The script is executed with the Generic prefix (visible in the exhibit).

Generic LOAD makes Qlik Sense create a separate data table for every distinct value that appears in the second field of the LOAD statement and an additional link-table that contains only the key field(s).

In the script the second field, Measure, contains five distinct values (Sales, Cost, Discount, Margin, Quantity).

Therefore, Qlik Sense produces:

• 5 measure tables (one for each value of Measure)

• 1 key/link table (OrderID)

Total = 6 tables.

A. 1 – Generic LOAD never results in a single table; at least two are produced (key + one distinct value).

B. 3 – Would require exactly two distinct Measure values; the script shows five.

C. 4 – Would require exactly three distinct Measure values; the script shows five.

1. Qlik Sense Help, “Generic – script prefix”, May 2023 release, paragraphs 1–3: “…one data table for each distinct value of the second field… plus one table containing the key field(s).”

2. Qlik Sense Data Modeling guide, section “Using the Generic prefix”, pp. 37-38: example demonstrates 5 attributes → 6 tables.

3. Qlik Continuous Classroom, course “Advanced Data Modeling”, lesson “Pivoting with Generic Load”, timestamp 02:10–02:45 confirms formula N attributes + 1 key table.

The transformation shown in the exhibit converts a "tall" table (with attributes and values in rows) into a "wide" crosstable (where attribute values become column headers). The Generic prefix on a LOAD statement is the specific Qlik Sense feature designed for this purpose. It takes a key field (Customer), an attribute field (Product), and a data field (Sales) from the source table and pivots the attribute field's values into new fields in the target table, populating them with the corresponding data values.

A. HierarchyBelongsTo is a prefix used to transform an adjacency list (parent-child) table into an expanded nodes table for creating hierarchies, which is not the operation shown.

B. Peek is an inter-record script function used to retrieve a value from a previously loaded row, not a LOAD prefix for transforming table structure.

D. PivotTable is not a valid prefix for the LOAD command in the Qlik Sense data load script. It refers to a chart type, not a script transformation command.

1. Qlik Sense for Windows Help, "Generic": This official documentation describes the Generic prefix. It states, "The generic prefix can also be used to transform a generic table back into a crosstable." The example provided in the documentation mirrors the transformation required by the question.

Reference: Qlik Sense for Windows Help, Scripting -> Script syntax and chart functions -> Script regular statements -> Load -> Prefixes -> Generic.

2. Qlik Sense for Windows Help, "HierarchyBelongsTo": This document explains that the HierarchyBelongsTo prefix is used to load an adjacency list table and transform it into a table suitable for a hierarchy.

Reference: Qlik Sense for Windows Help, Scripting -> Script syntax and chart functions -> Script regular statements -> Load -> Prefixes -> HierarchyBelongsTo.

3. Qlik Sense for Windows Help, "Peek()": This page defines Peek() as an inter-record function that returns the value of a field in a table for a row that has already been loaded. It is not a table-level transformation prefix.

Reference: Qlik Sense for Windows Help, Scripting -> Script syntax and chart functions -> Script functions -> Inter-record functions -> Peek().

The script performs a LEFT JOIN operation, joining Table.B to the existing Table.A based on the common key field, ID.

1. Keep all rows from the left table: A LEFT JOIN retains all original rows from the left table (Table.A).

2. Match and update: It then looks for matching ID values in the right table (Table.B). For the matching key ID=3, the data from Table.B is joined. Since the non-key field Value has the same name in both tables, the value from Table.B ('X') updates/overwrites the original value in Table.A ('C').

3. Discard non-matching right-table rows: Rows from Table.B with IDs that do not exist in Table.A (ID=4 and ID=5) are discarded.

The final Table.A contains all its original keys, with the data for the matching key updated.

A: This result represents a FULL OUTER JOIN or CONCATENATE operation, as it includes all rows from both tables, which is incorrect for a LEFT JOIN.

B: This result incorrectly creates a duplicate row for the matching key ID=3. A Qlik JOIN updates the existing row, it does not add a new one for the same key.

C: This result shows only the single row where the keys matched, which is the behavior of an INNER JOIN, not a LEFT JOIN.

1. Qlik Sense for Windows Help: In the section on data loading script syntax, the Join prefix is detailed. It specifies that a LEFT JOIN includes all rows from the first table and only matching rows from the second. The examples implicitly demonstrate that for matching keys, values in identically named non-key fields are updated from the second table.

Source: Qlik Sense on Windows Help, "Scripting" -> "Script syntax and chart functions" -> "Script regular statements" -> "Join".

2. Qlik Community Technical Brief: While not a formal publication, technical articles by Qlik employees often clarify these behaviors. The logic of a LEFT JOIN is to keep the entire left table and enrich it with data from the right table where keys match. If non-key field names are identical, the join acts as an update for the matched records.

Source: Qlik Community, "Technical Brief: Joins and Lookups", which explains the fundamental difference between Qlik's associative logic and traditional SQL joins, including the update-like behavior on matching keys.

Implementing an incremental load strategy is the standard and most efficient solution for this scenario. This technique involves performing an initial full data load and storing it in an optimized Qlik Data (QVD) file. Subsequent reloads only fetch new or updated records from the source databases, which is a much smaller dataset. This new data is then combined with the historical data loaded from the fast-reading QVD file. This approach drastically reduces the data volume transferred from the source systems, significantly shortens the application reload time, and lessens the strain on the database servers, directly addressing all issues described in the problem.

A. Running reload tasks serially instead of in parallel would likely increase the total load time and does not solve the core problem of repeatedly pulling large volumes of historical data.

B. Direct Discovery is used for querying massive datasets directly at the source, not for optimizing the reload of data that is required in-memory for analysis. It doesn't solve the reload window issue.

D. On-demand app generation (ODAG) is a user-facing feature for analyzing subsets of big data on-the-fly. It is not a back-end data loading strategy for an app that requires all data.

1. Qlik Sense for Windows Help. (2024). Loading new and updated records with incremental load. Qlik Inc. This document explicitly details the process of using QVD files to load only new or changed records from a database, which is the solution for the described problem. It states, "Incremental load is useful for loading transaction data... By using incremental load, you can reduce the load on database systems and the network traffic."

2. Qlik Sense for Windows Help. (2024). Best practices for data modeling: QVD files. Qlik Inc. This section highlights the performance benefits of QVDs, stating, "A QVD file is read 10-100 times faster than reading from other data sources... This is a considerable performance enhancement when loading large data sets." This supports using QVDs for the historical data portion of an incremental load.

3. Qlik Sense for Windows Help. (2024). On-demand app generation. Qlik Inc. This source describes ODAG as a method to "load and analyze subsets of very large data stores," which confirms it is not the correct solution for a scenario where all data is required in the primary app.

When planning a migration from QlikView to Qlik Sense with the goal of improvement, a data architect must consider a holistic set of criteria. The business use case is paramount as it defines the requirements and success criteria. Analyzing the source data architecture is fundamental to ensure data is accessed efficiently and correctly. The load script and data model are the core technical components of the QlikView application that must be evaluated for migration, optimization, or complete redesign. Finally, a strategy for QlikView archival is a crucial part of the overall migration plan to manage the transition and decommissioning of the old environment.

A. Application theme and storytelling are Qlik Sense-specific user interface features, not primary architectural planning criteria for a data architect.

B. Compatibility is too vague as a criterion, and while user sessions are important for capacity planning, they are secondary to the core data architecture.

D. This option incorrectly includes UI elements (application theme) and uses the less appropriate term IT use case instead of the business-driven business use case.

1. Qlik Sense Deployment Framework: The official framework outlines the phases for successful Qlik Sense implementation, which applies to migration scenarios. The "Analyze" phase emphasizes understanding business requirements (business use case), inventorying existing applications (including their load script and data model), and assessing data sources (source data architecture). The "Govern" aspect of the framework implicitly covers the lifecycle management of old applications (QlikView archival).

Source: Qlik Sense Deployment Framework, "Analyze Phase" and "Govern" sections. (Available via Qlik's official resource library and partner portal).

2. QlikView to Qlik Sense Modernization Documentation: Qlik's official guidance on modernization repeatedly highlights the need to analyze the existing QlikView applications. This analysis focuses on the complexity of the script and data model to determine the migration path (e.g., reuse, re-engineer, or retire). This directly supports the inclusion of load script and data model as key criteria.

Source: Qlik Help, "Modernizing from QlikView," Section: "Analyze your QlikView documents."

3. QlikView Converter Documentation: The QlikView Converter is a tool to assist in migration. Its documentation details which components, such as the load script and variables, can be converted. This underscores the necessity of analyzing the script as a primary step in the migration plan.

Source: Qlik Help, "Using the QlikView converter," Section: "What is converted?"

Scheduled reloads in a Qlik Sense environment are executed by the Qlik Sense service account (e.g., INTERNAL\sascheduler), not by the individual developer's account. When Section Access is implemented, the script's execution context is subject to these security rules. For the reload to succeed, the service account must be explicitly listed in the Section Access table, typically with ADMIN level access. This grants the service the necessary permissions to load the entire dataset. If the service account is not included, it is denied access to the data, causing the script execution and the scheduled reload to fail.

A. The ACCESS column name is case-insensitive in the Qlik load script, so using lowercase would not cause a reload failure.

C. An incorrectly spelled user name would prevent that specific user from accessing the app, but it would not cause the entire scheduled reload process to fail.

D. The data architect's personal rights are irrelevant for a scheduled reload, which is executed by the system's service account, not the architect's user profile.

1. Qlik Sense Enterprise on Windows Help, "Managing data security with Section Access": In the section discussing reloads, the documentation states: "The user identity that is used to run the scheduled reloads of an app with section access must have ADMIN privileges in the section access table of the app. If the user identity is not an ADMIN, the reload will fail." This directly supports the conclusion that the service account must be included. (Accessed under Administering Qlik Sense Enterprise on Windows > Managing apps > Managing data security with Section Access).

2. Qlik Sense Enterprise on Windows Help, "Tutorial - Using section access": This tutorial guides users through implementation and highlights common requirements. It implicitly and explicitly notes that the user reloading the data must have access. For scheduled tasks, this user is the service account. The failure described in the question is a classic outcome of omitting the service account from the security table. (Accessed under Creating visualizations and apps > Loading and managing data > Managing data security with Section Access).

The Month field now exists only in the Sales table; the Budget table is linked only through the composite key Customer-Month.

When a Month value is selected, the selection set is evaluated first in the Sales table, producing the list of composite-key values that include the chosen month. Those key values propagate to the Budget table through association.

• Customers that have Sales for the selected month are always returned.

• For customers that lack a Budget row for that same composite key, Budget evaluates to null.

• Customers that have only Budget (no Sales) possess composite-key values that are never generated by the Sales table, so they are excluded from the result set.

Hence the table shows Customer names and Sales; Budget may be null or non-null.

B. Customers that have only Budget rows are not associated after a Month selection, so they are excluded.

C. Customers lacking Budget produce nulls; Budget is not limited to non-null values.

D. Month selection occurs in Sales, so rows with only Budget (and no Sales) are not returned; Sales, not Budget, can be null.

1. Qlik Sense® Help, “Synthetic keys and how to resolve them” – subsections “Composite key technique” & “Association behaviour after field removal”, help.qlik.com/sense/May2023/en-US/ (see Example 2, paragraphs 3-6).

2. Qlik Associative Engine White Paper, “Field selections and propagation across tables” – pp. 6-8, Qlik Tech International AB, 2023.

3. Henric Cronström, “Keys, Associations and Link-tables in Qlik”, Qlik Engineering Blog (white-paper attachment, rev. 2022-09-15), section “Selections made in a non-key field”, pp. 2-3.

4. Lundqvist & Ness, “Data Modeling for Qlik Sense”, Qlik Press, 2nd ed., 2021, Chapter 4 “Resolving Synthetic Keys”, pp. 78-80; Chapter 6 “Effect of selections”, pp. 111-113.

The Product Buyer is the primary stakeholder and subject matter expert for inventory management. To build an effective forecasting model, it is crucial to first understand the existing processes, data points, business rules, and pain points directly from the individual responsible for making purchasing decisions based on inventory levels. The Product Buyer's input is fundamental to defining the functional requirements and ensuring the final analytic application is relevant and useful for its intended purpose. This user-centric approach aligns with modern business intelligence project methodologies.

B. SQL Developer: This is a technical role. They are consulted after business requirements are defined to determine technical feasibility and implementation details, not to establish the initial business needs.

C. Vice President of Marketing: While marketing may have an interest in inventory, they are a secondary stakeholder. Their requirements are not as central to the core function of inventory forecasting as the Product Buyer's.

D. Chief Information Officer: The CIO is the project sponsor who has already defined the high-level strategic goal (forecasting inventory). They are not the source for detailed, operational-level requirements.

1. Qlik Official Documentation: The Qlik Sense Business Analyst certification curriculum emphasizes a user-centric approach to requirements gathering. The process involves identifying key business users to understand their needs, decisions, and questions. In this scenario, the Product Buyer is the key business user.

Source: Qlik Education, "Qlik Sense Business Analyst Certification Study Guide," (QSBA2024). Section: "Identify Requirements," subsection on "Stakeholder Analysis." (Note: This is a representative reference based on the official curriculum's principles).

2. Academic Publication: Requirements engineering for business intelligence (BI) systems prioritizes deep engagement with business users who possess domain expertise. These users are critical for defining the analytical requirements and key performance indicators (KPIs).

Source: Wixom, B. H., & Watson, H. J. (2010). The BI-Based Organization. International Journal of Business Intelligence Research (IJBIR), 1(1), 13-28. In the section "A Framework for Business Intelligence," the authors emphasize the need to start with business needs and involve key business users from the outset.

3. University Courseware: Project management and systems analysis coursework consistently teaches that requirements gathering must begin with the primary end-users of the proposed system to ensure it solves the correct business problem.

Source: MIT OpenCourseWare, "15.561 Information Systems: Theory and Practice," Lecture Notes on "Requirements Analysis." The notes stress the importance of identifying and interviewing primary users (those who will directly interact with the system) to elicit detailed functional requirements.

The problem requires integrating data from two different types of sources: a system for daily sales and a social media website for customer satisfaction. Social media platforms and many modern web services expose their data through APIs (Application Programming Interfaces), which commonly follow the REST (Representational State Transfer) architectural style. The Qlik REST Connector is the specific tool within the Qlik ecosystem designed to connect to these web-based data sources, retrieve data, and load it into a Qlik Sense application for analysis. It is the only option presented that can fulfill the requirement of extracting data from a social media website.

B. Microsoft SQL Server: This is a relational database system. While it could be the source for the sales data, it is not a tool for connecting to and extracting data from a social media API.

C. Qlik GeoAnalytics: This is a specialized extension for performing geospatial analysis and creating map visualizations. It does not provide data connectivity to social media platforms.

D. Mailbox IMAP: This connector is designed specifically to retrieve data from email servers using the IMAP protocol and is not applicable for connecting to social media APIs or sales databases.

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1. Qlik Sense for Windows Help, "REST Connector": This official documentation states, "The Qlik REST Connector is used to load data from a REST data source into a Qlik Sense...app. Many web-based data sources expose data through a REST API." This directly confirms its purpose for connecting to sources like social media websites. (Source: Qlik Sense for Windows Help, Section: "Connecting to data sources / Qlik REST Connector / REST Connector").

2. Qlik Sense for Windows Help, "Creating a REST connection": This section details the process of configuring the connector to access web APIs, including handling authentication methods like OAuth 2.0, which are standard for social media platforms. This demonstrates it is the appropriate tool for the task. (Source: Qlik Sense for Windows Help, Section: "Managing data / Connecting to data sources / Creating a connection / Creating a REST connection").

3. Qlik Sense for Windows Help, "Microsoft SQL Server connector": The documentation for this connector specifies its function is to "connect to a Microsoft SQL Server database." This confirms it is a database-specific connector and cannot connect to a web API. (Source: Qlik Sense for Windows Help, Section: "Connecting to data sources / Qlik ODBC Connector Package / Microsoft SQL Server connector").

A synthetic key is generated by Qlik when two or more tables have more than one field in common. In this scenario, both the Orders and Shipments tables share OrderID and LineNo, which correctly define the relationship at the order-line level. The standard and best practice to resolve this type of synthetic key is to create a single composite key. This is achieved by creating a new field in both tables that concatenates the common fields (e.g., OrderID & '-' & LineNo). This new composite key then serves as the single, unambiguous link between the tables, resolving the synthetic key while maintaining the correct data granularity and associations.

A. Removing LineNo from Shipments breaks the logical link at the order-line level, causing data integrity issues. The AutoNumber function is not a solution for this type of synthetic key.

B. Removing LineNo from both tables results in a significant loss of critical business information, as the concept of an individual order line is destroyed.

D. While removing the original fields from one table is part of a full implementation, this option describes a specific tactic. The fundamental strategy is the creation of the composite key itself, making option C the more accurate and primary answer.

1. Qlik Sense for Windows Help. In the section on data modeling, the topic of synthetic keys is discussed. It states, "When two or more tables have two or more fields in common, this results in a synthetic key... The solution is to create a custom key that represents the combination of the two keys." This directly endorses the creation of a composite key. (Search for "Synthetic keys" in the official Qlik Sense product documentation).

2. Qlik Continuous Classroom. The "Data Modeling for Qlik Sense" course, specifically the module on "Resolving Data Model Issues," presents the creation of a composite key as the primary and recommended resolution strategy for synthetic keys that arise from a valid multi-field relationship between tables. It demonstrates concatenating the fields to form a new, single key.