15 Qlik Sense Interview Questions and Answers

Qlik Sense is a leading data visualization and business intelligence tool that enables users to create interactive and shareable dashboards. Known for its powerful associative data engine, Qlik Sense allows for intuitive data exploration and discovery, making it a valuable asset for organizations looking to leverage data-driven insights. Its user-friendly interface and robust capabilities make it a popular choice among data analysts and business intelligence professionals.

This article provides a curated selection of interview questions designed to test your knowledge and proficiency in Qlik Sense. By reviewing these questions and their detailed answers, you will be better prepared to demonstrate your expertise and problem-solving abilities in a Qlik Sense-focused interview setting.

Qlik Sense Interview Questions and Answers

1. Write a script to load data from a CSV file.

To load data from a CSV file in Qlik Sense, use the Data Load Editor. The script below demonstrates how to load data from a CSV file named “data.csv” located in the same directory as the Qlik Sense app.

LOAD
    Field1,
    Field2,
    Field3
FROM [lib://DataFiles/data.csv]
(txt, codepage is 1252, embedded labels, delimiter is ',', msq);

In this script:
– LOAD specifies the fields to be loaded.
– FROM specifies the path to the CSV file. The lib://DataFiles/ prefix references the data connection.
– The parameters define the file format, including text encoding, presence of embedded labels, delimiter, and multi-line support.

2. Use set analysis to filter data for a specific year.

Set analysis in Qlik Sense allows you to define a subset of data different from the current selection. To filter data for a specific year, use the following syntax within an expression:

Sum({<Year={2023}>} Sales)

This expression filters the data to include only records where the Year field is 2023, and the Sum function calculates the total sales for that year.

3. Implement section access to restrict data visibility based on user roles.

Section access in Qlik Sense controls data visibility based on user roles. It is implemented by defining access rights in a script, ensuring users only see authorized data.

Example:

SECTION ACCESS;
LOAD * INLINE [
    ACCESS, USERID, REDUCTION, OMIT
    ADMIN, ADMIN, *, *
    USER, USER1, 1, 
    USER, USER2, 2, 
];

SECTION APPLICATION;
LOAD * INLINE [
    REDUCTION, DATA
    1, Data for User1
    2, Data for User2
];

In this example, the SECTION ACCESS part defines access rights, and the SECTION APPLICATION part contains the actual data, filtered based on the REDUCTION field.

4. Write an expression to create a dynamic chart that shows sales by month.

To create a dynamic chart in Qlik Sense that shows sales by month, use Qlik Sense expressions. These expressions allow you to manipulate and analyze data dynamically within your visualizations. Use the Sum function to aggregate sales data and the Month function to group the data by month.

Example:

Sum(Sales)

For the dimension field:

Month(Date)

This creates a dynamic chart displaying total sales for each month, updating based on data and selections.

5. How would you transform data during the load process to concatenate two fields?

To concatenate two fields during the load process, use the & operator within the LOAD statement.

Example:

LOAD
    Field1,
    Field2,
    Field1 & ' ' & Field2 as ConcatenatedField
FROM
    DataSource;

This creates a new field, ConcatenatedField, combining Field1 and Field2 with a space in between.

6. How would you use a mapping load to replace field values in your dataset?

A mapping load in Qlik Sense creates a mapping table to replace field values in your dataset. The mapping table consists of two columns: original values and new values.

Example:

// Load the mapping table
MappingTable:
MAPPING LOAD
    OriginalValue,
    NewValue
FROM
    MappingFile.csv (txt, utf8, embedded labels, delimiter is ',');

// Load the main data
MainData:
LOAD
    Field1,
    Field2,
    ApplyMap('MappingTable', FieldToReplace, 'DefaultValue') AS FieldToReplace
FROM
    MainDataFile.csv (txt, utf8, embedded labels, delimiter is ',');

The ApplyMap function replaces values in FieldToReplace with corresponding values from MappingTable.

7. What are synthetic keys, and how would you address them in your data model?

Synthetic keys are generated when tables have multiple common fields, leading to ambiguous relationships. To address them, you can:

– Concatenate tables if they are logically similar.
– Rename fields to avoid synthetic keys.
– Create composite keys by combining common fields.
– Use mapping tables to resolve relationships without synthetic keys.

8. How would you identify and resolve circular references in your data model?

Circular references occur when there are multiple ways to traverse between tables, creating loops. To resolve them:

– Use the Data Model Viewer to identify circular references.
– Break the loop by removing unnecessary joins or creating link tables.
– Use the QUALIFY statement to rename fields and avoid automatic associations.
– Concatenate tables to eliminate circular references.

9. Use advanced set analysis to calculate the average sales for the top 10 customers.

Advanced set analysis allows for complex data filters and aggregations. To calculate the average sales for the top 10 customers:

Avg({<Customer = {"=Rank(Sum(Sales))<=10"}>} Sales)

This expression calculates the average sales for the top 10 customers based on total sales.

10. Write a script using the Aggr function to calculate the sum of sales per region.

The Aggr function creates a temporary table of aggregated data for further calculations. To calculate the sum of sales per region:

Sum(Aggr(Sum(Sales), Region))

The Aggr function aggregates the sum of sales for each region, and the outer Sum function sums these values.

11. Implement an incremental load to update your dataset with new records.

Incremental loading involves loading only new or updated records, rather than reloading the entire dataset. This is useful for large datasets. The process typically involves:

– Identifying new or updated records.
– Loading these records.
– Merging them with the existing dataset.

Example:

// Load the existing data
ExistingData:
LOAD *
FROM [lib://DataFiles/ExistingData.qvd] (qvd);

// Load new or updated records
NewData:
SQL SELECT *
FROM SourceTable
WHERE LastModified > (SELECT MAX(LastModified) FROM ExistingData);

// Concatenate new records
Concatenate (ExistingData)
LOAD *
RESIDENT NewData;

// Store updated dataset
STORE ExistingData INTO [lib://DataFiles/ExistingData.qvd] (qvd);

12. Explain the benefits of using QVD files and how you would create one.

QVD files offer several benefits:

– High-speed data retrieval.
– Efficient storage with compression.
– Support for incremental loading.
– Data consistency across applications.

To create a QVD file:

LOAD
    Field1,
    Field2,
    Field3
FROM
    SourceData;

STORE TableName INTO TableName.qvd (qvd);

13. What strategies would you use to optimize the performance of a Qlik Sense application?

To optimize Qlik Sense application performance:

– Use star or snowflake schemas in data modeling.
– Manage system resources efficiently.
– Pre-aggregate data to reduce runtime calculations.
– Simplify expressions and use set analysis.
– Design efficient dashboards with limited objects.
– Monitor memory usage and server configuration.

14. Describe techniques to optimize data loading performance.

To optimize data loading performance:

– Reduce data by filtering unnecessary records.
– Write efficient load scripts.
– Use QVD files for fast data retrieval.
– Design an efficient data model.
– Load data in parallel if possible.
– Monitor memory usage during the load process.

15. How would you integrate real-time data sources?

Integrating real-time data sources involves using Qlik’s connectors and data streaming capabilities. Tools like Qlik Data Transfer, Qlik Replicate, and Qlik Compose enable real-time ETL processes. APIs and webhooks can push data into Qlik Sense as it becomes available, and message brokers like Apache Kafka can handle streaming data.