Qlik QSDA2024 Exam With Confidence Using Practice Dumps

When implementing Section Access in Qlik Sense, it is crucial that all accounts that need to access the data—including the service account that performs the scheduled reload—are included in the Section Access table. If the service account is not included, Qlik Sense will not be able to access any data, leading to a failure in the reload process.

Here’s a breakdown of why the other options are less likely:

A. The ACCESS column in the Section Access table has been added in lowercase:This would generally result in a syntax error, but it would not allow the script to execute successfully without causing an immediate failure, unrelated to Section Access.

C. A user name listed in the Section Access table is spelled incorrectly:While this could lead to some users not having the correct access, it would not cause the entire reload to fail. The issue here is broader, affecting the entire application load process.

D. The data architect does not have rights to reload the app:If the architect did not have rights, the script would not run successfully even without Section Access.

The correct issue in this scenario is thatthe service account running the task is not included in the Section Access table. This is a common cause of load failures after adding Section Access. To resolve this, ensure that the service account is added with sufficient privileges in the Section Access table

During the transition from QlikView to Qlik Sense, the analytics team has the opportunity to improve the overall reporting. To ensure a smooth migration while optimizing the new environment, the data architect needs to consider several key factors.

Option Cis the best choice because it encompasses the essential aspects of a migration project:

QlikView Archival:

Archiving QlikView applications is crucial to ensure that historical data and applications are preserved and can be referenced if needed in the future. This step is important to maintain continuity and provide a fallback option if required during the transition.

Source Data Architecture:

Understanding the existing source data architecture is critical to ensure that the new Qlik Sense applications can seamlessly connect to the data sources. This also helps in identifying opportunities to optimize or re-architect the data pipelines for better performance in Qlik Sense.

Load Script:

The load script from QlikView might need to be revised or optimized for Qlik Sense. It's important to ensure that the script is compatible and takes advantage of Qlik Sense's capabilities, such as improved data handling, better inline transformations, and enhanced scripting functions.

Data Model:

Reviewing and possibly redesigning the data model is essential during the migration. Qlik Sense's associative engine allows for more flexibility, and this is an opportunity to improve the data model for better performance, scalability, and user experience.

Business Use Case:

Understanding the business use case is vital to ensure that the new Qlik Sense applications meet the business requirements effectively. This includes making sure that the new reports and dashboards are aligned with the business goals and provide the necessary insights.

References:

Qlik Migration Guide: When migrating from QlikView to Qlik Sense, it's important to consider not just the technical aspects but also the business implications and opportunities for improvement.

Qlik Documentation on Data Modeling and Load Script Optimization: These resources provide best practices on how to optimize load scripts and data models during migration to ensure smooth operation and better performance in Qlik Sense.

The scenario describes an app that is experiencing long load times due to the need to pull all records, both new and historical, from three large databases. This situation puts a strain on both the Qlik environment and the source databases. Given that all data is required for analysis, a full reload each time can be inefficient and resource-intensive.

Implementingincremental loadis a widely recommended approach in such cases. Incremental loading allows you to load only new or changed data since the last reload, rather than reloading all the data every time. This significantly reduces the time and resources required for reloading, as only a subset of the data needs to be processed during each reload. QVD (QlikView Data) files are typically used to store the historical data, while only the new or updated records are fetched from the source databases.

This approach would help:

Reduce the load on the source databases.

Shorten the data reload window.

Maintain historical data efficiently while ensuring that all new data is captured.