Eliminate Risk of Failure with Qlik QSDA2024 Exam Dumps

In this scenario, the data architect needs to replace the SupplierID in the Orders table with the corresponding SupplierName from the Suppliers table, and the desired output should be a single table that includes all the order details along with the SupplierName instead of the SupplierID.

Analyzing the Options:

Option A:

Uses a MAPPING LOAD followed by an APPLYMAP to replace SupplierID with SupplierName in the Orders table. However, the table is dropped afterward, which means it won't produce the required output.

The MAPPING LOAD approach is generally used to map values but is not necessary in this context as we are combining data from two tables directly.

Option B:

This option attempts to LEFT JOIN the Products table with the Suppliers table, but it does not directly address replacing SupplierID with SupplierName in the Orders table.

Additionally, it does not remove the SupplierID after the join, which is essential for the correct output.

Option C:

This option uses a LEFT JOIN with the DISTINCT keyword on the SupplierID field to avoid duplicates. The SupplierName is correctly joined to the Orders table, replacing the SupplierID.

This approach is the most appropriate because it results in a single table containing all order details with the SupplierName instead of the SupplierID.

Option D:

Similar to Option A, but it also introduces an unnecessary renaming step with MAPPING LOAD. It's redundant and does not improve the solution over Option C.

Correct Script Choice:

Option C is the correct script because:

It ensures that SupplierName replaces SupplierID in the Orders table using a LEFT JOIN.

The DISTINCT keyword is applied to the SupplierID field to prevent duplicate rows during the join.

The result is a single table containing the required information with SupplierName in place of SupplierID.

Qlik Sense Join Operations: Using the correct JOIN type and ensuring proper deduplication (with DISTINCT if necessary) is key to merging tables in Qlik Sense.

In this scenario, the data architect needs to load Table_A from an Excel file and ensure that the data is sorted by Field_2. The key here is to correctly load and sort the data in the script.

Understanding the Options:

Option A:

First, it loads the data into a temporary table (Temp) from the Excel file.

Then, it loads the data from the temporary table (Temp) into Table_A, using the ORDER BY Field_2 ASC clause to sort the data by Field_2.

Finally, it drops the temporary table (Temp), leaving the sorted data in Table_A.

Option B:

Directly loads the data from the Excel file into Table_A and applies the ORDER BY Field_2 ASC clause in the same step.

However, the ORDER BY clause in a direct load from an external source like Excel might not work as expected because Qlik Sense does not support ORDER BY when loading directly from a file.

Option C:

Similar to Option A but uses the NoConcatenate keyword to prevent concatenation, which is unnecessary since Temp and Table_A have different names.

While this script works, the NoConcatenate keyword is redundant in this context.

Option D:

The ORDER BY Field_2 ASC is placed before the LOAD statement, which is not a correct usage in Qlik Sense script syntax.

Correct Script Choice:

Option A is the correct script because it correctly sorts the data after loading it into a temporary table and then loads the sorted data into Table_A. This method ensures that the data is sorted by Field_2 and avoids any issues related to sorting during the initial data load.

Qlik Sense Scripting Best Practices: When sorting data in Qlik Sense, the correct approach is to use a RESIDENT LOAD with an ORDER BY clause after loading the data into a temporary table.

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 LineNo is 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.

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.

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:

The input data consists of three fields: Key, Attribute, and Value.

The desired output structure 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 the Generic Load function in Qlik Sense.

Why Generic?

Generic Load is 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.

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.

In this scenario, the Section Access security table controls user access to data within the Qlik Sense app. The user in question, ABC\PPP, has a specific entry in the security table that determines their access rights to the LEVEL field.

Understanding Section Access:

Section Access is used to enforce security by restricting access to certain data based on the user's credentials.

In the security table provided, the USER role for ABC\PPP is set to have access to all data (* in the LINK field), but the OMIT field is set to LEVEL. The OMIT field in Section Access specifies fields that should be omitted from the user's view.

Outcome:

Since the OMIT field for user ABC\PPP is set to LEVEL, this user will not have access to the LEVEL field in the Qlik Sense application.

Option D: The table is displayed without the LEVEL column is the correct outcome.

Qlik Sense Security and Section Access Documentation: The OMIT functionality in Section Access is specifically designed to remove fields from the user's access, ensuring that sensitive or unnecessary data is not exposed.