A data architect in the Enterprise Architecture team wants to develop a new application summarizing Qlik Sense usage by all company employees. They also want to gather usage metrics for other systems.
Who should the data architect contact to be granted access to the data?
When developing an application that summarizes Qlik Sense usage by company employees and also gathers usage metrics for other systems, the data architect needs to ensure they have the correct access to sensitive data. The following roles are crucial:
IT Security Director: Responsible for the security of IT systems and data. They would ensure that the data architect has the appropriate permissions to access usage metrics and other system data securely.
Human Resources Director: They manage employee-related data, including employment records that might be necessary for matching employee IDs with usage metrics. This access is crucial for correlating usage data with specific employees.
Qlik Sense Administrator: This individual has administrative rights over the Qlik Sense environment and can grant access to usage data within Qlik Sense, ensuring that the architect has the necessary data to analyze.
Given the need to securely and correctly handle sensitive data, including employee usage metrics across multiple systems, Option A includes all the appropriate contacts for access and permissions.
A data architect needs to load large amounts of data from a database that is continuously updated.
* New records are added, and existing records get updated and deleted.
* Each record has a LastModified field.
* All existing records are exported into a QVD file.
* The data architect wants to load the records into Qlik Sense efficiently.
Which steps should the data architect take to meet these requirements?
When dealing with a database that is continuously updated with new records, updates, and deletions, an efficient data load strategy is necessary to minimize the load time and keep the Qlik Sense data model up-to-date.
Explanation of Steps:
Load the existing data from the QVD:
This step retrieves the already loaded and processed data from a previous session. It acts as a base to which new or updated records will be added.
Load new and updated data from the database. Concatenate with the table loaded from the QVD:
The next step is to load only the new and updated records from the database. This minimizes the amount of data being loaded and focuses on just the changes.
The new and updated records are then concatenated with the existing data from the QVD, creating a combined dataset that includes all relevant information.
Create a separate table for the deleted rows and use a WHERE NOT EXISTS to remove these records:
A separate table is created to handle deletions. The WHERE NOT EXISTS clause is used to identify and remove records from the combined dataset that have been deleted in the source database.
Refer to the exhibit.
A company stores the employee data within a key composed of Country, UserlD, and Department. These fields are separated by a blank space. The UserlD field is composed of two characters that indicate the country followed by a unique code of two or three digits. A data architect wants to retrieve only that unique code.
Which function should the data architect use?
A)
B)
C)
D)
In this scenario, the key is composed of three components: Country, UserID, and Department, separated by spaces. The UserID itself consists of a two-character country code followed by a unique code of two or three digits. The objective is to extract only this unique numeric code from the UserID field.
Explanation of the Correct Function:
Option A: RIGHT(SUBFIELD(Key, ' ', 2), 3)
SUBFIELD(Key, ' ', 2): This function extracts the second part of the key (i.e., the UserID) by splitting the string using spaces as delimiters.
RIGHT(..., 3): After extracting the UserID, the RIGHT() function takes the last three characters of the string. This works because the unique code is either two or three digits, and the RIGHT() function will retrieve these digits from the UserID.
This combination ensures that the data architect extracts the unique code from the UserID field correctly.
A company generates l GB of ticketing data daily. The data is stored in multiple tables. Business users need to see trends of tickets processed for the past 2 years. Users very rarely access the transaction-level data for a specific date. Only the past 2 years of data must be loaded, which is 720 GB of data.
Which method should a data architect use to meet these requirements?
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.
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.
Exhibit.
A large electronics company re-assigns sales people once per year from one Department to another.
SPID is the Salesperson ID; the SPID for each individual sales person Name remains constant. The Department for a SPID may change; each change is stored in the Dynamic Dimension data.
Four tables need to be linked correctly: a transaction table, a dynamic salesperson dimension, a static salesperson dimension, and a department dimension.
Which script prefix should the data architect use?
In the scenario described, the Dynamic Dimension data tracks changes in department assignments for salespeople over time. To correctly link the transaction data with the salesperson data and ensure that sales are associated with the correct department based on the date, an IntervalMatch function should be used.
IntervalMatch is designed to match discrete data (like transaction dates) with a range of dates. In this case, each salesperson's department assignment is valid over a period of time, and the IntervalMatch function can be used to link the transaction data with the correct department for each salesperson based on the transaction date.
Option A (Merge): This option is incorrect as it refers to combining data sets, which doesn't address the need to handle the dynamic, date-based department assignments.
Option B (IntervalMatch): This is the correct choice because it allows you to match each transaction with the correct department assignment based on the ChangeDate in the Dynamic Dimension data.
Option C (Partial Reload): This refers to reloading only part of the data, which is not relevant to linking tables based on date ranges.
Option D (Semantic): This option is not applicable as it refers to a broader approach to data modeling and interpretation rather than specifically linking data based on time intervals.
Thus, IntervalMatch is the correct method for linking the transaction data with the dynamic salesperson dimension, ensuring that each transaction is associated with the correct department based on the historical assignment data.
