behavior with different locale settings123.
To accurately identify weekends across different locale settings, using the 'iso-weekday' component is reliable as it is consistent across various locales:
ISO Weekday Function: The ISO standard treats Monday as the first day of the week (1), which makes Sunday the seventh day (7). This standardization helps avoid discrepancies in weekday calculations that might arise due to locale-specific settings.
Identifying Weekends: The calculation checks if the 'iso-weekday' part of the date is either 1 (Sunday) or 7 (Saturday), thereby correctly identifying weekends regardless of the locale settings.
References:
Handling Locale-Specific Settings: Using ISO standards in date functions allows for uniform results across systems with differing locale settings, essential for consistent reporting in global applications.


質問 # 95
......

Analytics-Con-301試験問題の99％の合格率を確保できます。 Analytics-Con-301学習ガイドを使用すると、Analytics-Con-301試験に合格できます。そして、Salesforceはあなたが想像できる正しいことです。必ずAnalytics-Con-301認定を希望します。そこで、Analytics-Con-301試験資料と同じくらい優れたツールを使用して、わずか20〜30時間勉強して練習してから試験に合格してみませんか。さまざまなバージョンのSalesforce Certified Tableau Consultant試験ブレーンダンプを使用すると、いつでもどこでも勉強と練習ができます。

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