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最新的 Salesforce Consultant Analytics-Con-301 免費考試真題 (Q24-Q29):

問題 #24
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?

• A. The Aliases option
• B. The Split option
• C. A calculated field that uses the TRIM function
• D. A calculated field that uses the LEFT function

答案：B

解題說明：
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.

問題 #25
A Tableau consultant is tasked with choosing a method of setting up row-level security (RLS) entitlements with tables during a Tableau implementation. The consultant has received a set of roles from a client in one normalized table, and a set of entitlements from the client in another normalized table.
The consultant plans on using the deepest granularity method. However, when the consultant gains access to the final set of data, they discover duplicate values at the lowest level. Most of the regions in the client's dataset contain sub-regions named 'East' and 'West'. However, some regions have a 'Null' value for sub-region.

How should the consultant proceed?

• A. Use sparse entitlements because it is the most performant and the duplicate values will be handled by the mapping table.
• B. Use sparse entitlements because it defines entitlements at every level of the hierarchy and can handle duplicate values in the dataset.
• C. Use the deepest granularity method because it is the most performant and the duplicate values will be handled by the mapping table.
• D. Use the deepest granularity method because it defines entitlements at every level of the hierarchy and can handle duplicate values in the dataset.

答案：B

解題說明：
Comprehensive and Detailed Explanation From Exact Extract:
Tableau's RLS entitlement design patterns include:
1. Deepest Granularity Method
* Requires one unique role # one unique lowest-level value pairing.
* Fails when the dataset contains duplicate lowest-level values (e.g., multiple "East" sub-regions across different regions).
* Cannot operate correctly when some lowest-level values are NULL.
Thus, the deepest granularity method is not valid here.
2. Sparse Entitlements Method
Tableau documentation states:
* Sparse entitlements define RLS at each level of the hierarchy instead of only at the lowest level.
* This method supports duplicate lowest-level values.
* Handles scenarios where some levels are NULL because higher-level entitlements (e.g. Region = AMER) can still correctly apply.
* More flexible for hierarchical geographic structures (Region # Sub-Region # Country, etc.).
Given the client's dataset:
* Multiple "East" and "West" sub-regions
* Some "Null" sub-regions
* Hierarchical levels present
Sparse entitlements is the only correct and supported choice.
Why the incorrect options are wrong:
A & B - Deepest Granularity
* Deepest granularity fails when the lowest-level values are not unique.
* It cannot handle NULL values at the lowest tier.
* Performance is not superior in this scenario.
D - Sparse because it is most performant
Performance is not the defining advantage.
Flexibility and ability to handle duplicate lowest-level values is.
Thus, C is the correct statement.
* RLS entitlement design patterns: deepest vs. sparse entitlements.
* Rules requiring unique lowest-level identifiers for deepest granularity.
* Guidance stating sparse entitlements should be used when duplicates or NULL values exist in hierarchical structures.

問題 #26
A university has data on its undergraduate students and their majors by grade level (Freshman, Sophomore, Junior, Senior). The university is interested in visualizing the path students take as they change majors across grade levels.
Which visualization type should the consultant recommend?

• A. Tree Chart
• B. Radar Chart
• C. Chord Chart
• D. Sankey Diagram

答案：D

解題說明：
To visualize the path students take as they change majors across different grade levels, a Sankey Diagram is highly effective. This type of visualization illustrates the flow and quantity between different stages or categories:
Sankey Diagram: It allows for a visual representation of students' movements between majors over time. Each flow's thickness is proportional to the number of students moving from one major to another, giving a clear, immediate visual cue of major popularity and student migration patterns.
To create a Sankey Diagram in Tableau, you typically need to prepare the data specifically for this type of chart. The data must include source (starting major), target (ending major), and the value (number of students). It often requires custom calculations and data reshaping to get the data in a format that a Sankey can use.
Once the data is prepared, you can use a combination of calculated fields, path binning, and line charts to simulate the flow effect in Tableau. External plugins or web-based integrations might also be employed for more direct implementations.
References
Sankey Diagrams are not natively supported in Tableau but can be implemented through creative use of data preparation and calculations, as suggested in advanced Tableau user communities and demonstrated in various Tableau public galleries.

問題 #27
A transport and delivery company uses a command center dashboard in its logistics and distribution hubs. The dashboard is displayed on screens to show fleet movements, delivery status, and warehouse operations. The company needs the dashboard to provide up-to-date information without human intervention. The company's environment cannot access the internet, and the data source is configured to maintain a real-time connection.
How should the company meet this requirement?

• A. Search and download a suitable sandboxed dashboard extension from Tableau Exchange to refresh the dashboard at regular intervals.
• B. Build a web app with dashboard extension API that refreshes the dashboard at regular intervals. Host the web app on the company's web server.
• C. Configure the data source to extract instead of live. Schedule the extract refresh of data at regular intervals.
• D. Schedule a prep flow to refresh the dashboard at regular intervals.

答案：B

解題說明：
Comprehensive and Detailed Explanation From Exact Extract:
Key details from the scenario:
* The dashboard is on a command center screen.
* The environment cannot access the internet.
* The data source is real-time (live).
* The dashboard must keep itself refreshed automatically.
* No human interaction should be required.
From Tableau's documentation:
# Dashboard Extension API
Tableau's Extension API allows developers to create custom extensions that can automate refreshing a live dashboard at intervals.
The extension is hosted on the company's internal web server, which solves the "no internet access" limitation.
This is the only method in the listed options that:
* Works offline
* Can auto-refresh the dashboard
* Supports a "screen-based" live monitoring use case
* Uses the existing live connection (no need for extracts)
Thus, A is correct.
Why the other options are wrong:
# B. Schedule a Prep Flow
Prep flows do not refresh dashboards.
They only refresh prepared data sources.
The dashboard still won't auto-update unless reopened.
# C. Download an extension from Tableau Exchange
Tableau Exchange requires internet access, which the company does not have.
Also, "sandboxed extensions" cannot refresh the dashboard at timed intervals.
# D. Use extracts and scheduled extract refresh
Extracts require:
* A Tableau Server / Cloud schedule
* Internet access for Cloud
* And extracts are not real-timeThis contradicts the requirement for real-time data and an offline environment.

問題 #28
An analyst needs to interactively set a reference date to drive table calculations without leaving a view.
Which action should the analyst use?

• A. Parameter action
• B. Running action
• C. Highlight action
• D. Filter action

答案：A

解題說明：
Comprehensive and Detailed Explanation From Exact Extract:
Tableau's documentation on Parameter Actions states that they allow users to interactively update a parameter directly from the view-without opening the parameter control box.
To "interactively set a reference date" that drives a table calculation:
* A parameter must hold that reference date.
* A parameter action allows clicking or selecting a mark in the view to update the parameter value.
* Table calculations can then reference that parameter to change their computation dynamically.
Filter actions modify which data is shown, not a reference date.
Running actions change sheets, not computation parameters.
Highlight actions visually accent marks but do not set values.
Thus, parameter actions are the only mechanism that meets the requirement.
* Parameter Actions overview describing interactive parameter updates.
* Use cases where parameter actions drive table calculations.
* Action type comparison showing that only parameter actions update a computation value.

問題 #29
......

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