QlikView QSDA2024 Exam Dumps & Practice Test Questions

Question 1:

Which two techniques are most effective in enhancing the performance of a QlikView application? (Choose two.)

A. Utilizing QVD files for data storage
B. Refining the user interface layout
C. Loading data from the front-end interface
D. Implementing incremental data loading strategies
E. Using inline script statements to load data

Correct Answers: A and D

Explanation:

When working with QlikView applications, optimizing performance is essential to ensure fast, efficient, and reliable analytics delivery, especially in environments with large datasets or complex data models. Two of the most effective ways to boost performance are through the use of QVD (QlikView Data) files and incremental data loading.

Option A: Utilizing QVD Files
QVDs are a proprietary file format developed by Qlik to store data in a highly compressed, binary format. These files are read extremely quickly by QlikView because they are already formatted in a way that Qlik’s in-memory engine can process without additional transformation. By using QVDs, you eliminate the overhead of querying external databases for every reload, significantly reducing load times. Additionally, QVDs can be reused across multiple applications, making them ideal for building scalable, modular data architectures.

Option D: Implementing Incremental Loads
Incremental loading is a strategy where only new or changed records are loaded from the data source, instead of reloading the entire dataset during every execution. This drastically reduces the amount of data that QlikView has to process, speeding up reload times and conserving memory. It’s particularly effective for applications that pull data from large transaction logs or systems with frequent updates. Incremental loading also supports better system scalability and maintenance.

Let’s consider why the other options are less effective:

• Option B: Optimizing the UI layout can improve user interaction and responsiveness but does not significantly affect the underlying data processing speed. It’s more about front-end rendering rather than true performance gains in data load or memory efficiency.

• Option C: Loading from the front-end is not a supported practice in QlikView. Data should always be loaded and modeled via the script editor in the back-end for consistency, security, and performance.

• Option E: Inline scripts are good for hardcoding small sets of static data but are not suitable for dynamic or large data sets. They don’t contribute to improving data load speed and are limited in scale.

In summary, using QVD files and applying incremental data loads are two foundational best practices in QlikView for significantly improving performance in both development and production environments.

Question 2:

Which two features are commonly used in QlikView to manage user access and data-level security? (Choose two.)

A. Section Access
B. Role-Based Access Control (RBAC)
C. QlikView Publisher
D. User directory authentication
E. Document-level security settings

Correct Answers: A and E

Explanation:

Controlling user access and protecting sensitive data are critical components of any business intelligence platform. In QlikView, this is primarily accomplished through Section Access and Document-Level Security Settings, both of which allow administrators to define who can access specific data or perform certain actions within an application.

Option A: Section Access
Section Access is QlikView’s built-in method for implementing row-level and field-level security directly within the application script. It enables administrators to define users and assign permissions based on roles or user-specific attributes. For example, a sales manager can be granted access to only their region’s data while preventing visibility into other areas. The Section Access script typically includes fields like USERID, ACCESS, and reduction fields that filter the dataset during application load. It is enforced both in QlikView Desktop and QlikView Server, ensuring secure and consistent access control.

Option E: Document-Level Security
This approach allows for more granular control over user actions at the document interface level. For instance, administrators can restrict whether users can export data, make selections, or access specific sheets. These settings are applied directly in the QlikView document properties and complement Section Access by adding an extra layer of security beyond just data access.

Why the other options are not correct:

• Option B: Role-Based Access Control (RBAC) is more relevant to enterprise-wide identity and access management systems. While Qlik Sense supports RBAC natively, QlikView relies more on Section Access for access control rather than a formal RBAC system.

• Option C: QlikView Publisher is a tool for distributing and scheduling QlikView documents. While it can help with data reduction during document distribution, it is not directly responsible for enforcing user-level security inside documents.

• Option D: User directories (e.g., Active Directory) are used for authentication—confirming a user’s identity. However, this does not control what data a user can see or what actions they can perform. Authorization—handled by Section Access—is the mechanism for managing those privileges.

To conclude, Section Access and Document-level security settings are the primary tools in QlikView for controlling user access and protecting data integrity, making A and E the correct answers.

Question 3:

Which two practices are most important for building an efficient and reliable data model in QlikView? (Select two.)

A. Designing a star or snowflake schema to structure data
B. Linking tables through synthetic keys
C. Reducing the total number of tables in the model
D. Establishing relationships using unique key fields
E. Choosing in-memory storage to boost performance

Correct Answers: C and D

Explanation:

In QlikView, successful data modeling is essential for creating responsive, accurate, and maintainable dashboards and analytics applications. Unlike traditional relational databases, QlikView uses an associative in-memory model, which requires specific best practices to ensure optimal performance and data integrity. Two of the most important practices are minimizing the number of tables in your model and using unique keys to connect those tables.

• C. Minimizing the number of tables in the model
  A lean data model is a hallmark of QlikView best practices. By reducing the number of tables—through techniques like joins, concatenation, or the use of mapping—you simplify the structure of the data model. This makes it easier for QlikView to manage relationships and for developers to debug, optimize, and maintain the application. Fewer tables typically mean fewer synthetic keys, less complexity, and faster performance, especially when working with large datasets.

• D. Creating associations with unique keys
  Establishing explicit, unique key fields is critical for creating correct relationships between tables. QlikView automatically builds associations based on field names, so if common fields exist in multiple tables, it will attempt to link them—sometimes incorrectly. Unique, well-managed keys allow QlikView’s associative engine to work efficiently, enabling intuitive filtering and selection without the need for manual joins or complex logic. Poor key management, on the other hand, can introduce circular references and synthetic keys, which degrade both performance and accuracy.

Now, evaluating the incorrect options:

• A. Designing a star or snowflake schema
  While these are useful in traditional BI systems, QlikView doesn't depend on them. QlikView is schema-less by nature and operates best with flat or minimal hierarchy models. Therefore, this is not essential.

• B. Using synthetic keys
  Synthetic keys are automatically created when two or more fields exist in multiple tables. While QlikView supports them, they are considered a sign of poor modeling. They can create confusion, performance issues, and inaccurate data relationships.

• E. Choosing in-memory storage
  QlikView stores all data in memory by default. This is a built-in feature, not something the developer controls. While it enhances performance, it is not a modeling decision.

Conclusion:
For an optimized and reliable QlikView model, it's essential to simplify the table structure and use unique keys for precise associations. These two practices ensure the application remains scalable, maintainable, and performs well under load.

Question 4:

Which two techniques are effective for improving QlikView performance when working with large datasets? (Select two.)

A. Use the "Keep" keyword to limit data
B. Rely on synthetic keys for table linking
C. Break up large datasets into smaller QVD files
D. Use the AutoNumber function to optimize memory
E. Apply data transformations in the dashboard frontend

Correct Answers: C and D

Explanation:

Handling large volumes of data in QlikView requires intelligent optimization strategies to maintain fast load times, minimize memory consumption, and ensure a smooth user experience. Two proven methods that significantly enhance performance are splitting large datasets into multiple QVDs and using the AutoNumber function.

• C. Splitting datasets into smaller QVD files
  Breaking large datasets into segmented QVD files (e.g., by region, year, or category) enables incremental data loading. Instead of processing a massive file every time, you load only the relevant partitions. This strategy reduces load time and conserves memory, allowing your application to remain scalable even as data grows. QVDs are also highly optimized storage formats in QlikView, offering significantly faster read performance compared to querying directly from source systems.

• D. Using AutoNumber to reduce memory usage
  The AutoNumber function converts repeated string values (such as customer names or IDs) into integers, which consume less memory. This is especially useful for fields with high cardinality, such as product codes or transaction IDs. Memory savings compound as data volume increases, and the impact is most notable in large, associative models where keys appear in multiple tables.

Now let’s assess the other options:

• A. Using the "Keep" keyword
  While "Keep" helps filter and relate tables (similar to joins), it doesn’t offer significant performance improvements. It’s more useful for controlling data scope rather than optimizing large-scale models.

• B. Creating synthetic keys
  Although QlikView allows synthetic keys, they are a performance liability. Automatically generated keys from multiple field matches often lead to unpredictable associations, increased memory usage, and poor performance. They should be avoided and resolved during the modeling process.

• E. Transforming data in the frontend
  Applying complex calculations or transformations in the UI slows down every interaction (e.g., filtering, clicking, refreshing charts). These should be pre-processed in the script during load time to reduce real-time computation and improve responsiveness.

Conclusion:
Both splitting datasets into multiple QVD files and using AutoNumber to optimize key fields are essential for efficiently handling large-scale data in QlikView. They reduce memory load, enhance performance, and support a more robust analytical experience.

Question 5:

Which two QlikView scripting functions are most effective for reducing data load times and optimizing performance during data processing? (Choose two.)

A. ApplyMap()
B. Peek()
C. Join()
D. Resident Load
E. Concatenate()

Correct Answers: A and D

Explanation:

When working with large data models in QlikView, improving script efficiency and minimizing load times are critical to maintaining a responsive and scalable application. Among the tools available in QlikView scripting, two functions stand out for optimizing these aspects: ApplyMap() and Resident Load.

ApplyMap() is a powerful function that performs fast, efficient lookups to replace or enrich values during data loading. It is commonly used instead of more expensive JOIN operations when only a single field value needs to be mapped. For example, mapping a customer ID to a customer name or a code to a description is a perfect use case. Unlike joins that create larger intermediate tables and increase memory usage, ApplyMap() works inline during data load, which means less processing overhead. This function significantly enhances load performance, particularly in cases involving repeated lookups or reference tables.

Resident Load is another key function that contributes directly to load-time efficiency. It allows you to reuse an already-loaded table in memory for additional transformations, filtering, or aggregations without going back to the original data source. This means that instead of re-querying a database, which could be slow and introduce network latency, QlikView processes data that's already available in memory. This not only improves performance but also offers flexibility in creating staged transformations through stepwise logic, which is both easier to maintain and faster to execute.

Now, let’s consider why the other options are less suitable for this particular question:

• Peek(): Although useful for accessing individual values from previous rows, Peek() is typically used for row-by-row calculations or setting default values. It doesn’t have a notable impact on overall load time or processing efficiency across large datasets.

• Join(): While powerful, joins are often resource-intensive, especially when performed on large tables. Improperly handled joins can result in synthetic keys, bloated data models, and longer load times. Thus, in performance-sensitive environments, ApplyMap() is often preferred.

• Concatenate(): This function appends data from one table to another when structures are compatible. While useful for data modeling, it doesn't directly optimize the load process or reduce processing overhead.

In conclusion, if your goal is to accelerate data load and optimize script efficiency, ApplyMap() for fast lookups and Resident Load for leveraging in-memory data are the top-performing choices. Hence, the correct answers are A and D.

Question 6:

Which two of the following statements correctly describe QlikView's Set Analysis capabilities? (Choose two.)

A. Set Analysis lets you build expressions that can bypass user selections
B. Set Analysis is only available in charts and cannot be used in the script
C. Set Analysis allows data filtering based on specific fields within expressions
D. Set Analysis cannot function without variables
E. Set Analysis enables simultaneous analysis of multiple fields

Correct Answers: A and C

Explanation:

Set Analysis is one of QlikView's most powerful features for creating advanced, dynamic expressions that apply custom filtering criteria independently of current user selections. This makes it particularly effective in dashboard and chart creation, where analysts often need to display consistent metrics or KPIs regardless of how users interact with filters.

Option A is correct because Set Analysis allows you to override selections made by users. For instance, you might want to create a chart that always shows data for the year 2023, even if a user selects a different year. This can be achieved using syntax like:

This expression ensures that sales for 2023 are displayed, no matter what selection is made in the Year field. This behavior makes Set Analysis incredibly powerful for building locked-in comparisons, benchmarks, and exception-based reporting.

Option C is also correct. One of the primary functions of Set Analysis is to filter datasets dynamically using conditions on fields like Region, Product, or Year. It provides precise control over which subset of data is included in the expression evaluation. For example:

This expression filters the dataset to include only records matching the specified Region and Product before computing the sum of Revenue.

Let’s evaluate the other options:

• Option B: While it is true that Set Analysis is only used in the UI layer (charts, KPIs, etc.) and not in the script, this statement is more about scope than functionality. Although accurate, it is not as conceptually important as A and C in terms of defining what Set Analysis does.

• Option D: This is incorrect. Variables are optional in Set Analysis. They can make expressions dynamic or adaptable, but many expressions do not use variables at all. Set Analysis can work perfectly without them.

• Option E: This is misleading. Set Analysis can filter across multiple fields, but it doesn’t “analyze” them simultaneously in a statistical sense. The term “analyze” here may confuse learners, making this answer too vague to be selected.

In summary, Set Analysis excels in enabling context-specific data filtering within expressions and allowing independence from user selections. The two best statements that reflect its real functionality are A and C.

Question 7:

In QlikView, which two functions are specifically designed to detect or manage null values during data analysis or expression evaluation? (Choose two.)

A. If()
B. IsNull()
C. NullAsValue()
D. Coalesce()
E. Len()

Correct Answers: A and B

Explanation:

When working with data in QlikView, handling null values effectively is vital for building accurate reports, performing clean calculations, and maintaining data integrity. Nulls may occur due to missing data entries, failed joins, or transformations that result in undefined values. Two core functions in QlikView that directly help manage or identify nulls are If() and IsNull().

• A. If():
  The If() function is one of the most flexible and widely used conditional tools in QlikView. It allows you to create logical conditions and define alternate outcomes based on whether a specific condition is true or false. In the context of null handling, it is commonly paired with IsNull() to create fallback values. For instance:
  If(IsNull(Sales), 0, Sales)
  This logic substitutes any null value in the "Sales" field with 0. While If() does not detect nulls on its own, its combination with IsNull() makes it a critical part of null value processing in QlikView expressions.

• B. IsNull():
  This function is explicitly designed to detect if a value is null. It returns True if the provided value is null, and False otherwise. IsNull(Field) is the most direct way to identify and handle nulls, making it indispensable for data validation and transformation logic.

Let’s now address the incorrect options:

• C. NullAsValue():
  This is not a function, but rather a script directive used in the load script. It tells QlikView to treat null values in specified fields as actual field values, making them selectable in list boxes or filters. However, since the question explicitly asks for functions, NullAsValue() does not qualify.

• D. Coalesce():
  While Coalesce() is common in SQL and returns the first non-null value among its arguments, QlikView does not support this function natively. Instead, similar functionality must be manually built using nested If() statements.

• E. Len():
  The Len() function returns the length of a string. It can sometimes help detect empty strings, but it does not properly handle null values, which are different from blank strings. For instance, Len(null()) returns null, not zero, making it unsuitable for precise null detection.

In summary, the correct choices—If() and IsNull()—are essential for any QlikView developer or analyst working with incomplete or inconsistent datasets.

Question 8:

Which two of the following are considered key components of the QlikView architecture responsible for delivering data access and distribution in enterprise environments? (Choose two.)

A. QlikView Server
B. QlikView Desktop
C. QlikView Publisher
D. QlikView Management Console (QMC)
E. QlikView Analytics Platform

Correct Answers: A and C

Explanation:

The QlikView architecture is designed to support powerful data analytics and business intelligence solutions, especially in large-scale enterprise deployments. Among its various tools and components, QlikView Server (QVS) and QlikView Publisher form the foundation for data delivery, access control, and performance management.

• A. QlikView Server (QVS):
  This is the central engine of the QlikView deployment. It handles client-server communications, manages user sessions, loads .qvw files into memory, and ensures that multiple users can access QlikView documents concurrently through web browsers or the QlikView client. The server is responsible for data caching, load balancing, and secure communication, making it crucial for multi-user environments.

• C. QlikView Publisher:
  Publisher is used for automating data reloads, distributing documents, and applying data reduction policies using section access. It allows administrators to control which subsets of data each user can access, as well as schedule updates to ensure data freshness. Publisher is essential for maintaining data governance and efficiency in high-volume deployments.

Let’s now review why the remaining options are not considered architectural core components:

• B. QlikView Desktop:
  While essential for developing and designing QlikView applications, Desktop is primarily a developer’s tool rather than a part of the deployed architecture. It is used to create .qvw files, but once deployed, the architecture relies on the server components to distribute and render applications.

• D. QlikView Management Console (QMC):
  The QMC is a management interface, not a standalone architectural component. It enables administrators to configure tasks, manage licenses, monitor usage, and control document access. However, it does not itself serve or process data—those roles are fulfilled by the Server and Publisher.

• E. QlikView Analytics Platform:
  This term is not a defined component within the QlikView ecosystem. It may refer broadly to Qlik’s overall analytics capabilities but is more commonly associated with Qlik Sense, the newer, more modern BI platform by Qlik.

In conclusion, the two components at the heart of QlikView’s architecture that handle real-time data access and distribution are QlikView Server (A) and QlikView Publisher (C).

Question 9:

In QlikView, which two object types are specifically designed to improve data visualization and allow users to gain deeper insights through dashboards?

A. Pivot tables
B. QVD tables
C. Scatter charts
D. Expression labels
E. List boxes

Correct Answers: A and C

Explanation:

QlikView is a powerful tool for business intelligence that allows users to explore data interactively through visual dashboards. Among the various object types it supports, some are specifically meant to enhance data visualization—providing users with clear, graphical insights into patterns, relationships, and trends.

A. Pivot Tables: These are essential visualization objects in QlikView. They allow data to be dynamically summarized and rearranged by dragging and dropping dimensions and measures. Users can drill into data, apply aggregations, and visualize complex datasets in a compact and structured format. Their interactivity and flexibility make pivot tables a key component of most QlikView dashboards.

C. Scatter Charts: Scatter charts provide a graphical view that helps users spot correlations or anomalies between two (or more) numerical values—such as revenue vs. profit, or units sold vs. customer visits. This visualization type is especially helpful in identifying outliers and trends across multiple data points. In QlikView, scatter charts are interactive and respond to selections made in other dashboard objects, enhancing data exploration.

Now, why the other options are incorrect:

• B. QVD Tables: QVDs (QlikView Data files) are storage formats, not visual objects. They are crucial for efficient data loading but don’t provide any visual representation.

• D. Expression Labels: These improve readability by labeling measures in charts or tables, but they do not visualize data themselves.

• E. List Boxes: While useful for data selection and filtering, list boxes are not visualization tools in the traditional sense—they don’t graphically represent metrics or trends.

Conclusion: Pivot tables and scatter charts are the correct answers because they directly improve how users visualize and interpret data in a QlikView dashboard.

Question 10:

Which two practices contribute to building a robust and efficient data model in QlikView?

A. Avoid circular references and synthetic keys
B. Join all tables directly to the fact table
C. Use concatenation to merge tables with shared fields
D. Use direct connections for faster data loads
E. Build a star schema with fact and dimension tables

Correct Answers: A and E

Explanation:

Creating a strong data model is foundational to successful QlikView applications. A well-structured model improves performance, ensures accurate associations, and simplifies analysis. Two best practices stand out:

A. Avoid Circular References and Synthetic Keys: These are common pitfalls in QlikView data modeling. Circular references occur when multiple relationships form loops between tables, causing ambiguity. Synthetic keys are auto-generated by QlikView when two tables share multiple fields with the same name. These can lead to unpredictable behavior and degraded performance. To prevent this, developers often rename fields, use link tables, or adjust the structure to ensure clear, single-path associations.

E. Build a Star Schema: A star schema organizes data with a central fact table (containing measurable data like sales or revenue) surrounded by dimension tables (such as product, customer, or time). This structure is not only easy to understand but also aligns well with QlikView’s associative engine. It reduces redundancy, simplifies queries, and enhances performance.

Why the other options are incorrect:

• B. Join all tables directly to the fact table: While tempting, this can flatten the model unnecessarily, creating complexity and increasing the chance of misassociations or performance issues.

• C. Use Concatenation: Concatenation is appropriate for combining similar tables (like appending rows), but it’s not a method for strengthening relational data models.

• D. Use Direct Connections: While connecting directly to databases might seem faster, it's not a modeling best practice. Pre-loading and transforming data into QVDs is more efficient and scalable.

Conclusion: The best modeling practices are to avoid circular references and synthetic keys (A) and use a star schema design (E), both of which lead to clearer, faster, and more reliable QlikView applications.