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Free TCC-C01 Exam Braindumps (page: 3)

Page 2 of 15
PDF with 55 Questions

A consultant wants to improve the performance of reports by moving calculations to the data layer and materializing them in the extract.

Which calculation should the consultant use?

  1. ZN([Sales])*(1 - ZN([Discount]))
  2. CASE [Sector Parameter]
    WHEN 1 THEN "green"
    WHEN 2 THEN "yellow"
  3. SUM([Profit])/SUM([Sales])
  4. POWER(ZN(SUM([Sales]))/
    LOOKUP(ZN(SUM([Sales])), FIRST()),ZN(1/(INDEX()-1)))
    END

Answer(s): C

Explanation:

To improve performance by moving calculations to the data layer and materializing them in the extract, the consultant should choose calculations that benefit from pre-computation and significantly reduce the load during query time:
Aggregation-Level Calculation: The formula SUM([Profit])/SUM([Sales]) calculates a ratio at an aggregate level, which is ideal for pre-computation. Materializing this calculation in the extract means that the complex division operation is done once and stored, rather than being recalculated every time the report is accessed.
Performance Improvement: By pre-computing this aggregate ratio, Tableau can utilize the pre- calculated fields directly in visualizations, which speeds up report loading and interaction times as the heavy lifting of data processing is done during the data preparation stage.


Reference:

Materialization in Extracts: This concept involves pre-calculating and storing complex aggregations or calculations within the Tableau data extract itself, improving performance by reducing the computational load during visualization rendering.



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?

  1. Calc1: DATETRUNC ('month', DATEADD('month', 1, [Order Date])) Calc2: AVG(DATEDIFF ('week', [Order Date], [Calc1]))
  2. Calc1: DATETRUNC ('month', DATEADD ('month', 1, [Order Date])) Calc2: AVG(DATEDIFF ('day', [Order Date], [Calc1]))
  3. Calc1: DATETRUNC ('day', DATEADD('week', 4, [Order Date])) Calc2: AVG([Order Date] - [Calc1])
  4. Calc1: DATETRUNC ('day', DATEADD ('day', 31, [Order Date])) Calc2: AVG ([Order Date] - [Calc1])

Answer(s): B

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.


Reference:

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 documents.

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.


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.



A client notices that while creating calculated fields, occasionally the new fields are created as strings, integers, or Booleans. The client asks a consultant if there is a performance difference among these three data types.

What should the consultant tell the customer?

  1. Strings are fastest, followed by integers, and then Booleans.
  2. Integers are fastest, followed by Booleans, and then strings.
  3. Strings, integers, and Booleans all perform the same.
  4. Booleans are fastest, followed by integers, and then strings.

Answer(s): B

Explanation:

In Tableau, the performance of calculated fields can vary based on the data type used. Calculations involving integers and Booleans are generally faster than those involving strings. This is because numerical operations are typically more efficient for a computer to process than string operations, which can be more complex and time-consuming. Therefore, when performance is a consideration, it is advisable to use integers or Booleans over strings whenever possible.


Reference:

The performance hierarchy of data types in Tableau calculations is documented in resources that discuss best practices for optimizing Tableau performance.



A client has a large data set that contains more than 10 million rows.

A consultant wants to calculate a profitability threshold as efficiently as possible. The calculation must classify the profits by using the following specifications:

. Classify profit margins above 50% as Highly Profitable. . Classify profit margins between 0% and 50% as Profitable.
. Classify profit margins below 0% as Unprofitable.

Which calculation meets these requirements?

  1. IF [ProfitMargin]>0.50 Then 'Highly Profitable'
    ELSEIF [ProfitMargin]>=0 Then 'Profitable'
    ELSE 'Unprofitable'
    END
  2. IF [ProfitMargin]>=0.50 Then 'Highly Profitable'
    ELSEIF [ProfitMargin]>=0 Then 'Profitable'
    ELSE 'Unprofitable'
    END
  3. IF [ProfitMargin]>0.50 Then 'Highly Profitable'
    ELSEIF [ProfitMargin]>=0 Then 'Profitable'
    ELSEIF [ProfitMargin] <0 Then 'Unprofitable'
    END
  4. IF([ProfitMargin]>=0.50,'Highly Profitable', 'Profitable') ELSE 'Unprofitable'
    END

Answer(s): B

Explanation:

The correct calculation for classifying profit margins into categories based on specified thresholds involves the use of conditional statements that check ranges in a logical order:
Highly Profitable Classification: The first condition checks if the profit margin is 50% or more. This must use the ">=" operator to include exactly 50% as "Highly Profitable". Profitable Classification: The next condition checks if the profit margin is between 0% and 50%. Since any value falling at or above 50% is already classified, this condition only needs to check for values greater than or equal to 0%.
Unprofitable Classification: The final condition captures any remaining scenarios, which would only be values less than 0%.


Reference:

Logical Order in Conditional Statements: It is crucial in programming and data calculation to ensure that conditions in IF statements are structured in a logical and non-overlapping manner to accurately categorize all possible values.






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