QSDA2024 Qlik Sense Data Architect Certification Exam - Practice Questions

Creating a composite key is a standard best practice for resolving synthetic keys caused by multiple common fields between tables. By concatenating the common fields into a single, new key field in each table, you create an explicit, single-field link, which resolves the synthetic key and clarifies the data model.

This is a robust and common pattern. The file is first loaded into a temporary two-column table (Key, Value) using functions like Subfield(). Then, a FOR...NEXT loop iterates from 0 to NoOfRows()-1, using Peek() to read the key and value for each row and a LET statement to assign them as script variables.

A canonical date model, built using a Master Calendar, is the standard best practice. It centralizes all date fields into a single calendar, allowing Set Analysis expressions to easily perform time-based comparisons (like Year-over-Year) by modifying the selection on the calendar's Year field while respecting the user's selections on other date components like Month or Quarter.

This statement is false. While Binary load is extremely fast for loading an existing data model, you can still load additional data from other sources after the binary load statement. The binary load must be the very first statement in the script, but subsequent LOAD or SELECT statements can be used to add new tables or augment the existing ones.

The Exists(FieldName, Expression) function checks if the value of the expression (in this case, the current row's EmployeeID) exists in the symbol table for the specified field (ManagerID). This is the correct and most efficient way to create a flag indicating whether an employee's ID also appears in the list of managers.

IntervalMatch is specifically designed to link a discrete point in time (like EventTimestamp) to a time interval (between AdmissionDate and DischargeDate). It is the most efficient and direct way to solve this common data modeling problem, often seen in manufacturing, logistics, and healthcare.

The OMIT system field in the Section Access table allows for dynamic hiding of fields based on user access rights. By listing the field names to be omitted for specific users or groups, you can implement column-level security directly within the data reduction framework.

A metric is a quantifiable measure used to track and assess a business process. 'Average deal size' is a calculated value (e.g., Sum(Sales) / Count(Deals)) representing a performance indicator. 'Shipping address' and 'Product SKU' are descriptive attributes (dimensions), and 'Total revenue by product category' describes a metric sliced by a dimension.

A high subset ratio (close to 100%) on a key indicates that many values in the dimension table do not have corresponding entries in the fact table. This bloats the app's memory with unused dimensional data. The most effective solution is to load only the dimension records that have associated facts, which can be achieved using a Right Keep or an Exists() check.

This statement is true. By default, Qlik treats NULL values from different source fields as distinct, non-associating values. Setting NullInterpret to an empty string tells the script engine to convert any subsequent NULL values it encounters into empty strings. Since all empty strings are identical, this prevents Qlik from creating associations between records based on their NULL keys from different tables.