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Qlik QSDA2024 Exam Syllabus Topics:

Topic	Details
Topic 1	Data Model Design: In this section, data analysts and data architects are tested on their ability to determine relevant measures and attributes from each data source.
Topic 2	Data Transformations: This section examines the skills of data analysts and data architects in creating data content based on specific requirements. It also covers handling null and blank data and documenting Data Load scripts.
Topic 3	Data Connectivity: This part evaluates how data analysts identify necessary data sources and connectors. It focuses on selecting the most appropriate methods for establishing connections to various data sources.
Topic 4	Validation: This section tests data analysts and data architects on how to validate and test scripts and data. It focuses on selecting the best methods for ensuring data accuracy and integrity in given scenarios.
Topic 5	Identify Requirements: This section assesses the abilities of data analysts in defining key business requirements. It includes tasks such as identifying stakeholders, selecting relevant metrics, and determining the level of granularity and aggregation needed.

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Qlik Sense Data Architect Certification Exam - 2024 Sample Questions (Q32-Q37):

NEW QUESTION # 32
Exhibit.

Refer to the exhibit.
A data architect is provided with five tables. One table has Sales Information. The other four tables provide attributes that the end user will group and filter by.
There is only one Sales Person in each Region and only one Region per Customer.
Which data model is the most optimal for use in this situation?

Answer: C

Explanation:
In the given scenario, where the data architect is provided with five tables, the goal is to design the most optimal data model for use in Qlik Sense. The key considerations here are to ensure a proper star schema, minimize redundancy, and ensure clear and efficient relationships among the tables.
Option Dis the most optimal model for the following reasons:
* Star Schema Design:
* In Option D, the Fact_Gross_Sales table is clearly defined as the central fact table, while the other tables (Dim_SalesOrg, Dim_Item, Dim_Region, Dim_Customer) serve as dimension tables.
This layout adheres to the star schema model, which is generally recommended in Qlik Sense for performance and simplicity.
* Minimization of Redundancies:
* In this model, each dimension table is only connected directly to the fact table, and there are no unnecessary joins between dimension tables. This minimizes the chances of redundant data and ensures that each dimension is only represented once, linked through a unique key to the fact table.
* Clear and Efficient Relationships:
* Option D ensures that there is no ambiguity in the relationships between tables. Each key field (like Customer ID, SalesID, RegionID, ItemID) is clearly linked between the dimension and fact tables, making it easy for Qlik Sense to optimize queries and for users to perform accurate aggregations and analysis.
* Hierarchical Relationships and Data Integrity:
* This model effectively represents the hierarchical relationships inherent in the data. For example, each customer belongs to a region, each salesperson is associated with a sales organization, and each sales transaction involves an item. By structuring the data in this way, Option D maintains the integrity of these relationships.
* Flexibility for Analysis:
* The model allows users to group and filter data efficiently by different attributes (such as salesperson, region, customer, and item). Because the dimensions are not interlinked directly with each other but only through the fact table, this setup allows for more flexibility in creating visualizations and filtering data in Qlik Sense.
References:
* Qlik Sense Best Practices: Adhering to star schema designs in Qlik Sense helps in simplifying the data model, which is crucial for performance optimization and ease of use.
* Data Modeling Guidelines: The star schema is recommended over snowflake schema for its simplicity and performance benefits in Qlik Sense, particularly in scenarios where clear relationships are essential for the integrity and accuracy of the analysis.

NEW QUESTION # 33

Refer to the exhibits.
On executing a load script of an app, the country field needs to be normalized. The developer uses a mapping table to address the issue. The script runs successfully but the resulting table is not correct.
What should the data architect do?

A. Review the values of the source mapping table
B. Create two different mapping tables
C. Use LOAD DISTINCT on the mapping table
D. Use a LEFT JOIN Instead of the APPLYMAP

Answer: A

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 References:
* 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.

NEW QUESTION # 34
A data architect needs to develop a script to export tables from a model based upon rules from an independent file. The structure of the text file with the export rules is as follows:

These rules govern which table in the model to export, what the target root filename should be, and the number of copies to export.
The TableToExport values are already verified to exist in the model.
In addition, the format will always be QVD, and the copies will be incrementally numbered.
For example, the Customers table would be exported as:

What is the minimum set of scripting strategies the data architect must use?

A. One loop and one SELECT CASE statement
B. Two loops and one IF statement
C. One loop and two IF statements
D. Two loops without any conditional statements

Answer: C

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.

NEW QUESTION # 35
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?

A. Load only 2 years of data and use best practices in scripting and visualization to calculate and display aggregated data
B. Load only aggregated data for 2 years and apply filters on a sheet for transaction data
C. Load only 2 years of data in an aggregated app and create a separate transaction app for occasional use
D. Load only aggregated data for 2 years and use On-Demand App Generation (ODAG) for transaction data

Answer: D

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 Cis 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.
References:
* 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.

NEW QUESTION # 36
Exhibit.

One of the data sources a data architect must add for a newly developed app is an Excel spreadsheet. The Region field only has values for the first record for the region. The data architect must perform a transformation so that each row contains the correct Region.
Which function should the data architect implement to resolve this issue?

A. CrossTable
B. Previous
C. IntervalMatch
D. Above

Answer: B

Explanation:
The given Excel spreadsheet has a Region field where the region value is only specified for the first record within each region. The data architect needs to fill in the missing region values for subsequent rows.
* Previous() Function: The Previous() function in Qlik Sense returns the value of the expression from the previous row. In this case, it can be used to fill down the Region values so that each row contains the correct region information.
* Implementation: The script can be designed to check if the current row's Region value is missing (null). If it is missing, the script can assign the value from the previous row using the Previous() function.
LOAD
If(IsNull(Region), Previous(Region), Region) AS Region,
This logic fills in the missing Region values with the value from the preceding row, which effectively resolves the issue shown in the spreadsheet.

NEW QUESTION # 37
......

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MINIMUM QSDA2024 PASS SCORE, FREE QSDA2024 EXAM QUESTIONS

Minimum QSDA2024 Pass Score, Free QSDA2024 Exam Questions