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Qlik QSDA2024 Exam Syllabus Topics:

Topic	Details
Topic 1	Validation: This section tests data analysts and data architects on how to validate and test scripts and data. It focuses on selecting the best methods for ensuring data accuracy and integrity in given scenarios.
Topic 2	Identify Requirements: This section assesses the abilities of data analysts in defining key business requirements. It includes tasks such as identifying stakeholders, selecting relevant metrics, and determining the level of granularity and aggregation needed.
Topic 3	Data Model Design: In this section, data analysts and data architects are tested on their ability to determine relevant measures and attributes from each data source.
Topic 4	Data Connectivity: This part evaluates how data analysts identify necessary data sources and connectors. It focuses on selecting the most appropriate methods for establishing connections to various data sources.
Topic 5	Data Transformations: This section examines the skills of data analysts and data architects in creating data content based on specific requirements. It also covers handling null and blank data and documenting Data Load scripts.

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Qlik Sense Data Architect Certification Exam - 2024 Sample Questions (Q14-Q19):

NEW QUESTION # 14
Exhibit.

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?

A. 1. Load the Encounters table
2. Perform a Left Join Load on the Encounters table to the master calendar and alias the date fields appropriately for the Admission Date
3. Perform a Left Join Load on the Encounters table to the master calendar and alias the date fields appropriately for the Discharge Date
B. 1. Load the master calendar as AdmissionCalendar and alias the fields to reflect they are for Admission
2. Load the master calendar as DischargeCalendar and alias the fields to reflect they are for Discharge
3. Load the Encounters table
C. 1. Load the Master Calendar and Encounters tables
2. Perform a Join Load on the Encounters table to the Resident master calendar and alias the date fields appropriately for the Admission Date
3. Perform a Join Load on the Encounters table to the Resident master calendar and alias the date fields appropriately for the Discharge Date
D. 1. Load the master calendar
2. Create two mapping tables called AdmissionCalendar and DischargeCalendar from the Resident master calendar thatfeas all fields appropriately named
3. Load the Encounters table and use ApplyMap for the AdmissionDate and DischargeDate appropriately

Answer: B

Explanation:
In the scenario presented, a major healthcare organization needs an app that allows users to filter AdmissionDate and DischargeDate by all fields in the Master Calendar table, while also ensuring that users cannot filter on dates that have no associated encounters.
To meet these requirements, the most appropriate approach is to:
* Load the Master Calendar twice,once as AdmissionCalendar and once as DischargeCalendar. Each instance should have its fields appropriately aliased to reflect whether they pertain to Admission or Discharge dates.
* Load the Encounters tableas usual, but now you have two separate calendar tables that can be linked to the appropriate date fields (AdmissionDate and DischargeDate) in the Encounters table.
This approach ensures:
* Users can filter both AdmissionDate and DischargeDateindependently using the fields in their respective calendar tables.
* Only relevant datesassociated with actual encounters will be available for filtering, as the calendars are linked specifically to the AdmissionDate and DischargeDate fields.
* Efficiency and clarityin the data model, as the fields from the Master Calendar are distinctly assigned to either Admission or Discharge, avoiding any confusion or incorrect filtering.
This method avoids unnecessary complexity and directly meets the healthcare organization's requirements in a straightforward and scalable manner.

NEW QUESTION # 15
Exhibit.

Refer to the exhibit.
A data architect is provided with five tables. One table has Sales Information. The other four tables provide attributes that the end user will group and filter by.
There is only one Sales Person in each Region and only one Region per Customer.
Which data model is the most optimal for use in this situation?

Answer: A

Explanation:
In the given scenario, where the data architect is provided with five tables, the goal is to design the most optimal data model for use in Qlik Sense. The key considerations here are to ensure a proper star schema, minimize redundancy, and ensure clear and efficient relationships among the tables.
Option Dis the most optimal model for the following reasons:
* Star Schema Design:
* In Option D, the Fact_Gross_Sales table is clearly defined as the central fact table, while the other tables (Dim_SalesOrg, Dim_Item, Dim_Region, Dim_Customer) serve as dimension tables.
This layout adheres to the star schema model, which is generally recommended in Qlik Sense for performance and simplicity.
* Minimization of Redundancies:
* In this model, each dimension table is only connected directly to the fact table, and there are no unnecessary joins between dimension tables. This minimizes the chances of redundant data and ensures that each dimension is only represented once, linked through a unique key to the fact table.
* Clear and Efficient Relationships:
* Option D ensures that there is no ambiguity in the relationships between tables. Each key field (like Customer ID, SalesID, RegionID, ItemID) is clearly linked between the dimension and fact tables, making it easy for Qlik Sense to optimize queries and for users to perform accurate aggregations and analysis.
* Hierarchical Relationships and Data Integrity:
* This model effectively represents the hierarchical relationships inherent in the data. For example, each customer belongs to a region, each salesperson is associated with a sales organization, and each sales transaction involves an item. By structuring the data in this way, Option D maintains the integrity of these relationships.
* Flexibility for Analysis:
* The model allows users to group and filter data efficiently by different attributes (such as salesperson, region, customer, and item). Because the dimensions are not interlinked directly with each other but only through the fact table, this setup allows for more flexibility in creating visualizations and filtering data in Qlik Sense.
References:
* Qlik Sense Best Practices: Adhering to star schema designs in Qlik Sense helps in simplifying the data model, which is crucial for performance optimization and ease of use.
* Data Modeling Guidelines: The star schema is recommended over snowflake schema for its simplicity and performance benefits in Qlik Sense, particularly in scenarios where clear relationships are essential for the integrity and accuracy of the analysis.

NEW QUESTION # 16
Exhibit.

Refer to the exhibit.
A business analyst informs the data architect that not all analysis types over time show the expected data.
Instead they show very little data, if any.
Which Qlik script function should be used to resolve the issue in the data model?

A. TimeStamp#(OrderDate, 'M/D/YYYY hh.mm.ff') AS OrderDate in both the table "Orders" and "Master Calendar"
B. Date(OrderDate) AS OrderDate in both the table "Orders" and "Master Calendar"
C. DatefFloor(OrderDate)) AS OrderDate in both the table "Orders" and "Master Calendar"
D. TimeStamp(OrderDate) AS OrderDate in both the table "Orders" and "Master Calendar"

Answer: B

Explanation:
In the provided data model, there is an issue where certain types of analysis over time are not showing the expected data. This problem is often caused by a mismatch in the data formats of the OrderDate field between the Orders and MasterCalendar tables.
* Option A:DatefFloor(OrderDate)) would round down to the nearest date boundary, which might not address the root cause if the issue is related to different date and time formats.
* Option B:TimeStamp#(OrderDate, 'M/D/YYYY hh.mm.ff') ensures that the date is interpreted correctly as a timestamp, but this does not resolve potential mismatches in date format directly.
* Option C:TimeStamp(OrderDate) will keep both date and time, which may still cause mismatches if the MasterCalendar is dealing purely with dates.
* Option D:Date(OrderDate) formats the OrderDate to show only the date portion (removing the time part). This function will ensure that the date values are consistent across the Orders and MasterCalendar tables by converting the timestamps to just dates. This is the most straightforward and effective way to ensure consistency in date-based analysis.
In Qlik Sense, dates and timestamps are stored as dual values (both text and numeric), and mismatches can lead to incomplete or incorrect analyses. By using Date(OrderDate) in both the Orders and MasterCalendar tables, you ensure that the analysis will have consistent date values, resolving the issue described.

NEW QUESTION # 17
A data architect executes the following script:

Which values does the OrderDate field contain after executing the script?

A. 20210131, 2020/01/31, 31/01/2019, 0
B. 20210131, 2020/01/31, 31/01/2019, 31/12/2022
C. 20210131, 2020/01/31, 31/01/2019
D. 20210131, 2020/01/31, 31/01/2019, 9999

Answer: B

Explanation:
In the script provided, the alt() function is used to handle various date formats. The alt() function in Qlik Sense evaluates a list of expressions and returns the first valid expression. If none of the expressions are valid, it returns the last argument provided (in this case, '31/12/2022').
Step-by-step breakdown:
* The alt() function checks the Date field for three different formats:
* YYYYMMDD
* YYYY/MM/DD
* DD/MM/YYYY
* If none of these formats match the value in the Date field, the default date '31/12/2022' is assigned.
Values in the Date field:
* 20210131: Matches the first format YYYYMMDD.
* 2020/01/31: Matches the second format YYYY/MM/DD.
* 31/01/2019: Matches the third format DD/MM/YYYY.
* 9999: Does not match any of the formats, so the alt() function returns the default value '31/12/2022'.

NEW QUESTION # 18
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?

A. SQL Developer
B. Vice President of Marketing
C. Product Buyer
D. Chief Information Officer

Answer: C

Explanation:
In this scenario, the focus of the project is to enhance inventory analytics, specifically targeting forecasting inventory levels. The primary goal is to understand the factors influencing inventory management and to build a model that helps in predicting future inventory needs.
Option A: Product Buyeris the correct stakeholder to consult first.
Here's why:
* Direct Involvement in Inventory Management:
* The Product Buyer is typically responsible for making decisions related to purchasing and maintaining inventory levels. They have a deep understanding of the factors that influence inventory needs, such as lead times, supplier reliability, demand forecasting, and purchasing cycles.
* Knowledge of Inventory Requirements:
* Since the project's primary focus is forecasting inventory levels, the Product Buyer will provide crucial insights into the variables that affect inventory and the data needed for accurate forecasting. They can guide what historical data is essential and what external factors might need to be considered in the forecasting model.
* Alignment with Business Objectives:
* By consulting the Product Buyer, the project can ensure that the inventory forecasting models align with the company's inventory management objectives, avoiding overstocking or understocking, and thus optimizing costs.
References:
* Qlik Project Management Best Practices: In analytics projects, particularly those focused on specific operational areas like inventory management, consulting the stakeholders who are closest to the operational data and decision-making processes ensures that the solution will be relevant and effective.

NEW QUESTION # 19
......

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QSDA2024 RELIABLE TEST TESTKING | QSDA2024 EXAM DUMPS FREE

QSDA2024 Reliable Test Testking | QSDA2024 Exam Dumps Free