Question 1

A table is generated resulting from the following script: https://kxbjsyuhceggsyvxdkof.supabase.co/storage/v1/object/public/file-images/QLIK_QSDA2024/page_38_img_1.jpg When the data architect selects a date, some, but NOT all, orders for that date are shown. How should the data architect modify the script to show all orders for the selected date? A) https://kxbjsyuhceggsyvxdkof.supabase.co/storage/v1/object/public/file-images/QLIK_QSDA2024/page_38_img_2.jpg B) https://kxbjsyuhceggsyvxdkof.supabase.co/storage/v1/object/public/file-images/QLIK_QSDA2024/page_38_img_3.jpg C) https://kxbjsyuhceggsyvxdkof.supabase.co/storage/v1/object/public/file-images/QLIK_QSDA2024/page_39_img_1.jpg D) https://kxbjsyuhceggsyvxdkof.supabase.co/storage/v1/object/public/file-images/QLIK_QSDA2024/page_39_img_2.jpg

Accepted Answer

D

Explanation: The issue described, where selecting a date shows some but not all orders, is a classic symptom of a timestamp mismatch. The OrderDate field in the Orders table likely contains both date and time information (a timestamp), while the Calendar table contains only the date. The association fails because '2024-07-26 09:15:00' does not equal '2024-07-26'. Option D correctly resolves this by creating a LinkTable. It uses the Date(Floor(OrderDate)) functions to strip the time component from the OrderDate field, creating a clean date key. This LinkTable then associates the Calendar (via the clean OrderDate) with the Orders table (via OrderID). This is the best practice as it resolves the association issue without destructively altering the original OrderDate timestamp in the Orders table, preserving data granularity for other potential analyses.

Question 2

Exhibit. https://kxbjsyuhceggsyvxdkof.supabase.co/storage/v1/object/public/file-images/QLIK_QSDA2024/page_17_img_1.jpg Refer to the exhibit. A data architect is working on a Qlik Sense app the business has created to analyze the company orders and shipments. To understand the table structure, the business has given the following summary: • Every order creates a unique orderlD and an order date in the Orders table • An order can contain one or more order lines one for each product ID in the order details table • Products In the order are shipped (shipment date) as soon as they are ready and can be shipped separately • The dates need to be analyzed separately by Year, Month, and Quarter The data architect realizes the data model has issues that must be fixed. Which steps should the data architect perform?

Accepted Answer

C

Explanation: The provided data model exhibits two critical issues: a circular reference and a canonical date problem. 1. Circular Reference: The tables Orders, OrderDetails, and Shipments form a loop, which can lead to ambiguity and performance issues. The correct approach is to break this loop by creating a composite key (from OrderID and ProductID) to link OrderDetails and Shipments. Additionally, the ShipmentID in the Orders table is at the wrong level of granularity and contributes to the loop; it must be removed. 2. Canonical Date: The model has two date fields (OrderDate, ShipmentDate) linked to a single MasterCalendar. This is a canonical date problem, where selections on the calendar ambiguously filter both types of dates. The best-practice solution is to create a date link table (a canonical calendar), which connects a single master calendar to the multiple date fields in the fact tables. Option C correctly prescribes creating a composite key to resolve the loop and implementing a link table to solve the canonical date problem, representing the most robust architectural solution.

Question 3

Exhibit. https://kxbjsyuhceggsyvxdkof.supabase.co/storage/v1/object/public/file-images/QLIK_QSDA2024/page_25_img_1.jpg A chart for monthly hospital admissions and discharges incorrectly displays the month and year values on the x-axis. The date format for the source data field "Common Date" is M/D/YYYY. This format was used in a calculated field named "Month-Year" in the data manager when the data model was first built. Which expression should the data architect use to fix this issue?

Accepted Answer

A

Explanation: To correctly display a "Month-Year" value on the axis, the data must first be grouped by month. The MonthStart([Common Date]) function achieves this by converting every date within a given month to the first day of that month (e.g., '1/15/2022' and '1/28/2022' both become '1/1/2022'). The outer Date() function then takes this standardized date and applies the 'MMM-YYYY' format string, resulting in a correctly formatted and grouped dimension value like 'Jan-2022'.

Question 4

A Chief Information Officer has hired Qlik to enhance the organization's inventory analytics. In the
initial meeting, the client's focus was determined to be forecasting inventory levels.
Which stakeholder should be consulted first when gathering requirements?

Accepted Answer

A

Explanation: 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.

Question 5

Exhibit. https://kxbjsyuhceggsyvxdkof.supabase.co/storage/v1/object/public/file-images/QLIK_QSDA2024/page_80_img_1.jpg Refer to the exhibit. A data architect wants to transform the input data set to the output data set. Which prefix to the Qlik Sense LOAD command should the data architect use?

Accepted Answer

C

Explanation: 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.

Question 6

A data architect implements Section Access on an app to reduce the data for each user when the user
logs in. Each user is allowed to see their specific territory only.
The app is set for a scheduled reload every three hours. Without Section Access added, the app loads
successfully. When Section Access is added and the script runs, the app fails to load.
What is causing this issue?

Accepted Answer

B

Explanation: 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.

Question 7

A company generates l GB of ticketing data daily. The data is stored in multiple tables. Business users
need to see trends of tickets processed for the past 2 years. Users very rarely access the transaction-
level data for a specific date. Only the past 2 years of data must be loaded, which is 720 GB of data.
Which method should a data architect use to meet these requirements?

Accepted Answer

C

Explanation: The scenario describes a classic big data challenge where the full dataset (720 GB) is too large for efficient in-memory analysis. The user requirements are split: frequent analysis of high-level trends (aggregated data) and rare access to transaction-level details. On-Demand App Generation (ODAG) is the Qlik Sense feature specifically engineered for this purpose. It uses a small, fast summary app with pre-aggregated data for trend analysis. When a user needs to investigate details, they make selections in the summary app, and ODAG dynamically generates a new app containing only the relevant slice of transactional data. This provides optimal performance for the primary use case while still enabling deep-dive analysis when required.

Question 8

Exhibit. https://kxbjsyuhceggsyvxdkof.supabase.co/storage/v1/object/public/file-images/QLIK_QSDA2024/page_47_img_1.jpg Refer to the exhibit. A major healthcare organization requests a new app with the following requirements: • Users can filter AdmissionDate and DischargeDate by all fields in the Master Calendar table • Use an existing QVD file, which includes dates 20 years into the future • Users should not be able to filter on dates that have no associated encounters Which approach should the data architect take to meet these requirements?

Accepted Answer

C

Explanation: The scenario requires linking two date fields (AdmissionDate, DischargeDate) from a single fact table to a master calendar. This is a classic "role-playing dimension" problem. The correct and standard Qlik architecture for this is the canonical calendar pattern. This involves loading the calendar dimension table twice, creating two separate, unassociated tables in the data model. Each table's fields are aliased to correspond to one of the date roles (e.g., Date as AdmissionDate, Year as AdmissionYear in one table; Date as DischargeDate, Year as DischargeYear in the other). This creates the correct associative links to the fact table and allows for independent filtering and analysis of each date, which directly meets the primary requirement.

Question 9

A data architect needs to acquire social media data for the past 10 years. The data architect needs to
track all changes made to the source data, include all relevant fields, and reload the application four
times a day.
What information does the data architect need?

Accepted Answer

D

Explanation: To implement a robust incremental load strategy that tracks all changes, a data architect requires specific information and capabilities. A ModificationTime field is essential to efficiently identify which records have been created or updated since the last data load. A primary key field is non-negotiable, as it provides a unique identifier for each record, enabling the script to correctly apply updates to existing records and identify which records to remove. Finally, the overall process must account for all three types of data modifications: inserting new records, updating changed records, and removing records that have been deleted from the source system to ensure the Qlik application remains a true reflection of the source.

Question 10

A startup company is about have its Initial Public Offering (IPO) on the New York Stock Exchange.
This startup company has used Qlik Sense for many years for data-based decision making for Sales
and Marketing efforts, as well as for input into Financial Reporting. The startup's Qlik Sense
applications use variables that have different values at different points in time.
Due to the increased rigor required in record keeping for public companies, these variables must be
clearly recorded in the script reload logs of the Qlik Sense applications. These logs are refreshed
daily.
The data architect wants to have the variables names, with their current values, written into the
script reload logs. Which script statement should the data architect use?

Accepted Answer

B

Explanation: The Trace statement is the correct command for this requirement. Its specific purpose is to write a user-defined string to the script execution progress window and, more importantly, into the script log file. This allows the data architect to output the names and evaluated values of variables during the script reload process. For example, using Trace 'Variable vSalesDate is $(vSalesDate)'; will record the variable's name and its current value in the log, satisfying the need for auditable records.

Free Qlik QSDA2024 Actual Exam Questions