Tibco TCP-SP - TIBCO Spotfire Certified Professional Exam

According to the documentation, you can define script parameters for IronPython scripts in action controls and JavaScript parameters when you edit HTML of a text area. The type of parameter can be a simple data type such as a text string, an integer, etc., or it can be a visualization, page or data table in the current analysis. Therefore, visualizations and pages are two valid types of parameters that can be passed to a script. Markings and filtering schemes are not listed as possible parameter types. References: Details on Add/Edit Script Parameter, Using Scripts in the Text Area

To create a new filtering scheme, you need to access the File > Document Properties dialog box. From there, you can go to the Filtering Schemes tab and click New… to name and create a new filtering scheme. You can also change the filtering scheme for a certain page from the menu at the top of the Filters panel12. References:

Filtering Schemes

Details on Filtering Scheme Properties

Spotfire Filtering issues

Changing the filtering scheme to use on a page

Comparison circles are a way to display whether or not the mean values for various categories (boxes in the box plot) are significantly different from each other. The circles are drawn with their centers at the mean value for the box to which they correspond. If the circles for different groups do not overlap, the means of the two groups are generally significantly different. If the circles have a large overlap, the means are not significantly different. References: What are Comparison Circles? - TIBCO Software

 The R language is a dialect of S, which was designed in the 1980s and has been in widespread use in the statistical community since. The R language provides, among other things, a programming language, high level graphics, interfaces to other languages and debugging facilities. The R language is an open-source software, which means it is freely available to anyone. It can be accessed by a vibrant community of users and developers. The R language is an implementation of the S programming language. It also combines with lexical scoping semantics inspired by Scheme. The project was conceived in 1992, with an initial version released in 1995 and a stable beta version in 2000123. References: R Programming Language - Introduction, R Language Definition, R (programming language) - Wikipedia

Node navigation functions are used to reference specific records or groups of records in a hierarchy or an axis. They are part of the OVER category of functions, which are used to group data in calculations. Some examples of node navigation functions are All(), Previous(), Next(), Parent(), and Intersect()12. The other function categories do not contain node navigation functions. Statistical functions are used to perform statistical tests and calculations, such as Corr(), TTest(), and Anova()3. Binning functions are used to create bins or groups of values, such as BinByCount(), BinByDateTime(), and BinBySize()4. Spatial functions are used to perform spatial calculations and operations, such as ST_Area(), ST_Contains(), and ST_Distance()5. References: 1: Node navigation - TIBCO Software1, 2: Using Node Navigation in Spotfire » The Analytics Corner2, 3: Statistical Functions - TIBCO Software, 4: Binning Functions - TIBCO Software, 5: Spatial Functions - TIBCO Software

Data marking is the process of selecting items or rows in a visualization to view details or manipulate them. In a map chart visualization, data marking can be applied to two layers: the marker layer and the feature layer. The marker layer displays data points as markers or pies on the map, and the feature layer displays geographic features such as countries, regions, or states. Both layers allow data marking by clicking or dragging on the map, or by using axis marking on the scale labels. The map layer, the theme layer, and the image layer do not support data marking, as they are used to display the background map, the color scheme, and the custom images respectively. References:

Marking in Visualizations1

Explanation of marking and how it is displayed in visualizations in TIBCO Spotfire2

Map Chart Properties – Data – Map with Markers or Pies3

TIBCO Enterprise Runtime for R (TERR) is an enterprise-grade analytic engine that is fully compatible with the R language and embedded in Spotfire1. It extends the reach of R by allowing users to deploy their R code on a commercially supported and robust platform withoutthe need to rewrite their code2. It also resolves many limitations in R, such as memory management, performance, and scalability3. It is not a proprietary multi-paradigm programming language and numeric computing environment, but rather a reimplementation of the R language using C++4. The statement that describes TERR as such is incorrect and does not refer to TERR. References: Spotfire® Enterprise Runtime for R - TIBCO Software, What is TERR? How to get started with TERR in Spotfire. - TIBCO Software, TIBCO® Enterprise Runtime for R FAQ - TIBCO Software, TIBCO® Enterprise Runtime for R (TERR) - TIBCO Software

 A text area is a component that can be added to a page in Spotfire to provide text, images, links, buttons, property controls, filters, and dynamic items1. A dynamic item is a small visualization that can display an aggregated view of some data, such as a calculated value, asparkline, an icon, or a bullet graph2. A dynamic item can be inserted into a text area by using the Insert Dynamic Item option in the text area toolbar3. The other options are not valid items that can be inserted into a text area. A calculated column, a data table, and a custom expression are data-related concepts that can be used in Spotfire to create or manipulate data tables and columns4. A visualization is a graphical representation of data that can be added to a page as a separate component5. References: How to Use the Text Area, Dynamic Items - TIBCO Software, How to Insert a Dynamic Item, Data in Analysis - TIBCO Software, Visualizations - TIBCO Software

Unpivoting is a transformation method that changes the data table from a short/wide format to a tall/skinny format. This means that multiple columns are combined into one or a few columns, and the number of rows increases accordingly. Unpivoting can be useful when you want to analyze the distribution of data across different categories or values, or when you want to apply other transformations or calculations on the data. Unpivoting can be done either when loading data or after the data has been loaded into Spotfire. To unpivot data, you need to select which columns to keep as they are (category columns) and which columns to merge into one or more value columns (value columns). You can also specify the names of the new columns and the data types of the value columns. References: Unpivoting Data, Transforming Data, Spotfire Tips & Tricks: Normalize/Standardize your data with Spotfire