QSDA2024

Explanation:
In this scenario, the company generates 1 GB of ticketing data daily, accumulating up to 720 GB over two years. Business users mainly require trend analysis for the past two years and rarely need to access the transaction-level data. The objective is to load only the necessary data while ensuring the system remains performant.
Option C is the optimal choice for the following reasons:
Efficiency in Data Handling:
By loading only aggregated data for the two years, the app remains lean, ensuring faster load times and better performance when users interact with the dashboard. Aggregated data is sufficient for analyzing trends, which is the primary use case mentioned.
On-Demand App Generation (ODAG):
ODAG is a feature in Qlik Sense designed for scenarios like this one. It allows users to generate a smaller, transaction-level dataset on demand. Since users rarely need to drill down into transaction-level data, ODAG is a perfect fit. It lets users load detailed data for specific dates only when needed, thus saving resources and keeping the main application lightweight.
Performance Optimization:
Loading only aggregated data ensures that the application is optimized for performance. Users can analyze trends without the overhead of transaction-level details, and when they need more detailed data, ODAG allows for targeted loading of that data.
Reference: Qlik Sense Best Practices: Using ODAG is recommended when dealing with large datasets where full transaction data isn't frequently needed but should still be accessible.
Qlik Documentation on ODAG: ODAG helps in maintaining a balance between performance and data availability by providing a method to load only the necessary details on demand.

Explanation:
In this scenario, the issue arises from using the applymap() function to normalize the country field values, but the result is incorrect. The reason is most likely related to the values in the source mapping table not matching the values in the Fact_Table properly.
The applymap() function in Qlik Sense is designed to map one field to another using a mapping table. If the source values in the mapping table are inconsistent or incorrect, the applymap() will not function as expected, leading to incorrect results.
Steps to resolve:
Review the mapping table (MAP_COUNTRY): The country field in the CountryTable contains values such as "U.S.", "US", and "United States" for the same country. To correctly normalize the country names, you need to ensure that all variations of a country's name are consistently mapped to a single value (e.g., "USA").
Apply Mapping: Review and clean up the mapping table so that all possible variants of a country are correctly mapped to the desired normalized value.
Key
Reference: Mapping Tables in Qlik Sense: Mapping tables allow you to substitute field values with mapped values. Any mismatches or variations in source values should be thoroughly reviewed.
Applymap() Function: This function takes a mapping table and applies it to substitute a field value with its mapped equivalent. If the mapped values are not correct or incomplete, the output will not
be as expected.

Explanation:
In the provided scenario, the goal is to export tables from a Qlik Sense model based on rules specified in an external text file. The structure of the text file indicates which table to export, the filename to use, and how many copies to create.
Given this structure, the data architect needs to:
Loop through each row in the text file to process each table.
Use an IF statement to check whether the specified table exists in the model (though it's mentioned they are verified to exist, this step may involve conditional logic to ensure the rules are correctly followed).
Use another IF statement to handle the creation of multiple copies, ensuring each file is named incrementally (e.g., Clients1.qvd, Clients2.qvd, etc.).
Key Script Strategies:
Loop: A loop is necessary to iterate through each row of the text file to process the tables specified for export.
IF Statements: The first IF statement checks conditions such as whether the table should be exported (based on additional logic if needed). The second IF statement handles the creation of multiple copies by incrementing the filename.
This approach covers all the necessary logic with the minimum set of scripting strategies, ensuring that each table is exported according to the rules defined.

Explanation:
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.

Explanation:
In this scenario, the company needs to analyze daily sales data from different countries and also measure customer satisfaction of products as reported on a social media website. This suggests that the data is likely coming from different sources, including possibly an API or a web service (social media website).
The Qlik REST Connector is the appropriate tool for this job. It allows you to connect to RESTful web services and retrieve data directly into Qlik Sense. This is especially useful for integrating data from various online sources, such as social media platforms, which typically expose data via REST APIs. The REST Connector enables the extraction of large datasets from these sources, which is necessary given the requirement to produce 30 reports with an average of 20,000 rows each.
Microsoft SQL Server is not suitable for fetching data from web services or social media platforms.
Qlik GeoAnalytics is used for mapping and geographical data visualization, not for connecting to RESTful services.
Mailbox IMAP is for connecting to email servers and is not applicable to the data extraction needs described here.
Thus, Qlik REST Connector is the correct answer for this scenario.