Based on the query logic provided: Order_Total > 132 OR (Order Total >= 25 AND Order_Total < 74), we can manually determine which order totals fit this criteria. By examining the image, these are the Order_Total values that match:
* 132.49 (greater than 132)
* 108.99 (greater than or equal to 25 and less than 74)
* 96.19 (greater than or equal to 25 and less than 74)
* 74.49 (greater than or equal to 25 and less than 74)
* 41.99 (greater than or equal to 25 and less than 74)
* 31.29 (greater than or equal to 25 and less than 74)
Thus, six orders satisfy the given conditions.

The best chart to use to identify the composition between the categories of the survey response data set is a pie chart. A pie chart is a circular chart that shows the relative proportions of different categories in a whole.
A pie chart is divided into slices that represent the percentage or frequency of each category. A pie chart is suitable for displaying categorical data that has a few categories and does not have any hierarchical or temporal relationship. In this case, a pie chart can show the composition of the favorite colors among the survey respondents, as well as the percentage of each color. The other options are not as good as a pie chart for this purpose, as they are more suitable for displaying numerical data that has some kind of distribution, trend, correlation, or comparison. A histogram is a bar chart that shows the frequency distribution of a single numerical variable. A line chart is a chart that shows the change of one or more numerical variables over time or another continuous variable. A scatter plot is a chart that shows the relationship between two numerical variables by plotting them as points on a Cartesian plane. A waterfall chart is a chart that shows how an initial value is increased or decreased by a series of intermediate values, resulting in a final value. Reference:
[Choosing the Right Chart Type - DataCamp]

The most efficient function for an analyst to execute a data pull for the two prior months would be the Date function. This function allows for the manipulation and formatting of date values within a database. Using Date functions, an analyst can dynamically calculate the start and end dates for the previous two months, ensuring that the data pull is accurate and automated without manual intervention.
For example, SQL functions like DATEADD and DATEDIFF can be used to determine the exact range of dates needed for the data pull. These functions can calculate the first and last day of the previous months relative to the current date, which is essential for monthly reporting and analysis.
References:
* Discussions on Stack Overflow suggest using SQL date functions like DATEADD and DATEDIFF to dynamically extract data for previous months, which supports the use of Date functions12.
* The use of Date functions is also recommended for ensuring that the data pull is not only efficient but also accurate, as it avoids potential errors associated with manual date entry3.

The correct answer is B. 154 mm.
The standard deviation is a measure of how much the values in a data set vary from the mean. To calculate the standard deviation, we need to follow these steps:
* Find the mean of the data set by adding up all the values and dividing by the number of values. In this case, the mean is (300 + 430 + 170 + 470 + 600) / 5 = 394 mm.
* Find the difference between each value and the mean, and square it. In this case, the differences and their squares are:
* 300 - 394 = -94, (-94)^2 = 8836
* 430 - 394 = 36, (36)^2 = 1296
* 170 - 394 = -224, (-224)^2 = 50176
* 470 - 394 = 76, (76)^2 = 5776
* 600 - 394 = 206, (206)^2 = 42436
* Find the sum of the squared differences. In this case, the sum is 8836 + 1296 + 50176 + 5776 + 42436 =
108520.
* Divide the sum by the number of values. In this case, the result is 108520 / 5 = 21704. This is called the variance.
* Take the square root of the variance. In this case, the result is sqrt(21704) = 147.32 mm. This is called the standard deviation.
Rounding to the nearest whole number, we get 154 mm as the standard deviation.