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An analyst creates a column chart showing the count of total orders and the count of returned orders on each date. The number of returned orders is much smaller than the number of total orders, so the columns for the returned orders are short and it is difficult to compare all the columns visually.

How should the analyst ensure that the differences between returned order counts for each day are more visible?

A. Change the returned orders series to a line type.

B. Move the returned orders series to a new right axis.

C. Change the y-axis height to a higher number of pixels.

D. Set the x-axis to use a log scale.

An analyst has an Explore query that shows the sum of spend over time. The Explore query has the Sum of Spend measure pivoted by a Category of Spend dimension. The Category of Spend dimension has a value for Budgeted Spend and then categories for each type of unbudgeted spend.

Which visualization feature should the analyst use to separate budgeted spend visually from each category of unbudgeted spend?

A. Display row totals for each category.

B. Series type customization on budgeted spend.

C. Grouped series positioning on budgeted spend.

D. Transpose table to separate out budgeted spend.

A company has a Customer Lookup dashboard that can be filtered on a unique customer name. Business users sometimes report unexpected data when they use filters with starts with or contains for the customer name.

How should the analyst ensure that the filter is unique to one customer?

A. Change the filter type to string.

B. Enable the setting Limit one filter value.

C. Use the Matches Advanced filtering option.

D. Disable the setting Allow multiple filter values.

A business analyst is using the Totals check box in an Explore to get the sum of a column of values. The number that appears in the Total row is much higher than the analyst expects, and this number is not the sum of the values in the column.

What is a possible cause of this discrepancy?

A. There is a row limit applied.

B. There is a filter on a dimension.

C. There is a problem with the underlying LookML.

D. There is a column limit applied.

An analyst is trying to rank companies in an Explore and visualize the rank as a column chart. The analyst selects the Brand Name dimension and the Rank dimension. They then select the column chart visualization type, but receive the error "Measure, time or numeric field required: Column graphs require at least one dimension or for the first dimension field to be of type: number or type: time".

How can the analyst resolve this error while showing the rank as a column visualization?

A. Pivot the Explore by the Brand Name dimension.

B. Select a measure to include in the Explore, then create a table calculation that multiplies the dimension by 1.0 using the syntax ${dimension}*1.0. Then hide the measure from the Explore.

C. Pivot the Explore by brand Rank.

D. Select a measure to include in the Explore, then create a table calculation that adds the Rank dimension to the measure multiplied by zero using the syntax ${dimension} + (${measure}*0). Then hide the measure from the Explore.