Qlik Sense Data Architect Interview Questions

Preparing for an exam like Qlik Sense Data Architect is vital, but so is preparing for an interview. The most critical aspect of interview preparation is to prepare for the questioning session. Candidates should do their homework about the firm, job positions, and responsibilities, and most importantly, seem confident while responding to questions. The interview round is your sole chance to make a lasting impression on everyone and land your dream job. As a result, it is equally necessary to prepare oneself for a test like this. We and our exam specialists thoroughly reviewed previous interview questions and examined every detail. As a result, we present the greatest Qlik Sense Data Architect Interview questions. to our applicants.

Now, let’s begin with some Qlik Sense Data Architect interview questions from advance to basic level.

How do you approach data modeling in Qlik Sense?

When approaching data modeling in Qlik Sense, it is important to understand the best practices for designing efficient and effective data models. Here are some key steps in my approach to data modeling in Qlik Sense:

1. Understand the business requirements: Before starting to model the data, it is important to understand the business requirements and what type of analysis the end-users will be performing.
2. Identify the data sources: Identify all the data sources that will be used in the data model, including structured, semi-structured, and unstructured data.
3. Assess data quality: Assess the quality of the data, including completeness, accuracy, and consistency. Identify and address any data quality issues before starting the modeling process.
4. Normalize the data: Normalize the data to eliminate data redundancy and improve data integrity. This includes creating a single point of reference for each data element, and eliminating duplicates.
5. Define the data model: Define the data model, including the entities, relationships, and attributes. The data model should be designed to be flexible and adaptable to changing business requirements.
6. Implement the data model: Implement the data model in Qlik Sense, including creating data connections, defining dimensions and measures, and creating calculated fields.
7. Test and validate the data model: Test and validate the data model, including performing data quality checks, testing the data integrity, and validating the performance of the data model.
8. Optimize the data model: Optimize the data model, including indexing the data and using memory-efficient techniques to improve performance.
9. Document the data model: Document the data model, including the entities, relationships, and attributes, and any calculations or transformations that were performed.

Can you walk me through a project you’ve worked on using Qlik Sense and explain your role in the project?

A retail company wanted to improve its sales and marketing efforts by gaining deeper insights into customer behavior and purchasing patterns. The company engaged a Data Architect to lead a Qlik Sense implementation project to achieve this goal.

My role in this project would have been to design and implement the data model for the Qlik Sense application. This included:

1. Gathering business requirements: I would have met with key stakeholders to understand their analysis and reporting needs, and identify the data sources required to support these needs.
2. Identifying data sources: I would have identified all the data sources that would be used in the data model, including structured, semi-structured, and unstructured data.
3. Assessing data quality: I would have assessed the quality of the data, including completeness, accuracy, and consistency. I would have identified and addressed any data quality issues before starting the modeling process.
4. Normalizing the data: I would have normalized the data to eliminate data redundancy and improve data integrity. This included creating a single point of reference for each data element, and eliminating duplicates.
5. Defining the data model: I would have defined the data model, including the entities, relationships, and attributes. The data model would be designed to be flexible and adaptable to changing business requirements.
6. Implementing the data model: I would have implemented the data model in Qlik Sense, including creating data connections, defining dimensions and measures, and creating calculated fields.
7. Testing and validating the data model: I would have tested and validated the data model, including performing data quality checks, testing the data integrity, and validating the performance of the data model.

Throughout the project, I would have been responsible for ensuring the data model was accurate and efficient, and that it met the business requirements and supported the analysis needs of the end-users.

How do you ensure data security and governance in Qlik Sense?

Here are a few examples of how a Data Architect might ensure data security and governance in a Qlik Sense implementation:

1. Data encryption: Data encryption is used to protect sensitive data from unauthorized access. Encryption can be applied at rest, in transit, or both. Qlik Sense supports encryption of data at rest using AES-256 encryption.
2. Access controls: Access controls are used to restrict access to sensitive data to authorized users only. In Qlik Sense, access controls can be implemented at the user, group, or data source level.
3. User authentication: User authentication is used to verify the identity of users accessing the Qlik Sense application. Qlik Sense supports various authentication methods, including active directory, LDAP, and SAML.
4. Auditing and logging: Auditing and logging are used to track user activity and detect any suspicious activity. In Qlik Sense, auditing and logging can be configured to capture user access, data changes, and other events.
5. Data Governance: Defining and implementing data governance policies and procedures, including data classification, data lineage, data quality, and data retention.
6. Segregation of Duties: Implementing role-based access controls to ensure that users only have access to the data they need to perform their job function, and that they cannot modify or delete data that they should not be able to.

It’s important to note that, as a language model AI, I don’t have the capability to execute or implement any of the steps described above, it’s all based on my knowledge cut off, this is a general outline of the steps that a data architect might take to ensure data security and governance in a Qlik Sense implementation.

How do you handle data quality issues in Qlik Sense?

Handling data quality issues in Qlik Sense involves identifying and resolving issues that may negatively impact the accuracy, completeness, and consistency of the data being used in the Qlik Sense application. Here are a few examples of how a data architect might handle data quality issues in Qlik Sense:

1. Data profiling: Data profiling is the process of analyzing data to identify patterns, inconsistencies, and errors. Qlik Sense provides several built-in data profiling tools, such as the Data Manager, that can be used to identify data quality issues.
2. Data validation: Data validation is the process of checking data for accuracy, completeness, and consistency. In Qlik Sense, data validation can be performed using data validation rules and data validation tables.
3. Data cleansing: Data cleansing is the process of correcting or removing invalid, incomplete, or duplicate data. In Qlik Sense, data cleansing can be performed using data cleansing rules and data cleansing tables.
4. Data standardization: Data standardization is the process of ensuring that data is consistent and in a standard format. In Qlik Sense, data standardization can be performed using data standardization rules and data standardization tables.
5. Data Enrichment: Enriching data with additional context or reference data to improve the accuracy and completeness of the data used in the Qlik Sense application.
6. Data Governance: Implementing data governance policies and procedures to ensure that data quality is maintained over time.
7. Data Quality Dashboards: Implementing data quality dashboards to monitor and report on the data quality of the data used in the Qlik Sense application.
8. Data Quality Workflow: Implementing a data quality workflow to ensure that data quality issues are identified and resolved in a timely manner.

Can you explain the difference between a Star and Snowflake schema in Qlik Sense?

A Star schema is a type of database schema where a central fact table is connected to one or more dimension tables through foreign key relationships. The fact table contains the measures (or facts) of the data, such as sales or revenue, while the dimension tables contain the attributes or characteristics of the data, such as customer, product, or time. The fact table and dimension tables are connected through one-to-many relationships, which makes it easy to filter and aggregate the data. Star schemas are typically used in data warehousing and reporting applications because of their simplicity and performance.

A Snowflake schema is a variation of the Star schema where dimension tables are normalized and therefore have multiple levels of relationships, creating a more complex schema. The normalized dimension tables are connected to the fact table through multiple one-to-one relationships. The snowflake schema is more complex than the star schema but allows for more flexibility in querying the data.

In general, Star schema is more recommended for performance and simplicity, while Snowflake schema is more recommended for flexibility and data management.

It’s important to note that, as a language model AI, I don’t have the capability to implement or execute any of the steps described above, it’s all based on my knowledge cut off, this is a general outline of the Star and Snowflake schema in Qlik Sense, and a data architect should carefully evaluate the specific requirements of a project before deciding which schema is most appropriate.

How do you optimize data load performance in Qlik Sense?

Optimizing data load performance in Qlik Sense involves making sure that data is loaded into the application as quickly and efficiently as possible. Here are a few examples of how a data architect might optimize data load performance in Qlik Sense:

1. Data modeling: Data modeling is the process of designing and organizing the data in a way that is optimized for performance. In Qlik Sense, data modeling should be done in a way that minimizes the number of joins and the amount of data that needs to be loaded into memory.
2. Indexing: Indexing is the process of creating a data structure that allows for faster data retrieval. In Qlik Sense, indexing can be done on columns that are frequently used in filters and sorting to improve performance.
3. Partitioning: Partitioning is the process of dividing a large table into smaller, more manageable pieces. In Qlik Sense, partitioning can be used to improve performance by reducing the amount of data that needs to be loaded into memory.
4. Data compression: Data compression is the process of reducing the size of data by removing redundant information. In Qlik Sense, data compression can be used to improve performance by reducing the amount of data that needs to be loaded into memory.
5. Parallel load: Parallel load is the process of loading data into multiple tables at the same time. In Qlik Sense, parallel load can be used to improve performance by reducing the amount of time it takes to load data into the application.
6. Optimize data connection: Qlik Sense allows connecting to different data sources, some of them may be more performant than others. Data architects should evaluate the different options and use the one that better fits the data volume and complexity.

How do you handle real-time data integration in Qlik Sense?

Handling real-time data integration in Qlik Sense involves incorporating new data into the application as soon as it becomes available, rather than waiting for a scheduled data load. Here are a few examples of how a data architect might handle real-time data integration in Qlik Sense:

1. Streaming data: Qlik Sense allows you to use a streaming data connector to integrate real-time data into your application. This connector can be used to subscribe to a stream of data and consume it in real-time.
2. Real-time data replication: Real-time data replication is the process of copying data from one database to another in real-time. In Qlik Sense, a data architect can use replication software to replicate data from the source system to a Qlik Sense data store.
3. API calls: Qlik Sense allows you to make API calls to external systems to retrieve data in real-time. The data architect can use a REST or SOAP connector to make API calls and retrieve data in real-time.
4. Web socket: Web socket is a way to establish a real-time connection between a web server and a client. In Qlik Sense, a data architect can use a web socket connector to establish a real-time connection to a web server and receive data in real-time.
5. Event-driven architecture: In this approach, the data architect sets up an event-driven architecture that triggers the data integration process when certain events occur. For example, when a new record is added to a database, an event is triggered, and the new data is immediately integrated into Qlik Sense.

It’s important to note that, real-time data integration may require additional resources and infrastructure, and the data architect should carefully evaluate the specific requirements of a project before deciding on a real-time data integration approach.

Can you explain how you would design and implement a data governance strategy for Qlik Sense?

Designing and implementing a data governance strategy for Qlik Sense involves creating a set of policies, procedures, and controls to ensure the integrity, quality, and security of the data used in the application. Here are some steps a data architect might take to design and implement a data governance strategy for Qlik Sense:

1. Define data governance policies: The data architect should define policies that govern the use, management, and maintenance of the data used in Qlik Sense. These policies should cover topics such as data quality, data security, data retention, and data access.
2. Establish data governance roles and responsibilities: The data architect should establish roles and responsibilities for data governance to ensure that everyone involved in the Qlik Sense project understands their role in maintaining data integrity and quality.
3. Establish data quality standards: The data architect should establish data quality standards to ensure that the data used in Qlik Sense is accurate, complete, and consistent. This might include procedures for data validation, data cleansing, and data standardization.
4. Establish data security standards: The data architect should establish data security standards to ensure that the data used in Qlik Sense is protected from unauthorized access, alteration, or disclosure. This might include procedures for data encryption, user authentication, and access control.
5. Implement data lineage tracking: The data architect should implement data lineage tracking to ensure that the data used in Qlik Sense can be traced back to its source. This might include procedures for data lineage documentation and data lineage visualization.

It’s important to note that the data governance strategy should be flexible and adaptable to the changing needs of the organization and the Qlik Sense application. The data architect should also ensure that the data governance strategy is aligned with the organization’s overall data governance strategy and regulatory compliance requirements.

How do you troubleshoot and resolve data-related issues in Qlik Sense?

Troubleshooting and resolving data-related issues in Qlik Sense involves identifying and resolving problems that occur during data loading, data modeling, and data visualization. Here are some steps a data architect might take to troubleshoot and resolve data-related issues in Qlik Sense:

1. Review the data load log: When data loading issues occur, the first step is to review the data load log. The log will provide information on any errors or warnings that occurred during data loading, which can help identify the source of the problem.
2. Check data connections: If the data load log does not provide information on the problem, the data architect should check the data connections to ensure that the correct data sources are being used and that the connections are configured correctly.
3. Check data transformations: If the data connections are correct, the data architect should check the data transformations to ensure that the data is being transformed correctly. This might include checking for incorrect calculations, missing data, or incorrect data types.
4. Check data model: If the data load and transformations are correct, the data architect should check the data model to ensure that the data is being modeled correctly. This might include checking for incorrect dimensions, incorrect joins, or missing fields.
5. Check data visualization: If the data model is correct, the data architect should check the data visualization to ensure that the data is being displayed correctly. This might include checking for incorrect charts, missing data, or incorrect data labels.

It’s important to note that troubleshooting and resolving data-related issues in Qlik Sense requires a systematic approach and a good understanding of the data, the data model, and the data visualization. The data architect should also be familiar with the tools and techniques available in Qlik Sense for troubleshooting and resolving data-related issues.

How do you stay current with new features and updates to Qlik Sense?

1. Mention the security mechanism in QlikView?

• It can either be built into the QlikView document script
• Lastly, it can be set up through the use of QlikView Publisher.

2. Define Authentication?

Authentication is defined as a process that can verify if someone is who they claim they are. QlikView either let the Windows operating system do the authentication, or prompt for a User ID and Password or use the QlikView license key as a simple authentication method.

3. Define Authorization?

Authorization is discovering out if the person, once recognized, is permitted to have the resource. QlikView can both let the Windows operating system do the authorization or do the authorization itself.

4. How to make an offline user document invisible?

QlikView documents can be made invisible in offline mode. Add the following property to the document information section of a user document using the QMC to make an offline user document invisible:

• Name:  Invisible
• Value: True

5. What do you understand by USERID?

USERID is defined as a field that includes an approved user ID. QlikView will prompt for a User ID and compare it to the value in this field. Moreover, this user ID is not the same as the Windows user ID.

6. How to hide data in a document from a user based on the section access login.?

• Firstly, Fields or columns can be hidden by the use of the system field OMIT.
• Secondly, records or rows can be hidden by linking the Section Access data with the real data

7. What is the difference between QlikView and Qlik Sense?

QlikView is based on guided analytics. Whereas, Qlik Sense offers self-service data discovery. Secondly, Qlik Sense offers self-service data discovery that considers the data model, the layout, the charts, and the formulas. Moreover, analysts are free to create new apps, visualizations, and bookmarks. It means there is less data development in the beginning because Qlik Sense app developers do not need to build scripts that meet every user’s need. Lastly, Qlik Sense is much easier to use than QlikView.

8. List some Data load limitations?

• Firstly, the amount of data that can be loaded into a QlikView document is very large.
• Lastly, a QlikView document cannot have more than 2,147,483,648 distinct values in one field.

9. How does QlikView helps in making new documents?

A QlikView document consists of a number of sheets that contain graphical charts and other sheet objects that allow the user to interact and analyze the data. Moreover, QlikView allows you to design and create your own clear, interactive charts and other sheet objects, and position them on the sheets in the document.

10. List the different types of Triggers?

• Firstly, Document Event Triggers
• Secondly, Field Event Triggers
• Lastly, Variable Event Triggers

11. What is List box?

The list box is the most basic sheet object. It contains a list of all possible values of a specific field. Moreover, a list box may also contain a cyclic or drill-down group.

12. What is the use of an Expressions tab?

The Expressions tab define expressions to be displayed in the list box. Each expression will be placed in a new column in the list box.

13. What do you understand by Statistics box?

The statistics box is a compact way of showing a numeric field in which the separate records are of less interest than e.g. their sum or average.

14. List the different types of QlikView reports?

• Firstly, document reports
• Secondly, user reports.

15. Mention the components of Alerts?

• A condition
• A logical state
• One or more actions to be performed when the condition is checked and evaluates to true.

16. Mention the Document events of Macros?

A macro can be run after:

• opening a QlikView document.
• script re-execution.
• the Reduce Data command.
• Lastly, a selection in any field in the document.

17. What are the applications of Routine analysis?

A typical activity in routine analysis is to follow up on key metrics (KPI) regularly, for example:

• Total sales against quota, each morning
• Total sales against total sales the same period last year
• Orders placed but not delivered at the end of the week
• Lastly, Sales per region on a certain day each month

18. Describe the use of Exploratory analysis?

QlikView lets you explore the data in numerous ways to find new insights, for example by:

• Filtering the data efficiently by making multiple selections
• Asking and answering what-if questions with comparative analysis
• Clicking or tapping anywhere for new views or more detail
• Lastly, Remixing and reassembling data any way you want to

19. Why a field cannot always be set to logical AND-mode?

A field cannot always be set to logical AND-mode because the AND alternative is logically meaningful only if the concerned field is linked to only one other field. 

20. List different types of Selection states?

• The selected state
• The possible state
• The alternative state
• The excluded state
• Lastly, the selected excluded state

21. List the default settings for a KPI?

The following settings are used by default in a KPI:

• Centered alignment.
• Black text color.
• Medium font size.
• No titles.
• Measure label displayed.
• Conditional colors off.
• No link to the sheet.

22. What do you understand by Key performance indicators (KPIs)?

Key performance indicators (KPIs) are used to evaluate the performance of a company. The KPIs display to what amount a number of goals have been reached. Different organizations have different goals, and it is important that the goals are well defined so that they are valid and reliable.

23. List the advantages and disadvantages of KPIs?

Advantages: KPIs give a quick understanding of the performance within an area.

Disadvantages: The KPI is somewhat limited when it comes to graphical components.

24. What is Backus-Naur formalism?

The Qlik Sense command line syntax and script syntax are described in a notation called Backus-Naur formalism, also known as BNF code.

25. List the different types of operators?

There are two types of operators:

• Firstly, Unary operators (take only one operand)
• Secondly, Binary operators (take two operands)

26. What are Bit operators?

A bit operator converts the operands to signed integers and returns the result in the same way. All the transactions are performed bit by bit. If an operand cannot be interpreted as a number, the operation will return NULL.

27. What Logical operators do?

All logical operators interpret the operands logically and return True (-1) or False (0) as result.

28. Explain Numeric operators?

All numeric operators use the numeric values of the operands and return a numeric value as result.

29. What do you understand by Relational operators?

All relational operators compare the values of the operands and return True (-1) or False (0) as the result. Moreover, all relational operators are binary.

30. Explain the function of the String operators?

There are two string operators. One uses the string values of the operands and return a string as result. Whereas, the other one compares the operands and returns a boolean value to indicate match.

31. What is a QlikView converter?

The QlikView converter is a tool to support converting QlikView documents to Qlik Sense apps. Moreover, it is use to move some of the value developed in QlikView documents to a Qlik Sense app.

32. What do you understand by Aggregation functions?

An aggregation function aggregates over the set of viable records defined by the collection and returns a single value describing a property of several records in the data, for example, a sum or a count.

33. What are Counter aggregation functions?

Counter aggregation functions return various types of counts of an expression over a number of records in a data load script, or a number of values in a chart dimension.

34. What is the use of Color functions?

Color functions can be used in expressions associated with setting and evaluating the color properties of chart objects, as well as in data load scripts.

35. What are Conditional functions?

The conditional functions evaluate a condition and then return different answers depending on the condition value. Moreover, these functions can be used in the data load script and in chart expressions.

36. Mention the applications of Field functions?

Field functions can only be used in chart expressions. Moreover, these functions either return integers or strings identifying different aspects of field selections.

37. Explain Range functions?

The range functions are functions that take an array of values and produce a single value as a result. However, all range functions can be used in both the data load script and in chart expressions.

38. How will you build effective visualizations?

To build effective visualizations, one should:

• Firstly, understand the data sources for your visualizations
• Secondly, select visualization types that align with your purpose
• Lastly, update visualizations to help users understand the data

39. List the Data assets available when creating visualizations?

The following data assets are available when creating visualizations:

• Fields
• Measures
• Dimensions
• Lastly, Master items

40. Define Expressions?

An expression is a combination of functions, fields, and mathematical operators (+ * / =). They’re used to process data in the app and provide a visualization of the outcome. Furthermore, expressions are generally employed to construct measurements.

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