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QSDA2024 QlikView Practice Test Questions and Exam Dumps

Question 1

Which two of the following features can be used to improve the performance of a QlikView application? (Choose 2.)

A. Use of QVD (QlikView Data) files for data storage
B. Optimizing the layout and design of the user interface
C. Loading data directly from the QlikView front-end
D. Enabling incremental data loads to reduce data volume
E. Using inline scripts to speed up data load times

Correct Answer: A and D

Explanation
Improving the performance of a QlikView application involves a combination of optimizing data handling, scripting practices, and interface design. Among the options presented, two features stand out as particularly effective for performance enhancement: using QVD files for data storage and enabling incremental data loads.

Let’s examine each option:

• Option A: Use of QVD (QlikView Data) files for data storage
  QVD files are a proprietary QlikView format used for efficient data storage and retrieval. When data is loaded from a QVD file, it is significantly faster than loading the same data from a traditional database or Excel file. This is because QVDs are optimized for speed and contain data that has already been structured for QlikView's internal engine. Moreover, QVDs help in reusing pre-processed data across multiple applications, minimizing load times and enhancing performance.

• Option B: Optimizing the layout and design of the user interface
  While layout optimization can enhance user experience and reduce rendering time on the front end, it has a relatively limited effect on overall application performance compared to data load strategies. Excessive use of complex charts, calculated dimensions, or expressions can slow down the interface, but this option does not directly contribute to back-end performance improvement.

• Option C: Loading data directly from the QlikView front-end
  QlikView applications are designed to load data via the script editor, not from the front-end interface. Loading from the front end is not standard practice and doesn't offer performance advantages. In fact, trying to manipulate data loads through front-end interactions can increase complexity and reduce efficiency.

• Option D: Enabling incremental data loads to reduce data volume
  Incremental loading is a powerful method for optimizing QlikView performance. Instead of reloading the entire dataset every time, the application only loads new or changed data. This significantly reduces the amount of data processed during each reload, leading to faster load times and less memory usage. It is especially effective in applications that deal with large datasets or where frequent updates are needed.

• Option E: Using inline scripts to speed up data load times
  Inline scripts are mainly used for entering small amounts of static data directly into the script. While they can simplify script writing for small datasets, they are not scalable and do not improve load performance for larger, dynamic datasets. Therefore, they are not considered an effective method for performance enhancement.

In summary, A. using QVD files and D. enabling incremental loads are the most effective strategies for improving QlikView application performance. These methods focus on efficient data handling and reduce resource consumption, which are key to building responsive and scalable analytics applications.

Question 2

Which two methods listed below are used to control user security and access in QlikView? (Select two options.)

A. Section Access
B. Role-based access control (RBAC)
C. QlikView Publisher
D. Using user directories for authentication
E. Document-level security settings

Correct Answer : A and E

Explanation:

In QlikView, controlling user access and security is vital to ensure that sensitive business data is only visible to authorized individuals. QlikView provides multiple mechanisms for implementing these controls, with Section Access and Document-level security settings being two of the primary methods.

Section Access is a built-in QlikView feature used to control access to data within an application. It is configured by defining a special access control script directly in the QlikView document. Through Section Access, administrators can define which users can see which data, down to very granular levels. For instance, you can restrict users to only see the rows and fields in a QlikView application that are relevant to their role or department. This form of security is enforced both in the QlikView Desktop and on the QlikView Server, ensuring consistency. Section Access can manage user authentication and authorization based on fields like USERID, PASSWORD, ACCESS LEVEL (ADMIN/USER), and reduction fields that filter the data set visible to each user.

Document-level security settings are another important method for controlling user access in QlikView. These settings are applied directly within a QlikView document's properties. Administrators can use these settings to control what a user can or cannot do within a document. For example, you can configure the document so that users cannot reload data, cannot export data, cannot make selections, or cannot access certain sheets. This security model complements Section Access by providing more user interface-level control beyond just data visibility.

Let’s now review why the other options are incorrect:

B. Role-based access control (RBAC) – While RBAC is a general security model used widely across IT systems, QlikView traditionally relies more on Section Access and document security rather than a formal RBAC model. Qlik Sense, a more modern product from Qlik, does incorporate RBAC more clearly, but in QlikView, access control is primarily handled through Section Access scripts and document-level permissions.

C. QlikView Publisher – QlikView Publisher is a distribution and task management tool. It is primarily used for scheduling data reloads, distributing QlikView documents to users, and managing data reduction during document distribution. While it plays a role in delivering content securely, it is not itself a method of managing user security and access within a QlikView document.

D. Using user directories for authentication – While user directories (such as Active Directory) are important for authenticating users (confirming their identity), authentication is different from access control. Authentication verifies who a user is, whereas access control determines what the user is allowed to see and do. The question specifically asks about managing user security and access, not just authentication.

In conclusion, the two correct methods that QlikView uses to manage user security and access are A (Section Access) and E (Document-level security settings), because they directly control what data and functionality users can access within a QlikView document.

Question 3

Which two elements are crucial for successful data modeling in QlikView? (Choose two.)

A. Creating a star schema or snowflake schema for data organization
B. Using synthetic keys to link multiple data sources
C. Minimizing the number of tables in the data model
D. Creating links between tables using unique keys for effective association
E. Storing all data in memory for improved data retrieval speed

Correct answers: C and D

Explanation:

QlikView is a powerful business intelligence tool that enables data visualization and analytics through its associative data model. Effective data modeling is at the core of ensuring that QlikView applications run efficiently, provide accurate insights, and are easy to maintain. To achieve this, QlikView developers need to apply key principles that reduce complexity and improve performance.

Let’s analyze each option based on how well it supports effective data modeling in QlikView.

A. Creating a star schema or snowflake schema for data organization
While traditional relational databases benefit from using star and snowflake schemas, QlikView does not require or depend on these schemas. QlikView’s associative data model works best with simplified structures. Although it can accommodate star-like designs, it is more important to reduce unnecessary joins and complex hierarchies. Therefore, while star schemas can be used, they are not essential in QlikView.

B. Using synthetic keys to link multiple data sources
Synthetic keys occur in QlikView when two or more tables have two or more fields in common. While QlikView automatically generates synthetic keys to manage these relationships, relying on them is discouraged. They can cause performance issues, lead to data inaccuracies, and make the data model harder to interpret. Best practice is to resolve synthetic keys by renaming or removing fields so that explicit and controlled associations are created. Hence, using synthetic keys is not considered an effective data modeling strategy.

C. Minimizing the number of tables in the data model
Reducing the number of tables is a key technique in QlikView data modeling. By concatenating tables or joining related data during the data load process, developers can simplify the structure, improving performance and manageability. A flatter model reduces complexity and makes it easier for QlikView’s engine to build associations efficiently. Therefore, minimizing the number of tables is an essential practice for optimal data modeling in QlikView.

D. Creating links between tables using unique keys for effective association
QlikView’s strength lies in its associative model, which depends on key fields that link data between tables. Using unique or properly managed keys allows QlikView to automatically associate data and enable drill-downs, selections, and dynamic filters. This makes the application intuitive and highly responsive. Proper key management is essential to avoid circular references and synthetic keys, so creating links with unique keys is a fundamental best practice.

E. Storing all data in memory for improved data retrieval speed
While QlikView is an in-memory analytics engine, this feature is inherent to the tool, not a design decision in the data modeling process. You don’t choose whether to store data in memory—QlikView automatically does this. Therefore, although in-memory storage does provide performance benefits, it is not a modeling technique or a step the developer takes to create an effective data model.
Effective data modeling in QlikView depends on keeping the model simple and ensuring proper associations between tables. This means minimizing unnecessary complexity (like too many tables or synthetic keys) and explicitly linking data through well-structured keys.

Correct answers: C and D

Question 4

Which two of the following are valid ways to optimize a QlikView document for large datasets? (Choose 2.)

A. Use the "Keep" keyword to reduce the data load time
B. Create synthetic keys to allow easier linking of large datasets
C. Split large datasets into multiple smaller QVD files for more efficient storage
D. Use the "AutoNumber" function to reduce memory usage
E. Perform data transformations directly in the QlikView frontend

Correct Answer: C and D

Explanation
Optimizing QlikView documents for large datasets is crucial for maintaining efficient performance, fast loading, and manageable memory usage. Among the listed options, splitting large datasets into multiple smaller QVD files and using the AutoNumber function are two well-established practices that directly contribute to performance optimization when dealing with large volumes of data.

Let’s examine each option:

• Option A: Use the "Keep" keyword to reduce the data load time
  The "Keep" prefix (such as "Inner Keep" or "Left Keep") allows you to load tables with a filtered relationship, similar to joins, but it is not specifically used for reducing load time. While it can help limit the data loaded, the "Keep" function is not inherently a performance optimization technique for large datasets and doesn't provide a significant advantage over more efficient data handling methods like QVDs and optimized joins.

• Option B: Create synthetic keys to allow easier linking of large datasets
  This is actually a bad practice in QlikView. Synthetic keys occur when QlikView finds multiple common fields between two or more tables and automatically creates a composite key. While it may seem like it simplifies data modeling, synthetic keys often lead to increased memory usage, confusing data associations, and potential performance degradation. Best practices recommend resolving synthetic keys by renaming fields or using a link table for proper association.

• Option C: Split large datasets into multiple smaller QVD files for more efficient storage
  This is a highly effective strategy. By breaking a large dataset into smaller, manageable QVDs (for instance, split by year or region), developers can load only the necessary subsets of data into memory at any given time. This minimizes memory usage, reduces reload time, and allows for incremental loading. QVD files are also optimized for rapid loading compared to querying data directly from source systems.

• Option D: Use the "AutoNumber" function to reduce memory usage
  This is another valid optimization technique. The "AutoNumber" function replaces frequently repeated textual keys (such as customer names or product IDs) with integer keys, which consume less memory. This is especially beneficial for large associative datasets, where string values appear repeatedly. Reducing memory usage in this way can have a significant impact on overall performance.

• Option E: Perform data transformations directly in the QlikView frontend
  This is considered poor practice. Transforming data in the frontend (such as through calculated dimensions or expressions in charts) adds processing overhead every time a user interacts with the application. Complex frontend logic slows down performance, especially with large datasets. Instead, transformations should be done in the script during data load, which is a one-time cost rather than a recurring burden during user interaction.

In conclusion, C. splitting large datasets into multiple QVDs and D. using the AutoNumber function are both effective and recommended techniques for optimizing QlikView documents when working with large datasets. These methods enhance performance, reduce memory consumption, and contribute to a more scalable and responsive analytical environment.

Question 5

Which two QlikView scripting functions are especially useful for decreasing data load time and optimizing the efficiency of processing data? (Choose 2.)

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

Correct Answer : A and D

Explanation:

Optimizing data load time and enhancing the efficiency of data processing in QlikView is crucial for building performant dashboards and ensuring a smooth user experience. Among the functions listed, ApplyMap() and Resident Load are particularly beneficial when trying to reduce overhead and streamline the data model during scripting.

ApplyMap() is one of the most powerful and efficient functions in QlikView for performing lookups or mapping values from one table into another. This function is often used instead of more resource-intensive joins. By replacing a join with ApplyMap(), the script executes faster because ApplyMap() does not create intermediate tables or temporary joins. It directly looks up and substitutes values during the load, effectively functioning like a VLOOKUP in Excel but optimized for performance in QlikView. This reduction in join complexity can lead to faster reload times and simpler data models, especially when working with dimension lookups such as mapping customer names to IDs or translating codes to descriptions.

Resident Load, on the other hand, allows developers to reuse already loaded data within the script. Instead of querying an external data source again, QlikView uses the in-memory table (resident table) that has already been loaded in a previous step. This has two primary advantages: first, it reduces the load time since external data sources are not re-queried; second, it enables incremental transformation, filtering, and aggregation of data already in memory. This staged approach to data transformation is more efficient than executing all logic in a single, complex load statement directly from the source.

Now, consider why the other options are less ideal for this particular question:

B. Peek() – While Peek() is a useful function for retrieving the value of a specific field from a particular row of a previously loaded table, it is primarily used for row-wise operations or referencing previously loaded values during data transformations. It does not directly improve load time or general processing efficiency for large datasets.

C. Join() – Join is a common operation in data transformation, but it is generally more resource-intensive than ApplyMap(). Joins increase load times due to the necessity of comparing and merging datasets. They can also result in data bloat or performance issues if not handled carefully, especially with large datasets or poorly matched keys. In performance-focused scripting, joins are often avoided in favor of more efficient alternatives like ApplyMap().

E. Concatenate() – Concatenate() is used to append records from one table to another, often to unify similar data structures. While useful for combining data, it doesn't directly contribute to reduced load time or processing efficiency unless you’re specifically avoiding synthetic keys or optimizing the data model structure. In most cases, it’s more of a modeling tool than a performance-enhancing one.

In conclusion, the two scripting functions that offer clear benefits in reducing load time and improving data processing efficiency are ApplyMap(), due to its fast lookup capabilities, and Resident Load, for its ability to reuse and transform in-memory data efficiently. Therefore, the correct answers are A and D.

Question 6

Which two statements about QlikView’s "Set Analysis" are correct? (Choose 2.)

A. Set Analysis allows for creating dynamic expressions that ignore selections
B. Set Analysis can only be used in charts, not in the script
C. It enables filtering data on specific dimensions or measures in an expression
D. Set Analysis requires the use of variables to work
E. It allows multiple fields to be analyzed simultaneously

Correct answers: A and C

Explanation:

Set Analysis is one of QlikView's most powerful features for advanced data filtering within expressions. It allows developers and analysts to override or refine user selections, thereby creating more flexible and insightful visualizations. Understanding what Set Analysis can and cannot do is key to using QlikView effectively.

Let’s evaluate each option carefully.

A. Set Analysis allows for creating dynamic expressions that ignore selections
This is true. One of the main strengths of Set Analysis is that it allows you to define expressions that override current selections in the app. For example, if you want to show sales for 2022 regardless of what year the user has selected, you can use Set Analysis like this:
Sum({<Year={2022}>} Sales)
This means that Set Analysis can be used to ignore, include, or exclude specific selections, making your expressions more dynamic and context-sensitive. Therefore, option A is correct.

B. Set Analysis can only be used in charts, not in the script
This is true, but whether it makes the statement correct depends on interpretation. In QlikView, Set Analysis is only used in the front-end, meaning within charts, text objects, KPIs, and other visual elements. It cannot be used in the data load script—the scripting environment relies on standard QlikView syntax, not Set Analysis.
While this statement is technically accurate, in terms of selecting the best answers, it’s less relevant than others that describe Set Analysis functionality more directly. Therefore, this is not one of the top two best answers, though it is factually true.

C. It enables filtering data on specific dimensions or measures in an expression
Yes, this is one of the core features of Set Analysis. For example, if you want to look at sales data for a particular region or product category, you can specify that condition inside the expression like:
Sum({<Region={'North America'}>} Sales)
This type of expression filters the data based on specific dimensions (like Region, Product, Year), making the chart or visualization reflect a targeted data subset. Therefore, option C is correct.

D. Set Analysis requires the use of variables to work
This is incorrect. Set Analysis does not require variables. While it’s possible to use variables within Set Analysis to make expressions more dynamic, they are not required. Many Set Analysis expressions are written without any variables at all. Thus, this option misrepresents how Set Analysis works.

E. It allows multiple fields to be analyzed simultaneously
This statement is partially correct, but misleading. Set Analysis can filter on multiple fields at once within a single expression. For example:
Sum({<Region={'EMEA'}, Year={2023}>} Sales)
However, the phrase “analyzed simultaneously” is vague and might be misunderstood. The primary purpose of Set Analysis is to filter data, not analyze fields directly. So while it supports filtering across multiple fields, the wording in this option is unclear and not the best answer. Therefore, this is not selected.

Set Analysis empowers users to create powerful, customized expressions that can override selections and apply filters based on defined criteria. It is limited to the front-end (not the script), but it does not require variables or imply analysis in a statistical sense.

Correct answers: A and C

Question 7

Which two of the following functions can be used to handle null values in QlikView? (Choose 2.)

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

Correct Answer: A and B

Explanation
In QlikView, null values are often encountered when dealing with incomplete data sets, and properly handling these nulls is crucial for accurate calculations and visualizations. Among the options provided, the If() and IsNull() functions are specifically designed for managing and reacting to null values during both scripting and expression building.

Let's go through each option:

• Option A: If()
  The If() function is a versatile tool used in QlikView to create conditional logic. It can be used to evaluate whether a field contains a null value and apply alternate logic accordingly. For example, If(IsNull(Sales), 0, Sales) replaces null sales values with 0. This is a direct and effective way to handle nulls in front-end expressions or during the data load process.

• Option B: IsNull()
  The IsNull() function explicitly checks whether a given field or expression is null. It returns true if the value is null and false otherwise. This function is often used in combination with If() or other logical structures to replace or manage null values. For example, you can write: If(IsNull(Customer), 'Unknown', Customer) to handle missing customer names.

• Option C: NullAsValue()
  NullAsValue is not a function but a script statement in QlikView. It is used in the load script to convert nulls into actual field values so that they can be selected or manipulated in the QlikView interface. While it does help handle nulls, it is not a function that can be used within expressions or calculations, which disqualifies it in the context of this question asking for functions.

• Option D: Coalesce()
  Coalesce() is a common SQL function used to return the first non-null value among its arguments, but it is not available in QlikView. While QlikView offers similar functionality through nested If statements or pick/match constructs, Coalesce is not a recognized function in QlikView's expression language.

• Option E: Len()
  Len() returns the length of a string. While it can indirectly indicate a null or empty string (e.g., Len(Field) = 0 could mean the field is blank), it does not specifically handle nulls. Moreover, Len(null()) does not return meaningful results since nulls are not strings. Thus, this function is not primarily used for null handling.

In summary, A. If() and B. IsNull() are the two valid functions in QlikView used to directly identify and manage null values, allowing developers to ensure cleaner data presentation and avoid logic errors in reports and visualizations.

Question 8

Which two of the following components are part of the QlikView architecture? (Select two options.)

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

Correct Answer : A and C

Explanation:

The QlikView architecture is composed of multiple interconnected components that work together to deliver data visualization, analysis, and business intelligence capabilities. Two of the most critical components within this architecture are QlikView Server and QlikView Publisher. These components play central roles in data access, security, distribution, and performance optimization in enterprise environments.

Let’s look at each correct answer in detail:

QlikView Server (QVS) is a core component of the QlikView architecture. It is responsible for handling client communication, managing sessions, loading documents into memory, and ensuring secure and efficient data access across users. QlikView Server enables users to interact with QlikView documents via web browsers or QlikView clients, and it ensures that all user interactions are handled in a scalable and secure way. Without the QVS, QlikView dashboards would not be accessible in a multi-user, server-based environment.

QlikView Publisher is another integral component. It is primarily used for data reload automation, document distribution, and access control. Publisher allows developers and administrators to schedule data reloads, apply data reduction (using Section Access), and distribute QlikView documents to different users or groups based on their access rights. This makes QlikView Publisher especially valuable in large-scale deployments where automated and secure distribution of updated data models is necessary.

Now, let’s examine why the other options are not included in the core QlikView architecture (as it is typically defined):

B. QlikView Desktop – While QlikView Desktop is an essential tool for creating and designing QlikView applications, it is not considered a component of the architecture per se. It is more of a development environment used by developers and analysts to build .qvw files, which are later published to the QlikView Server for broader access. In the strictest architectural sense, it’s more of a client tool than an architectural component.

D. QlikView Management Console (QMC) – The QMC is a web-based interface used to manage QlikView Server and Publisher settings, tasks, and access controls. However, it is a management tool, not a core architectural component on its own. It facilitates the configuration and administration of the architecture but is not itself a structural element of the platform.

E. QlikView Analytics Platform – This refers to a broader solution concept or suite and is more commonly associated with Qlik Sense, the newer Qlik product. QlikView does not have a formally branded “Analytics Platform” component. This terminology is somewhat ambiguous and does not refer to a discrete or installable component within the QlikView architecture.

In conclusion, the two most accurate and foundational components of the QlikView architecture are QlikView Server (A), which powers the distribution and user access to dashboards, and QlikView Publisher (C), which manages data reloads and document distribution. Therefore, the correct answers are A and C.

Question 9

Which two types of QlikView objects allow for better visualization and representation of data in dashboards? (Choose 2.)

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

Correct answers: A and C

Explanation:

QlikView is a data visualization and business intelligence tool that offers various object types to present and interact with data. Some objects are designed purely for data handling or storage (like QVDs), while others enhance the visual representation and user interaction on a dashboard. In this question, we’re looking for objects that contribute directly to better visualization and data representation, which typically means chart-based or table-based objects used in the front-end user interface.

Let’s analyze each option:

A. Pivot tables
Pivot tables are one of the most powerful visualization tools in QlikView. They allow you to summarize and reorganize data dynamically by moving fields between rows and columns. With a pivot table, users can drill down into data hierarchies, aggregate values, and display measures clearly. Their interactive nature and the ability to rearrange dimensions and metrics make them ideal for dashboards. Therefore, pivot tables directly enhance visualization and are a correct answer.

B. QVD tables
QVD (QlikView Data) files are not visualization objects. They are data storage formats used in QlikView to load and store data efficiently. QVDs are extremely useful for performance optimization and incremental loading but are not used on the dashboard for presenting or visualizing data. As such, they do not help with data visualization or representation in dashboards and are therefore not a correct answer.

C. Scatter charts
Scatter charts are a type of data visualization that displays values for typically two or three variables. They are useful in dashboards for identifying correlations, distributions, and outliers. For example, sales vs. profit across different regions can be visualized to detect patterns. QlikView supports interactive scatter plots, where data points can be selected and analyzed. Because scatter charts directly contribute to insightful visual representation, this is a correct answer.

D. Expression labels
Expression labels are simply labels for expressions within charts or tables. While they help clarify what a given expression represents, they are not objects used for visualization themselves. They are metadata that improve readability but do not provide any visual representation of data. Therefore, this is not a correct answer.

E. List boxes
List boxes are a type of selection object used for filtering data based on field values. While list boxes are very useful for navigating and interacting with data, they are not visualization tools in the traditional sense. They do not show aggregated or calculated data like charts or tables do. Their primary purpose is to allow users to make selections rather than to visualize data trends or patterns. Therefore, this is not a correct answer for a question focused on visualization and representation.

Conclusion:
The two best options that align with the purpose of visualizing and representing data in a QlikView dashboard are pivot tables and scatter charts. These objects help users gain insights by displaying data in structured and graphical formats. The other options, while useful in their own right, are either not visualization tools (QVDs, expression labels) or are focused on selection rather than representation (list boxes).

Correct answers: A and C

Question 10

Which two of the following techniques are used to create a strong data model in QlikView? (Choose 2.)

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

Correct Answer: A and E

Explanation
Creating a strong and efficient data model in QlikView is one of the most important aspects of building a responsive, scalable, and logically accurate dashboard or application. The structure of the data model not only affects performance but also determines how accurately QlikView interprets relationships among tables.

Let’s analyze each option to understand which techniques are valid:

• Option A: Avoid circular references and synthetic keys
  This is a core principle of good QlikView data modeling. Circular references occur when there are multiple paths between two tables, leading to ambiguity in data relationships. Synthetic keys are automatically generated by QlikView when two or more tables share more than one common field. Both scenarios can result in incorrect associations, performance issues, and difficult-to-debug logic errors. To build a clean, strong model, you should manually manage keys and relationships, avoiding these automatic constructs by renaming fields or using link tables.

• Option B: Join all tables directly to the fact table
  This approach is not ideal. Joining every table to a fact table can flatten the data model unnecessarily and may create a wide, complex structure that's harder to manage. It can also lead to performance issues, incorrect aggregations, and field naming conflicts. Instead, it's often better to use a star schema, where dimension tables connect directly to the fact table, maintaining clarity and efficiency.

• Option C: Use concatenation to merge tables with common fields
  Concatenation is typically used to stack similar tables (e.g., sales data from different years or regions), not to merge tables that share common fields like keys. Merging unrelated tables just because they share field names can introduce synthetic keys or incorrect relationships. Therefore, while concatenation has its place, it’s not a general-purpose method for improving the strength of a data model.

• Option D: Use direct connections to the database for faster data loads
  Direct connections to databases may simplify data access, but they do not inherently strengthen the data model. In fact, loading large datasets directly from databases without proper optimization can slow down performance. QlikView is designed to work efficiently with pre-processed data (e.g., from QVD files), not necessarily with direct queries. Thus, this is not a modeling best practice.

• Option E: Build a star schema with fact and dimension tables
  This is a best practice in QlikView modeling. A star schema is a logical design where a central fact table (containing measurable data like sales, revenue, etc.) is connected to surrounding dimension tables (like product, customer, time). This structure simplifies associations, improves query performance, and reduces redundancy. It also mirrors common BI modeling approaches, making it easier for users to understand and navigate the data.

In summary, the two most important techniques for building a strong QlikView data model are avoiding circular references and synthetic keys (A) and using a star schema (E). These practices help create a clean, logical, and efficient data structure that supports robust analysis and fast performance.