QUESTION: 5

Refer to the exhibit.

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?

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

Answer(s): D

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

Reference:

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.

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QUESTION: 6

A data architect executes the following script:

Which values does the OrderDate field contain after executing the script?

20210131, 2020/01/31, 31/01/2019
20210131, 2020/01/31, 31/01/2019, 9999
20210131, 2020/01/31, 31/01/2019, 0
20210131, 2020/01/31, 31/01/2019, 31/12/2022

Answer(s): D

Explanation:

In the script provided, the alt() function is used to handle various date formats. The alt() function in Qlik Sense evaluates a list of expressions and returns the first valid expression. If none of the expressions are valid, it returns the last argument provided (in this case, '31/12/2022').

Step-by-step breakdown:

The alt() function checks the Date field for three different formats:

YYYYMMDD

YYYY/MM/DD

DD/MM/YYYY

If none of these formats match the value in the Date field, the default date '31/12/2022' is assigned.

Values in the Date field:

20210131: Matches the first format YYYYMMDD.

2020/01/31: Matches the second format YYYY/MM/DD.

31/01/2019: Matches the third format DD/MM/YYYY.

9999: Does not match any of the formats, so the alt() function returns the default value '31/12/2022'.

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QUESTION: 7

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?

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.
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.
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.
1. Load the existing data from the QV
2. Load new and updated data from the database. Concatenate with the table loaded from the QV
3. Create a separate table for the deleted rows and use a WHERE NOT EXISTS to remove these records.

Answer(s): 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.

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QUESTION: 8

Exhibit.

Refer to the exhibit.

The data architect needs to build a model that contains Sales and Budget data for each customer. Some customers have Sales without a Budget, and other customers have a Budget with no Sales.

During loading, the data architect resolves a synthetic key by creating the composite key.

For validation, the data architect creates a table that contains Customer, Month, Sales, and Budget columns.

What will the data architect see when selecting a month?

Customer Names and Sales records for the selected month, Budgets column can contain null or non-null values
All Customer Names for both Sales and Budget records for the selected month
Customer Names and Sales records for the selected month but with only non-null values in Budget column
Customer Names and Budaets records for the selected month. Sales column can contain null or non-null values

Answer(s): A

Explanation:

In the scenario where the data model is built with a composite key (keyYearMonthCustNo) to resolve synthetic keys, the following outcomes occur:

Sales and Budget Data Integration:

The composite key ensures that each combination of Year, Month, and Customer is uniquely represented in the combined Sales and Budget data.

During data selection (e.g., when a specific month is selected), Qlik Sense will show all the customer names that have either Sales or Budget data associated with that month.

Resulting Data View:

For the selected month, customers with sales records will display their Sales data. However, if the corresponding Budget data is missing, the Budget column will contain null values.

Similarly, if a customer has a Budget but no Sales data for the selected month, the Sales column will show null values.

Validation Outcome:
When the data architect selects a month, they will see the following:

Customer Names and Sales records for the selected month, where the Sales column will have values and the Budget column may contain null or non-null values depending on the data availability.

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