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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

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.

Topic 4

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 5

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.

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Qlik Sense Data Architect Certification Exam - 2024 Sample Questions (Q50-Q55):

NEW QUESTION # 50
Exhibit.

Refer to the exhibit.
A business analyst informs the data architect that not all analysis types over time show the expected data.
Instead they show very little data, if any.
Which Qlik script function should be used to resolve the issue in the data model?

A. DatefFloor(OrderDate)) AS OrderDate in both the table "Orders" and "Master Calendar"
B. TimeStamp(OrderDate) AS OrderDate in both the table "Orders" and "Master Calendar"
C. Date(OrderDate) AS OrderDate in both the table "Orders" and "Master Calendar"
D. TimeStamp#(OrderDate, 'M/D/YYYY hh.mm.ff') AS OrderDate in both the table "Orders" and "Master Calendar"

Answer: C

Explanation:
In the provided data model, there is an issue where certain types of analysis over time are not showing the expected data. This problem is often caused by a mismatch in the data formats of the OrderDate field between the Orders and MasterCalendar tables.
* Option A:DatefFloor(OrderDate)) would round down to the nearest date boundary, which might not address the root cause if the issue is related to different date and time formats.
* Option B:TimeStamp#(OrderDate, 'M/D/YYYY hh.mm.ff') ensures that the date is interpreted correctly as a timestamp, but this does not resolve potential mismatches in date format directly.
* Option C:TimeStamp(OrderDate) will keep both date and time, which may still cause mismatches if the MasterCalendar is dealing purely with dates.
* Option D:Date(OrderDate) formats the OrderDate to show only the date portion (removing the time part). This function will ensure that the date values are consistent across the Orders and MasterCalendar tables by converting the timestamps to just dates. This is the most straightforward and effective way to ensure consistency in date-based analysis.
In Qlik Sense, dates and timestamps are stored as dual values (both text and numeric), and mismatches can lead to incomplete or incorrect analyses. By using Date(OrderDate) in both the Orders and MasterCalendar tables, you ensure that the analysis will have consistent date values, resolving the issue described.

NEW QUESTION # 51
A data architect receives an error while running script.
What will happen to the existing data model?

A. The data model will be replaced with the tables that were successfully loaded before the error.
B. The latest error-free data model will be maintained.
C. The data model will be removed from the application.
D. Newly loaded tables will be merged with the existing data model until the error is resolved.

Answer: B

Explanation:
In Qlik Sense, when a data load script is executed and an error occurs, the script execution is halted immediately, and any tables that were being loaded at the time of the error are discarded. However, the existing data model-i.e., the last successfully loaded data model-remains intact and is not affected by the failed script. This ensures that the application retains the last known good state of the data, avoiding any partial or inconsistent data loads that could occur due to an error.
When the script encounters an error:
* The tables that were successfully loaded prior to the error are retained in the session, but these tables are not merged with the existing data model.
* The existing data model before the script was executed remains unchanged and is maintained.
* No partial or incomplete data is loaded into the application; hence, the data model remains consistent and reliable.
Qlik Sense Data Architect ReferencesThis behavior is designed to protect the integrity of the data model. In scenarios where script execution fails, the user can debug and fix the script without risking the data integrity of the existing application. The key references include:
* Qlik Help Documentation: Provides detailed information on how Qlik Sense handles script errors, highlighting that the existing data model remains unchanged after an error.
* Data Load Editor Practices: Best practices dictate ensuring that the script is fully functional before executing it to avoid data inconsistency. In cases where an error occurs, understanding that the current data model is maintained helps in strategic debugging and script correction.

NEW QUESTION # 52
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 aggregated data for 2 years and apply filters on a sheet for transaction data
B. Load only 2 years of data and use best practices in scripting and visualization to calculate and display aggregated 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 # 53
Exhibit.

Refer to the exhibit.
A data architect is loading the tables and a synthetic key is generated.
How should the data architect resolve the synthetic key?

A. Remove the LineNo field from both tables and use the AutoNumber function on the OrderlD field
B. Create a composite key using OrderlD and LineNo, and remove OrderlD and LineNo from Shipments
C. Create a composite key using OrderlD and UneNo
D. Remove the LineNo field from Shipments and use the AutoNumber function on the OrderlD field

Answer: C

Explanation:
In this scenario, the data architect is loading two tables, Orders and Shipments, into Qlik Sense, and a synthetic key is being generated due to the presence of shared fields (OrderID and LineNo) between these tables.
Understanding the Issue:
* Synthetic Keys: Qlik Sense automatically creates synthetic keys when two or more tables share multiple fields with the same names. While synthetic keys aren't necessarily problematic, they can sometimes lead to incorrect or unexpected data associations and should be resolved when possible to maintain clarity and control over the data model.
* The tables Orders and Shipments share the fields OrderID and LineNo. In this context, these fields together uniquely identify each record, so they are both necessary for accurate data linkage.
Correct Resolution Approach:
Option C: Create a composite key using OrderID and LineNois the best approach.
Here's why:
* Composite Key Creation:
* By creating a composite key that combines OrderID and LineNo (e.g., OrderID & '-' & LineNo), you ensure that each line in the orders and shipments tables is uniquely identified. This composite key will accurately link the related records from the Orders and Shipments tables.
* Avoiding Synthetic Keys:
* By manually creating this composite key, you eliminate the need for Qlik Sense to generate a synthetic key, thereby simplifying the data model and ensuring that data associations are clear and controlled.
* Retaining Both Fields:
* This approach allows you to keep both OrderID and LineNo as separate fields in your tables if needed for other analyses or reporting purposes, while using the composite key for linking the tables.
References:
* Qlik Sense Data Modeling Best Practices: When dealing with multiple fields that are used together to uniquely identify records, it is recommended to create composite keys rather than relying on Qlik Sense's synthetic keys for clarity and better control.

NEW QUESTION # 54

Refer to the exhibit.
A data architect needs to create a data model for a new app. Users must be able to see:
* Total sales for each customer
* Total sales for a given state
* Customers that have not had any sales
* Names of salesperson and regional account managers
* Total number of sales by date
Which steps should the data architect perform to meet these requirements?
Which steps should the data architect perform to meet these requirements?

A. 1. Use a Mapping Load for the Employees table
2. Load the Sales table and use ApplyMap to get the names for SalesPersonID and RegionalAcctMgrlD
3. Use a Left Join Load to add the customer details for the Sales table
B. 1. Load the Customers table and alias the CustID field as CustomerlD
2. Use a Mapping Load for the Employees table
3. Load the Sales table and use ApplyMap to get the names for SalesPersonID and RegionalAcctMgrlD
C. 1. Load the Customers table and alias the CustID field as CustomerlD
2. Load the Employees table
3. Load the Sales table and alias the SalesPersonID and RegionalAcctMgrlD fields as EmployeelD
D. 1. Load the Sales table
2. Load the Customers table
3. Load the Employees table twice; name it and alias the EmployeelD field appropriately each time

Answer: D

Explanation:
In the provided scenario, the data architect needs to create a data model that supports various analyses, including total sales for each customer, total sales by state, identifying customers with no sales, and displaying the names of salespersons and regional account managers.
Here's whyOption Cis the correct choice:
* Loading the Sales Table:The Sales table contains key information related to sales transactions, including SaleID, CustomerID, Amount, SaleDate, SalesPersonID, and RegionalAcctMgrID. This table must be loaded first as it will be central to the analysis.
* Loading the Customers Table:The Customers table includes customer details such as CustID, CustName, Address, City, State, and Zip. Loading this table and linking it to the Sales table via the CustomerID field allows you to perform analyses such as total sales per customer and total sales by state. Importantly, loading the customers separately will also allow the identification of customers without any sales.
* Loading the Employees Table Twice:The Employees table must be loaded twice because it is used to look up two different roles in the sales process: the SalesPersonID and the RegionalAcctMgrID. When loading the table twice:
* The first instance of the Employees table will be used to map the SalesPersonID to EmployeeName.
* The second instance will be used to map the RegionalAcctMgrID to EmployeeName.
* Aliasing the EmployeeID field appropriately in each instance is crucial to prevent creating synthetic keys and to ensure the correct association with the roles in the sales process.
This approach ensures that the data model will correctly support all the required analyses, including identifying customers without sales, which is crucial for meeting the business requirements.
* Option AandOption Bpropose using a mapping load and ApplyMap, which can complicate the model and does not directly address all the business requirements.
* Option Dinvolves aliasing fields in a way that could create unnecessary complexity and might not accurately reflect the relationships in the data.
Thus,Option Cis the correct answer as it best meets the requirements while maintaining a clear and functional data model.

NEW QUESTION # 55
......

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