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Exam Analytics-Con-301 Questions Fee - Exam Analytics-Con-301 Tutorials

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Salesforce Analytics-Con-301 Exam Syllabus Topics:

Topic
Details

Topic 1

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 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.

Topic 3

Data Management: This part focuses on establishing governance and support for published content. Tableau Consultants are expected to manage data security, publish and maintain data sources and workbooks, and oversee content access. It includes applying governance best practices, using metadata APIs, and supporting administration functions to maintain data integrity and accessibility.

Topic 4

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 (Q18-Q23):

NEW QUESTION # 18
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?

A. Calc1: DATETRUNC ('day', DATEADD('week', 4, [Order Date]))
Calc2: AVG([Order Date] - [Calc1])
B. Calc1: DATETRUNC ('month', DATEADD('month', 1, [Order Date]))
Calc2: AVG(DATEDIFF ('week', [Order Date], [Calc1]))
C. Calc1: DATETRUNC ('day', DATEADD ('day', 31, [Order Date]))
Calc2: AVG ([Order Date] - [Calc1])
D. Calc1: DATETRUNC ('month', DATEADD ('month', 1, [Order Date]))
Calc2: AVG(DATEDIFF ('day', [Order Date], [Calc1]))

Answer: D

Explanation:
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.

NEW QUESTION # 19
A client wants to produce a visualization to show quarterly profit growth and aggregated sales totals across a number of product categories from the data provided below.

Which set of charts should the consultant use to meet the client's requirements?

A. Scatter plot and pie chart
B. Gantt and bar charts
C. Waterfall chart and tree map
D. Line and bubble charts

Answer: C

Explanation:
To effectively display quarterly profit growth and aggregated sales totals across different product categories, a combination of a Waterfall chart and a Tree Map is recommended:
Waterfall Chart: This chart type is excellent for visualizing the sequential growth or decline of profits across different quarters for each sub-category. It clearly shows how profits accumulate over time, highlighting both positive and negative changes, which makes it ideal for tracking profit growth or decline through the quarters.
Tree Map: A Tree Map can efficiently display aggregated sales totals where each block size represents the total sales of a product category, providing a quick, visually impactful comparison across categories. This is especially useful when the client wants to understand which categories contribute most to sales in a glanceable format.
Together, these charts provide a comprehensive overview of both profit trends over time (Waterfall Chart) and a comparative snapshot of sales performance across categories (Tree Map), meeting the client's need to analyze performance dynamics in a detailed yet consolidated manner.
References
These recommendations are based on common best practices for data visualization in Tableau, where specific chart types are chosen for their strengths in communicating certain types of data relationships and dynamics, as detailed in Tableau's official visualization guides.

NEW QUESTION # 20
A company has a data source for sales transactions. The data source has the following characteristics:
. Millions of transactions occur weekly.
. The transactions are added nightly.
. Incorrect transactions are revised every week on Saturday.
* The end users need to see up-to-date data daily.
A consultant needs to publish a data source in Tableau Server to ensure that all the transactions in the data source are available.
What should the consultant do to create and publish the data?

A. Publish an incremental extract refresh every day and perform a full extract refresh every Saturday.
B. Publish an incremental extract refresh every day and publish a secondary data set containing data revisions.
C. Publish an incremental refresh every Saturday.
D. Publish a live connection to Tableau Server.

Answer: A

Explanation:
Given the need for up-to-date data on a daily basis and weekly revisions, the best approach is to use an incremental extract refresh daily to update the data source with new transactions. On Saturdays, when incorrect transactions are revised, a full extract refresh should be performed to incorporate all revisions and ensure the data's accuracy. This strategy allows end users to have access to the most current data throughout the week while also accounting for any necessary corrections12.
References: The solution is based on best practices for managing data sources in Tableau Server, which recommend using incremental refreshes for frequent updates and full refreshes when significant changes or corrections are made to the data12.

NEW QUESTION # 21
A client wants to migrate their Tableau Server to Tableau Cloud. The Tableau Server is configured with three sites: Finance, Strategy, and Marketing. A consultant must provide a solution that minimizes user impact and costs.
Which configuration should the consultant recommend for Tableau Cloud to meet the client's requirements?

A. Three separate Tableau Cloud instances for Finance, Strategy, and Marketing
B. One Tableau Cloud instance with two sites for Strategy and Marketing, and one Tableau Server instance for Finance
C. One Tableau Cloud instance configured with a Finance project folder, Strategy project folder, and Marketing project folder
D. One Tableau Cloud instance configured with all workbooks in a single project

Answer: C

Explanation:
To minimize user impact and costs while migrating from Tableau Server to Tableau Cloud with multiple sites, the best solution is:
Single Tableau Cloud Instance with Multiple Projects: Instead of multiple sites which could imply higher management overhead and possibly higher costs, configuring one Tableau Cloud instance with different project folders for each former site (Finance, Strategy, Marketing) is most efficient.
Benefits: This setup maintains organizational separation of data and access similar to having different sites but leverages the unified management and simplicity of a single cloud instance. It reduces complexity in user access management and integration points.
Implementation: Each project folder acts like a mini-site within the larger instance, where specific permissions and content can be managed independently, akin to the original server setup but within a single cloud-based environment.
References
This recommendation is in line with best practices for cloud migration focusing on consolidation and cost efficiency, as suggested in Tableau's official documentation for cloud migration strategies.

NEW QUESTION # 22
A client wants to see the average number of orders per customer per month, broken down by region. The client has created the following calculated field:
Orders per Customer: {FIXED [Customer ID]: COUNTD([Order ID])}
The client then creates a line chart that plots AVG(Orders per Customer) over MONTH(Order Date) by Region. The numbers shown by this chart are far higher than the customer expects.
The client asks a consultant to rewrite the calculation so the result meets their expectation.
Which calculation should the consultant use?

A. {FIXED [Customer ID], [Region]: COUNTD([Order ID])}
B. {INCLUDE [Customer ID]: COUNTD([Order ID])}
C. {EXCLUDE [Customer ID]: COUNTD([Order ID])}
D. {FIXED [Customer ID], [Region], [Order Date]: COUNTD([Order ID])}

Answer: A

Explanation:
The calculation {FIXED [Customer ID], [Region]: COUNTD([Order ID])} is the correct one to use for this scenario. This Level of Detail (LOD) expression will calculate the distinct count of orders for each customer within each region, which is then averaged per month. This approach ensures that the average number of orders per customer is accurately calculated for each region and then broken down by month, aligning with the client's expectations.
References: The LOD expressions in Tableau allow for precise control over the level of detail at which calculations are performed, which is essential for accurate data analysis. The use of {FIXED} expressions to specify the granularity of the calculation is a common practice and is well-documented in Tableau's official resources12.
The initial calculation provided by the client likely overestimates the average number of orders per customer per month by region due to improper granularity control. The revised calculation must take into account both the customer and the region to correctly aggregate the data:
FIXED Level of Detail Expression: This calculation uses a FIXED expression to count distinct order IDs for each customer within each region. This ensures that the count of orders is correctly grouped by both customer ID and region, addressing potential duplication or misaggregation issues.
Accurate Aggregation: By specifying both [Customer ID] and [Region] in the FIXED expression, the calculation prevents the overcounting of orders that may appear if only customer ID was considered, especially when a customer could be ordering from multiple regions.
References:
Level of Detail Expressions in Tableau: These expressions allow you to specify the level of granularity you need for your calculations, independent of the visualization's level of detail, thus offering precise control over data aggregation.

NEW QUESTION # 23
......

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Noah King Noah King

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