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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 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.
Topic 5	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.

Qlik Sense Data Architect Certification Exam - 2024 Sample Questions (Q23-Q28):

NEW QUESTION # 23
Exhibit.

A large electronics company re-assigns sales people once per year from one Department to another.
SPID is the Salesperson ID; the SPID for each individual sales person Name remains constant. The Department for a SPID may change; each change is stored in the Dynamic Dimension data.
Four tables need to be linked correctly: a transaction table, a dynamic salesperson dimension, a static salesperson dimension, and a department dimension.
Which script prefix should the data architect use?

A. Partial Reload
B. Semantic
C. IntervalMatch
D. Merge

Answer: C

Explanation:
In the scenario described, the Dynamic Dimension data tracks changes in department assignments for salespeople over time. To correctly link the transaction data with the salesperson data and ensure that sales are associated with the correct department based on the date, an IntervalMatch function should be used.
IntervalMatchis designed to match discrete data (like transaction dates) with a range of dates. In this case, each salesperson's department assignment is valid over a period of time, and the IntervalMatch function can be used to link the transaction data with the correct department for each salesperson based on the transaction date.
* Option A (Merge):This option is incorrect as it refers to combining data sets, which doesn't address the need to handle the dynamic, date-based department assignments.
* Option B (IntervalMatch):This is the correct choice because it allows you to match each transaction with the correct department assignment based on the ChangeDate in the Dynamic Dimension data.
* Option C (Partial Reload):This refers to reloading only part of the data, which is not relevant to linking tables based on date ranges.
* Option D (Semantic):This option is not applicable as it refers to a broader approach to data modeling and interpretation rather than specifically linking data based on time intervals.
Thus,IntervalMatchis the correct method for linking the transaction data with the dynamic salesperson dimension, ensuring that each transaction is associated with the correct department based on the historical assignment data.

NEW QUESTION # 24
The data architect has been tasked with building a sales reporting application.
* Part way through the year, the company realigned the sales territories
* Sales reps need to track both their overall performance, and their performance in their current territory
* Regional managers need to track performance for their region based on the date of the sale transaction
* There is a data table from HR that contains the Sales Rep ID, the manager, the region, and the start and end dates for that assignment
* Sales transactions have the salesperson in them, but not the manager or region.
What is the first step the data architect should take to build this data model to accurately reflect performance?

A. Build a star schema around the sales table, and use the Hierarchy function to join the HR data to the model
B. Create a link table with a compound key of Sales Rep / Transaction Date to find the correct manager and region
C. Implement an "as of calendar against the sales table and use ApplyMap to fill in the needed management data
D. Use the IntervalMatch function with the transaction date and the HR table to generate point in time data

Answer: D

Explanation:
In the provided scenario, the sales territories were realigned during the year, and it is necessary to track performance based on the date of the sale and the salesperson's assignment during that period. The IntervalMatch function is the best approach to create a time-based relationship between the sales transactions and the sales territory assignments.
* IntervalMatch: This function is used to match discrete values (e.g., transaction dates) with intervals (e.
g., start and end dates for sales territory assignments). By matching the transaction dates with the intervals in the HR table, you can accurately determine which territory and manager were in effect at the time of each sale.
Using IntervalMatch, you can generate point-in-time data that accurately reflects the dynamic nature of sales territory assignments, allowing both sales reps and regional managers to track performance over time.

NEW QUESTION # 25
Users of a published app report incomplete visualizations. The data architect checks the app multiple times and cannot replicate the error. The error affects only one team.
Which is the most likely cause?

A. An Omit field has been applied.
B. The affected users were NOT added to the Section Access table.
C. Section access restricts too many records.
D. A security rule has been applied to the sheet object.

Answer: C

Explanation:
In this scenario, users of a published app report incomplete visualizations, but the data architect is unable to replicate the error. This issue affects only one team, suggesting that the problem is related to how data is being restricted or filtered for that specific team.
* Section Access: This is a security feature in Qlik Sense that controls user access to specific data within an app. If Section Access is misconfigured, it can restrict access to more records than intended, leading to incomplete visualizations for certain users or teams.
* Restricting Too Many Records: If the Section Access is too restrictive, it might limit the data available to the affected users, causing the visualizations to display incomplete information. This could explain why the data architect, who likely has full access, cannot replicate the issue.

NEW QUESTION # 26
Exhibit.

A data architect must load the two tables without creating a synthetic key. The data architect also must make sure expressions like Sum([Budgeted Sales]) are calculated correctly.
Which load script meets these requirements?

Answer: C

Explanation:
In the scenario provided, the data architect needs to load two tables (Budget and Sales) without creating a synthetic key, while ensuring that expressions like Sum([Budgeted Sales]) are calculated correctly.
Here is a breakdown of the options:
* Option A (Outer Join):This option uses an outer join between the Sales table and the Budget table.
While this approach will combine the tables based on the common fields (Year and Region), it will result in a single table that contains all fields from both tables. This approach prevents the creation of a synthetic key and retains all records from both tables, ensuring that all budgeted and actual sales data is available. As a result, calculations like Sum([Budgeted Sales]) will work correctly.This is the correct solution.
* Option B (Concatenate):This option uses concatenate, which combines the tables by stacking them on top of each other as if they were one table. This approach will not prevent synthetic keys and could cause issues with calculations since Budgeted Sales and Actual Sales would be in the same column, leading to incorrect aggregation results.
* Option C (Separate Load):This option simply loads the tables separately without any join or concatenation. While this will not create a synthetic key, it will result in two separate tables in the data model. Without any connection between these tables, calculations involving both Budgeted Sales and Actual Sales will not work correctly.
* Option D (Inner Join):This option uses an inner join, which will combine only the records that match in both tables based on Year and Region. While this approach avoids synthetic keys, it may exclude records that do not have a corresponding match in both tables, potentially leading to incomplete data.
Given the requirements to avoid synthetic keys and ensure correct calculations,Option A (Outer Join)is the most appropriate approach. It ensures all relevant data is included and that the data model remains free from synthetic keys, while also allowing accurate calculations.

NEW QUESTION # 27
A data architect needs to develop three separate apps (Sales, Finance, and Operations). The three apps share numerous identical calculation expressions.
The goals include:
* Reducing duplicate script
* Saving time on expression modifications
* Increasing reusable Qlik developer assets.
The data architect creates a common script and stores it on a file server that Qlik Sense can access. How should the data architect complete the requirements?

A. Include script function
B. Macro on server
C. Call batch file
D. Execute server script

Answer: A

Explanation:
When developing multiple Qlik Sense applications (Sales, Finance, Operations) that share numerous identical calculation expressions, it is crucial to have a centralized, reusable script to avoid redundancy, save time on modifications, and increase the reusability of the assets.
The best approach in Qlik Sense to achieve these goals is to use theIncludescript function. This function allows the data architect to reference a script file that is stored on a file server. The Include function willinject the contents of the external script file into the Qlik Sense script at the point where the Include statement is called. This means that all three apps (Sales, Finance, Operations) can include this common script, and any updates made to the script will automatically apply to all apps that include it.
This method provides a highly maintainable solution because:
* No Duplicate Script:The shared logic is maintained in a single file, eliminating redundancy.
* Ease of Modifications:Any changes made to the script are propagated to all applications that include it.
* Reusable Assets:The script can be reused across different applications, enhancing efficiency and consistency.

NEW QUESTION # 28
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QLIK QSDA2024 QUESTIONS - PASS EXAM AND GET CAREER BENEFITS

Qlik QSDA2024 Questions - Pass Exam and Get Career Benefits