Appian ACD200 - Appian Certified Senior Developer

The question is about referencing and using arrays of a CDT (Custom Data Type). The following statements are true:

There is no difference between using “dot notation” and the index() function. Both methods can be used to access elements of an array by their position. For example, if a is an array of CDTs, then a[1] and index(a, 1) will return the same element.

An array of integers can be passed as the second parameter to the index() function to return the data at those positions in the array. For example, if a is an array of CDTs, then index(a, {1, 3, 5}) will return an array of CDTs containing the elements at positions 1, 3, and 5 in a.

The following statements are false:

Other Appian functions can be used within the square brackets “[]” when using dot notation to return specific data from the array. This is not possible, as dot notation only accepts integers or integer arrays as indices. For example, if a is an array of CDTs, then a[length(a)] will cause an error, as length(a) is not a valid index.

The function wherecontains can act on operands of different types. This is not true, as wherecontains only works on operands of the same type. For example, if a is an array of CDTs and b is an array of integers, then wherecontains(a, b) will cause an error, as a and b are not of the same type.

References:

Dot Notation

index()

wherecontains()

The requirement is to create a service account for a consumer to access the Appian Web API. A basic user is the user type that should be created for this purpose, as it has the minimum permissions needed to invoke web APIs and does not consume a license. A basic user can also be assigned to specific groups or roles to control the access to web APIs. The other options are not suitable for this purpose, as they either have more permissions than needed or consume a license. References:

User Types

Web API Security

 The best way to restrict the uploading of documents without extensions at the environment level is to enable the File Upload > Block files without an extension option under the Administration Console. This option will prevent users from uploading files that do not have a valid file extension, such as .docx, .pdf, or .png. This can help to avoid security risks and compatibility issues with unsupported file types. Therefore, the correct answer is A.

References:

[File Upload Settings]

[File Extensions]

The question is about presenting data through data visualization and matching the chart types to the data they are best suited to represent. The following are the correct matches:

A pie chart is best suited to represent proportional data in one category and can help us understand the contribution of parts to a whole. A pie chart shows the relative size of each part as a slice of a circular pie, and can display percentages or absolute values. For example, a pie chart can show the market share of different smartphone brands in one country.

A line chart is best suited to represent direct comparison of data with multiple categories with a relatively small set of positive and negative values. A line chart shows the change or trend of data over time or another variable, and can display multiple lines for different categories. For example, a line chart can show the temperature change of different cities over a year.

A bar chart is best suited to represent values across a relatively large number of categories that are not sequential or time-based. A bar chart shows the magnitude or frequency of data using horizontal or vertical bars, and can display multiple bars for different categories. For example, a bar chart can show the number of students enrolled in different courses in a university.

References:

Pie Chart

Line Chart

Bar Chart

According to Appian best practices, environment-specific constants should be used to define values that vary from one Appian environment to another. For example, if you want to generate an email body that contains the environment name, you can create a constant named ENVIRONMENT_NAME and assign it different values for DEV and TEST environments. This way, you can avoid hard-coding values in your expressions or rules, and simplify your deployment process by using automatic constant value mapping.

 A sprint retrospective is a meeting that takes place at the end of each sprint, where the team reflects on what went well and what can be improved in their work process. The purpose of the retrospective is to identify and implement actions that can enhance the team’s performance and satisfaction in future sprints. Two basic questions that can help facilitate the retrospective are: What went well? and What didn’t go well and can be improved? These questions allow the team to celebrate their achievements, acknowledge their challenges, and generate ideas for improvement.

The two statements about the a!queryEntity() function that are true are:

The entity value must be given as a constant of type Data Store Entity. This is because the entity value specifies which data store entity (table or view) to query from. The value must be a constant that references an existing data store entity in Appian Designer. You cannot use a variable or an expression for this value.

Setting the fetchTotalCount parameter to False can improve the performance of the rule. This is because setting this parameter to False tells Appian not to calculate the total number of records that match the query criteria, which can be an expensive operation for large data sets. By default, this parameter is set to True, which means Appian will return the total count along with the query results. References: a!queryEntity() Function

 The metric from the data_store_details.csv file that will help you understand the number of operations against data store is Total Count. This metric represents the total number of queries, inserts, updates, deletes, and executes performed against the data store during the specified time period. You can use this metric to identify which data stores are heavily used and may need performance tuning or scaling. References: [Data Store Details Report], [Data Store Metrics]

When building a process model that transforms a large data set, two things that should be ensured to include in the process model are an XOR gateway before the transformation node, to check for empty/null values, and a transformation applied with an expression rule that takes the data set as an input, and loops through the dataset using a looping function, such as foreach. These things can help to improve the performance and reliability of the process model. The XOR gateway can prevent unnecessary processing of empty or null data sets, which can save time and resources. The expression rule can perform the transformation in one transaction, instead of using multiple nodes or subprocesses, which can reduce the memory footprint and the database load of the process model. Therefore, the correct answers are B and D.

References:

[XOR Gateway]

[Expression Rules]

[foreach() Function]