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Qlik QSBA2024 Exam - Topic 1 Question 21 Discussion

Actual exam question for Qlik's QSBA2024 exam
Question #: 21
Topic #: 1
[All QSBA2024 Questions]

An app needs to load a few hundred rows of data from a .csv text file. The file is the result of a concatenated data dump by multiple divisions across several countries. These divisions use different internal systems and processes, which causes country names to appear differently. For example, the United States of America appears in several places as 'USA', 'U.S.A.', or 'US'.

For the country dimension to work properly in the app, the naming of countries must be standardized in the data model.

Which action should the business analyst complete to address this issue?

Show Suggested Answer Hide Answer
Suggested Answer: B

In Qlik Sense, when dealing with inconsistent naming conventions across different systems or divisions (like the variation in country names), the best practice is to standardize the data during the loading process. Using a lookup table is the most efficient approach to achieve this. This involves loading a separate table that contains all variations of a country name along with the standardized version. During the load process, Qlik Sense can then map the varying names to a common value.

Key Concepts:

Lookup Table: A lookup table contains key-value pairs where different versions of a data element (like country names) are mapped to a single standard value. In this case, the lookup table could have entries like USA, U.S.A., US all mapped to United States of America.

Data Standardization: This is crucial in ensuring consistent analysis across datasets. By converting variations of country names into a single consistent value, the business analyst ensures that all data visualizations and analysis will treat 'USA', 'US', etc., as the same entity.

Why the Other Options Are Less Suitable:

A . Create a calculated master dimension expression: While this could theoretically work by creating a calculated expression to handle variations, it's not scalable or maintainable, especially as new variations in country names could appear in future data loads.

C . Cleanse the source text file prior to loading: This option would require modifying the raw data files manually, which is time-consuming and not sustainable if data is frequently updated or if the number of variations is extensive.

D . Use the Replace option in Data manager: The Replace option in the Data Manager could work on a small scale, but it requires manual intervention each time, which is not efficient or sustainable when new data is loaded. Also, it's more useful for one-off corrections than for handling systemic issues across multiple data loads.

References for Qlik Sense Business Analyst:

Data Modeling Best Practices: Lookup tables are a common approach to resolve issues of inconsistent data across multiple sources. They ensure that data is consistently represented in visualizations and reduce the need for manual intervention.

Data Cleansing During Loading: Qlik Sense allows for transformation and data cleansing during the data load process. A lookup table is part of this capability and ensures that the data loaded into the app is clean and consistent.

Using a lookup table is the most scalable and maintainable approach to standardizing country names in this scenario, which is why option B is the verified solution.


Contribute your Thoughts:

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Alpha
1 day ago
Definitely B, a lookup table is the way to go!
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Paulina
6 days ago
Definitely, this is a common data cleansing problem. A lookup table is the way to go.
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Rosendo
11 days ago
Haha, I bet the person who wrote this question has dealt with this country name issue before!
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Precious
17 days ago
Using the Replace option in Data Manager (D) could work, but a lookup table seems more robust.
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Antonio
22 days ago
Option C, cleansing the source file, could work but it might be more time-consuming.
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Reta
27 days ago
I agree, a lookup table would be the most efficient way to handle the inconsistent country names.
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Verda
1 month ago
Option B looks like the best solution to standardize the country names.
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Garry
1 month ago
Creating a calculated master dimension expression sounds complicated, but I guess it could help if we need to handle multiple formats.
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Erick
1 month ago
I feel like using the Replace option in Data manager could work, but it seems a bit limited for all the variations we have.
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Lou
2 months ago
I think I'm going to go with option B. Creating a lookup table seems like the best way to handle the inconsistent country naming without having to mess with the source file directly. It's a more scalable solution in my opinion.
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Sage
2 months ago
Option D, the Replace option in Data Manager, could also work here. That might be a quicker fix than building a lookup table, but I'm not sure if it would be as robust of a solution.
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Chana
2 months ago
Hmm, I'm leaning towards option C. Cleansing the source file before loading the data seems like it would be the most comprehensive solution. That way, we can ensure the data is standardized from the start.
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Deonna
2 months ago
I remember a practice question where we used a lookup table to handle similar issues. That might be the right approach here too.
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Cory
2 months ago
I think we might need to standardize the country names somehow, but I'm not sure if cleansing the source file is the best option.
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Moon
3 months ago
I'm a bit confused on this one. Should we be looking at the source file and trying to clean it up first, or is a lookup table the better solution? I'm not sure which option would be the most efficient.
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Edna
3 months ago
I think option B is the way to go here. Creating a lookup table to convert the country names seems like the most straightforward approach.
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Ligia
2 months ago
I agree, option B makes the most sense. A lookup table would streamline the process.
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Kristal
3 months ago
Plus, it’s easier to maintain in the long run.
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Josephine
3 months ago
I wonder if cleansing the source file could work too?
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