Exhibit.
Refer to the exhibit.
A data architect wants to transform the input data set to the output data set. Which prefix to the Qlik Sense LOAD command should the data architect use?
In this scenario, the data architect wants to transform the input dataset, which is in a key-value pair structure, into a table where each attribute becomes a column with its corresponding value under the relevant key.
Understanding the Requirement:
The input data consists of three fields: Key, Attribute, and Value.
The desired output structure has the Key as a primary identifier, and the Attributes (like Color, Diameter, Height, etc.) are spread across the columns, with corresponding values filled in each row.
Best Method to Achieve this Transformation:
The appropriate method to convert key-value pairs into a structured table where each unique attribute becomes a separate column is the Generic Load function in Qlik Sense.
Why Generic?
Generic Load is specifically designed for situations where data is stored in a key-value format (like the one provided) and needs to be converted into a more traditional tabular format, with attributes as columns.
It creates a separate table for each combination of Key and Attribute, effectively 'pivoting' the attribute values into columns in the output table.
How it Works:
When applying a GENERIC LOAD to the input dataset, Qlik Sense will generate multiple tables, one for each Attribute. However, in the final data model, Qlik Sense automatically joins these tables by the Key field, effectively producing the desired output structure.
Qlik Sense Documentation on Generic Load: The documentation outlines how to use the Generic Load to handle key-value pairs and pivot them into a more traditional table format.
Users of a published app report incomplete visualizations. The data architect checks the app multiple times and cannot replicate the error. The error affects only one team.
Which is the most likely cause?
In this scenario, users of a published app report incomplete visualizations, but the data architect is unable to replicate the error. This issue affects only one team, suggesting that the problem is related to how data is being restricted or filtered for that specific team.
Section Access: This is a security feature in Qlik Sense that controls user access to specific data within an app. If Section Access is misconfigured, it can restrict access to more records than intended, leading to incomplete visualizations for certain users or teams.
Restricting Too Many Records: If the Section Access is too restrictive, it might limit the data available to the affected users, causing the visualizations to display incomplete information. This could explain why the data architect, who likely has full access, cannot replicate the issue.
A data architect needs to acquire social media data for the past 10 years. The data architect needs to track all changes made to the source data, include all relevant fields, and reload the application four times a day.
What information does the data architect need?
The scenario describes a need to track social media data over the past 10 years, capturing all changes (inserts, updates, deletes) while reloading the data four times a day.
To manage this:
ModificationTime: This field is essential for tracking changes over time. It indicates when a record was last modified, allowing the script to determine whether it needs to insert, update, or delete records.
Primary Key Field: A primary key is crucial for uniquely identifying records. It enables the script to match records in the source with those already loaded, facilitating updates and deletions.
Insert and Update Records: The script should handle both inserting new records and updating existing ones based on the ModificationTime.
Remove Records: If records are deleted in the source, they should also be removed in the Qlik Sense data model to maintain consistency.
This approach ensures that all changes in the social media data are accurately captured and reflected in the Qlik Sense application.
A data architect needs to write the expression for a measure on a KPI to show the sales person with the highest sales. The sort order of the values of the fields is unknown. When two or more sales people have sold the same amount, the expression should return all of those sales people.
Which expression should the data architect use?
A)
B)
C)
D)
The requirement is to create a measure that identifies the salesperson with the highest sales. If multiple salespeople have the same highest sales amount, the measure should return all of those salespeople.
Explanation of Option A:
Rank(Sum(Sales), 1): The Rank() function is used to rank salespersons based on the sum of their sales. The rank 1 indicates the top position.
Aggr() Function: This function aggregates the data and returns the results grouped by the SalesPerson field.
IF() Condition: The IF condition checks if the salesperson's rank is 1 (highest sales).
Concat(DISTINCT ...): The Concat() function concatenates all the salespersons who have the highest sales, separated by spaces or another delimiter, ensuring that all top performers are returned.
Example:
If three salespersons have the highest sales, this expression will return all three names separated by a space.
Refer to the exhibit.
A data architect needs to build a dashboard that displays the aggregated sates for each sales representative. All aggregations on the data must be performed in the script.
Which script should the data architect use to meet these requirements?
A)
B)
C)
D)
The goal is to display the aggregated sales for each sales representative, with all aggregations being performed in the script. Option C is the correct choice because it performs the aggregation correctly using a Group by clause, ensuring that the sum of sales for each employee is calculated within the script.
Data Load:
The Data table is loaded first from the Sales table. This includes the OrderID, OrderDate, CustomerID, EmployeeID, and Sales.
Next, the Emp table is loaded containing EmployeeID and EmployeeName.
Joining Data:
A Left Join is performed between the Data table and the Emp table on EmployeeID, enriching the data with EmployeeName.
Aggregation:
The Summary table is created by loading the EmployeeName and calculating the total sales using the sum([Sales]) function.
The Resident keyword indicates that the data is pulled from the existing tables in memory, specifically the Data table.
The Group by clause ensures that the aggregation is performed correctly for each EmployeeName, summarizing the total sales for each employee.
Key Qlik Sense Data Architect Reference:
Resident Load: This is a method to reuse data that is already loaded into the app's memory. By using a Resident load, you can create new tables or perform calculations like aggregation on the existing data.
Group by Clause: The Group by clause is essential when performing aggregations in the script. It groups the data by specified fields and performs the desired aggregation function (e.g., sum, count).
Left Join: Used to combine data from two tables. In this case, Left Join is used to enrich the sales data with employee names, ensuring that the sales data is associated correctly with the respective employee.
Conclusion: Option C is the most appropriate script for this task because it correctly performs the necessary joins and aggregations in the script. This ensures that the dashboard will display the correct aggregated sales per employee, meeting the data architect's requirements.
