QUIZ 2025 HIGH-QUALITY QSDA2024: QLIK SENSE DATA ARCHITECT CERTIFICATION EXAM - 2024 USEFUL DUMPS

Quiz 2025 High-quality QSDA2024: Qlik Sense Data Architect Certification Exam - 2024 Useful Dumps

Quiz 2025 High-quality QSDA2024: Qlik Sense Data Architect Certification Exam - 2024 Useful Dumps

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Qlik QSDA2024 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 3
  • 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 4
  • 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 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 (Q42-Q47):

NEW QUESTION # 42

Refer to the exhibit.
What does the expression sum< [orderMetAmount ]) return when all values in LineNo are selected?

  • A. 0
  • B. 1
  • C. 2
  • D. 3

Answer: B

Explanation:
The expression sum([OrderNetAmount]) sums the values in the OrderNetAmount field across the dataset.
Given that the dataset includes an inline table that is joined with another, the expression calculates the sum of OrderNetAmount for all selected rows. In this scenario, all values in LineNo are selected, which doesn't affect the summation of OrderNetAmount because LineNo isn't directly used in the sum calculation.
Step-by-step Calculation:
* The Orders table contains the OrderNetAmount for each order. The values provided are 90, 500, 100, and 120.
* Adding these values together:90+500+100+120=81090 + 500 + 100 + 120 = 81090+500+100+120=810
* However, after the Left Join operation with the OrderDetails table, some of these rows might be duplicated if the join results in multiple matches. But since the field being summed, OrderNetAmount, is from the original Orders table and not affected by the details in OrderDetails, the sum still remains consistent with the original values in the Orders table.
Thus, the sum of OrderNetAmount is 149014901490, based on the combined effects of the original data structure and the join operation.


NEW QUESTION # 43
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?

  • A. 1. Load the existing data from the QVD.
    2. Load the new and updated data from the database without the rows that have just been loaded from the QVD and concatenate with data from the QVD.
    3. Load all records from the key field from the database and use an INNER JOIN on the previous table.
  • B. 1. Load the new and updated data from the database.
    2. Load the existing data from the QVD without the updated rows that have just been loaded from the database and concatenate with the new and updated records.
    3. Load all records from the key field from the database and use an INNER JOIN on the previous table.
  • C. 1. Use a partial LOAD to load new and updated data from the database.
    2. Load the existing data from the QVD without the updated rows that have just been loaded from the database and concatenate with the new and updated records.
    3. Use the PEEK function to remove the deleted rows.
  • D. 1. Load the existing data from the QVD.
    2. Load new and updated data from the database. Concatenate with the table loaded from the QVD.
    3. Create a separate table for the deleted rows and use a WHERE NOT EXISTS to remove these records.

Answer: D

Explanation:
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.


NEW QUESTION # 44
Exhibit.

Refer to the exhibit.
A data architect is working on a Qlik Sense app the business has created to analyze the company orders and shipments.
To understand the table structure, the business has given the following summary:
* Every order creates a unique orderlD and an order date in the Orders table
* An order can contain one or more order lines one for each product ID in the order details table
* Products In the order are shipped (shipment date) as soon as they are ready and can be shipped separately
* The dates need to be analyzed separately by Year, Month, and Quarter
The data architect realizes the data model has issues that must be fixed. Which steps should the data architect perform?

  • A. 1. Create a key with OrderlD and ProductID in the OrderDetails table and in the Shipments table
    2. Delete the ShipmentID in the Orders table
    3. Delete the ProductID and OrderlD In the Shipments table
    4. Concatenate Orders and OrderDetails
    5. Create a link table using the MasterCalendar table and create a concatenated field between OrderDate and ShipmentDate
  • B. 1. Create a key with OrderlD and ProductID In the OrderDetails table and in the Orders table
    2. Delete the ShipmentID in the Shipments table
    3. Delete the ProductID and OrderlD in the OrderDetails table
    4. Concatenate Orders and OrderDetails
    5. Create a link table using the MasterCalendar table and create a concatenated field between OrderDate and ShipmentDate
  • C. 1. Create a key with OrderlD and ProductID in the OrderDetails table and in the Orders table
    2. Delete the ShipmentID in the Shipments table
    3. Delete the ProductID and OrderlD in the OrderDetails table
    4. Left join Orders and OrderDetails
    5. Use Derive statement with the MasterCalendar table and apply the derive fields to OrderDate and ShipmentDate
  • D. 1. Create a key with OrderlD and ProductID in the OrderDetails table and in the Shipments table
    2. Delete the ShipmentID in the Orders table
    3. Delete the ProductID and OrderlD in the Shipments table
    4. Left join Orders and OrderDetails
    5. Use Derive statement with the MasterCalendar table and apply the derive fields to OrderDate and ShipmentDate

Answer: A

Explanation:
In the given data model, there are several issues related to table relationships and key fields that need to be addressed to create a functional and optimized data model. Here's how each step in the chosen solution (Option C) resolves these issues:
* Create a key with OrderID and ProductID in the OrderDetails table and in the Shipments table:
* By creating a composite key with OrderID and ProductID, you uniquely identify each line item in both the OrderDetails and Shipments tables. This step is crucial for ensuring that each product within an order is correctly associated with its respective shipment.
* Delete the ShipmentID in the Orders table:
* The ShipmentID in the Orders table is redundant because the Shipments table already captures this information at a more granular level (i.e., at the product level). Removing ShipmentID avoids potential circular references or synthetic keys.
* Delete the ProductID and OrderID in the Shipments table:
* After creating the composite key in step 1, the individual ProductID and OrderID fields in the Shipments table are no longer necessary for joins. Removing them reduces redundancy and simplifies the table structure.
* Concatenate Orders and OrderDetails:
* Concatenating Orders and OrderDetails into a single table creates a unified table that contains all necessary order-related information. This helps in simplifying the model and avoiding issues related to managing separate but related tables.
* Create a link table using the MasterCalendar table and create a concatenated field between OrderDate and ShipmentDate:
* A link table is created to associate the combined table with the MasterCalendar. By creating a concatenated field that combines OrderDate and ShipmentDate, you ensure that both dates are properly linked to the calendar, allowing for accurate time-based analysis.


NEW QUESTION # 45
Exhibit.

Refer to the exhibit.
A data architect wants to transform the input data set to the output data set. Which prefix to the Qlik Sense LOAD command should the data architect use?

  • A. PivotTable
  • B. Peek
  • C. Generic
  • D. Hierarchy Be longsTo

Answer: C

Explanation:
In this scenario, the data architect wants to transform the input dataset, which is in a key-value pair structure, into a table where each attribute becomes a column with its corresponding value under the relevant key.
Understanding the Requirement:
* Theinputdata consists of three fields: Key, Attribute, and Value.
* The desiredoutputstructure has the Key as a primary identifier, and the Attributes (like Color, Diameter, Height, etc.) are spread across the columns, with corresponding values filled in each row.
Best Method to Achieve this Transformation:
* The appropriate method to convert key-value pairs into a structured table where each unique attribute becomes a separate column is theGeneric Loadfunction in Qlik Sense.
Why Generic?
* Generic Loadis specifically designed for situations where data is stored in a key-value format (like the one provided) and needs to be converted into a more traditional tabular format, with attributes as columns.
* It creates a separate table for each combination of Key and Attribute, effectively "pivoting" the attribute values into columns in the output table.
How it Works:
* When applying a GENERIC LOAD to the input dataset, Qlik Sense will generate multiple tables, one for each Attribute. However, in the final data model, Qlik Sense automatically joins these tables by the Key field, effectively producing the desired output structure.
References:
* Qlik Sense Documentation on Generic Load: The documentation outlines how to use the Generic Load to handle key-value pairs and pivot them into a more traditional table format.


NEW QUESTION # 46

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. 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
  • B. 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
  • C. 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
  • 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 # 47
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