Blog

Steve Miller Steve Miller

0 Course Enrolled • 0 Course Completed

Biography

Pass Guaranteed Quiz 2025 Qlik QSDA2024: Efficient Test Qlik Sense Data Architect Certification Exam - 2024 Simulator Online

P.S. Free 2025 Qlik QSDA2024 dumps are available on Google Drive shared by Prep4sureGuide: https://drive.google.com/open?id=1B142yZQL6I3EokCwoa30a17AQdp27YMo

You may urgently need to attend QSDA2024 certificate exam and get the QSDA2024 certificate to prove you are qualified for the job in some area. But what certificate is valuable and useful and can help you a lot? Passing the QSDA2024 test certification can help you prove that you are competent in some area and if you buy our QSDA2024 Study Materials you will pass the QSDA2024 test almost without any problems. There are many benefits after you pass the QSDA2024 certification such as you can enter in the big company and double your wage.

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

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 3

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 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

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.

>> Test QSDA2024 Simulator Online <<

QSDA2024 Latest Dumps Pdf | QSDA2024 Latest Exam Price

In the era of informational globalization, the world has witnessed climax of science and technology development, and has enjoyed the prosperity of various scientific blooms. In 21st century, every country had entered the period of talent competition, therefore, we must begin to extend our QSDA2024 personal skills, only by this can we become the pioneer among our competitors. We here tell you that there is no need to worry about. Our QSDA2024 Actual Questions are updated in a high speed. Since the date you pay successfully, you will enjoy the QSDA2024 test guide freely for one year, which can save your time and money. We will send you the latest QSDA2024 study dumps through your email, so please check your email then.

Qlik Sense Data Architect Certification Exam - 2024 Sample Questions (Q40-Q45):

NEW QUESTION # 40
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?

A. Load only aggregated data for 2 years and apply filters on a sheet for transaction data
B. Load only aggregated data for 2 years and use On-Demand App Generation (ODAG) for transaction data
C. Load only 2 years of data in an aggregated app and create a separate transaction app for occasional use
D. Load only 2 years of data and use best practices in scripting and visualization to calculate and display aggregated data

Answer: B

Explanation:
In this scenario, the company generates 1 GB of ticketing data daily, accumulating up to 720 GB over two years. Business users mainly require trend analysis for the past two years and rarely need to access the transaction-level data. The objective is to load only the necessary data while ensuring the system remains performant.
Option Cis the optimal choice for the following reasons:
* Efficiency in Data Handling:
* By loading only aggregated data for the two years, the app remains lean, ensuring faster load times and better performance when users interact with the dashboard. Aggregated data is sufficient for analyzing trends, which is the primary use case mentioned.
* On-Demand App Generation (ODAG):
* ODAG is a feature in Qlik Sense designed for scenarios like this one. It allows users to generate a smaller, transaction-level dataset on demand. Since users rarely need to drill down into transaction-level data, ODAG is a perfect fit. It lets users load detailed data for specific dates only when needed, thus saving resources and keeping the main application lightweight.
* Performance Optimization:
* Loading only aggregated data ensures that the application is optimized for performance. Users can analyze trends without the overhead of transaction-level details, and when they need more detailed data, ODAG allows for targeted loading of that data.
References:
* Qlik Sense Best Practices: Using ODAG is recommended when dealing with large datasets where full transaction data isn't frequently needed but should still be accessible.
* Qlik Documentation on ODAG: ODAG helps in maintaining a balance between performance and data availability by providing a method to load only the necessary details on demand.

NEW QUESTION # 41
A table is generated resulting from the following script:

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?

Answer: A

Explanation:
The issue described is that not all orders for a selected date are shown. This issue arises because the original script uses the Date(OrderTime) function, which only extracts the date part of the OrderTime timestamp, potentially resulting in incorrect matching when filtering by date due to the time component still being present in the underlying data.
Explanation of Option D:
* Floor(OrderTime): The Floor() function truncates the OrderTime timestamp to remove the time component, leaving only the date part. This ensures that all orders on the same date are treated equally, without any interference from the time component.
* Date(Floor(OrderTime), 'YYYY-MM-DD'): The Date() function formats the floored value into a date format (YYYY-MM-DD), which is essential for consistent date comparison.
This approach ensures that when you select a date in the application, all orders for that date are shown, as the time component has been effectively removed.

NEW QUESTION # 42
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
C. 20210131, 2020/01/31, 31/01/2019, 31/12/2022
D. 20210131, 2020/01/31, 31/01/2019, 9999

Answer: C

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 # 43

Refer to the exhibit.
A data architect needs to create a data model for a new app. Users must be able to see:
* Total sales for each customer
* Total sales for a given state
* Customers that have not had any sales
* Names of salesperson and regional account managers
* Total number of sales by date
Which steps should the data architect perform to meet these requirements?
Which steps should the data architect perform to meet these requirements?

A. 1. Load the Customers table and alias the CustID field as CustomerlD
2. Load the Employees table
3. Load the Sales table and alias the SalesPersonID and RegionalAcctMgrlD fields as EmployeelD
B. 1. Load the Sales table
2. Load the Customers table
3. Load the Employees table twice; name it and alias the EmployeelD field appropriately each time
C. 1. Use a Mapping Load for the Employees table
2. Load the Sales table and use ApplyMap to get the names for SalesPersonID and RegionalAcctMgrlD
3. Use a Left Join Load to add the customer details for the Sales table
D. 1. Load the Customers table and alias the CustID field as CustomerlD
2. Use a Mapping Load for the Employees table
3. Load the Sales table and use ApplyMap to get the names for SalesPersonID and RegionalAcctMgrlD

Answer: B

Explanation:
In the provided scenario, the data architect needs to create a data model that supports various analyses, including total sales for each customer, total sales by state, identifying customers with no sales, and displaying the names of salespersons and regional account managers.
Here's whyOption Cis the correct choice:
* Loading the Sales Table:The Sales table contains key information related to sales transactions, including SaleID, CustomerID, Amount, SaleDate, SalesPersonID, and RegionalAcctMgrID. This table must be loaded first as it will be central to the analysis.
* Loading the Customers Table:The Customers table includes customer details such as CustID, CustName, Address, City, State, and Zip. Loading this table and linking it to the Sales table via the CustomerID field allows you to perform analyses such as total sales per customer and total sales by state. Importantly, loading the customers separately will also allow the identification of customers without any sales.
* Loading the Employees Table Twice:The Employees table must be loaded twice because it is used to look up two different roles in the sales process: the SalesPersonID and the RegionalAcctMgrID. When loading the table twice:
* The first instance of the Employees table will be used to map the SalesPersonID to EmployeeName.
* The second instance will be used to map the RegionalAcctMgrID to EmployeeName.
* Aliasing the EmployeeID field appropriately in each instance is crucial to prevent creating synthetic keys and to ensure the correct association with the roles in the sales process.
This approach ensures that the data model will correctly support all the required analyses, including identifying customers without sales, which is crucial for meeting the business requirements.
* Option AandOption Bpropose using a mapping load and ApplyMap, which can complicate the model and does not directly address all the business requirements.
* Option Dinvolves aliasing fields in a way that could create unnecessary complexity and might not accurately reflect the relationships in the data.
Thus,Option Cis the correct answer as it best meets the requirements while maintaining a clear and functional data model.

NEW QUESTION # 44
Sales managers need to see an overview of historical performance and highlight the current year's metrics.
The app has the following requirements:
* Display the current year's total sales
* Total sales displayed must respond to the user's selections
Which variables should a data architect create to meet these requirements?

Answer: C

Explanation:
To meet the requirements of displaying the current year's total sales in a way that responds to user selections, the correct approach involves using both SET and LET statements to define the necessary variables in the data load editor.
Explanation of Option C:
* SET vCurrentYear = Year(Today());
* The SET statement is used here to assign the current year to the variable vCurrentYear. The SET statement treats the variable as a text string without evaluation. This is appropriate for a variable that will be used as part of an expression, ensuring the correct year is dynamically set based on the current date.
* LET vCurrentYTDSales = '=SUM({$<Year={'$(vCurrentYear)'}>} [Sales Amount])';
* The LET statement is used here to assign an evaluated expression to the variable vCurrentYTDSales. This expression calculates the Year-to-Date (YTD) sales for the current year by filtering the Year field to match vCurrentYear. The LET statement ensures that the expression inside the variable is evaluated, meaning that when vCurrentYTDSales is called in a chart or KPI, it dynamically calculates the YTD sales based on the current year and any user selections.
Key Points:
* Dynamic Year Calculation: Year(Today()) dynamically calculates the current year every time the script runs.
* Responsive to Selections: The set analysis syntax {$<Year={'$(vCurrentYear)'}>} ensures that the sales totals respond to user selections while still focusing on the current year's data.
* Appropriate Use of SET and LET: The combination of SET for storing the year and LET for storing the evaluated sum expression ensures that the variables are used effectively in the application.

NEW QUESTION # 45
......

With the development of economic globalization, your competitors have expanded to a global scale. Obtaining an international QSDA2024 certification should be your basic configuration. What I want to tell you is that for QSDA2024 Preparation materials, this is a very simple matter. And as we can claim that as long as you study with our QSDA2024 learning guide for 20 to 30 hours, then you will pass the exam as easy as pie.

QSDA2024 Latest Dumps Pdf: https://www.prep4sureguide.com/QSDA2024-prep4sure-exam-guide.html

2025 Latest Prep4sureGuide QSDA2024 PDF Dumps and QSDA2024 Exam Engine Free Share: https://drive.google.com/open?id=1B142yZQL6I3EokCwoa30a17AQdp27YMo

Cookie	Duration	Description
cookielawinfo-checkbox-analytics	11 months	This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Analytics".
cookielawinfo-checkbox-functional	11 months	The cookie is set by GDPR cookie consent to record the user consent for the cookies in the category "Functional".
cookielawinfo-checkbox-necessary	11 months	This cookie is set by GDPR Cookie Consent plugin. The cookies is used to store the user consent for the cookies in the category "Necessary".
cookielawinfo-checkbox-others	11 months	This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Other.
cookielawinfo-checkbox-performance	11 months	This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Performance".
viewed_cookie_policy	11 months	The cookie is set by the GDPR Cookie Consent plugin and is used to store whether or not user has consented to the use of cookies. It does not store any personal data.

Steve Miller Steve Miller

Biography

COOKIE NOTICE