FREE PDF QUIZ 2025 QSDA2024: QLIK SENSE DATA ARCHITECT CERTIFICATION EXAM - 2024–HIGH-QUALITY PASS GUARANTEED

Free PDF Quiz 2025 QSDA2024: Qlik Sense Data Architect Certification Exam - 2024–High-quality Pass Guaranteed

Free PDF Quiz 2025 QSDA2024: Qlik Sense Data Architect Certification Exam - 2024–High-quality Pass Guaranteed

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Qlik QSDA2024 Exam Syllabus Topics:

TopicDetails
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
  • 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 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 (Q32-Q37):

NEW QUESTION # 32

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 Sales table
    2. Load the Customers table
    3. Load the Employees table twice; name it and alias the EmployeelD field appropriately each time
  • B. 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
  • 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: A

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 # 33
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?

  • A. A user name listed in the Section Access table is spelled incorrectly.
  • B. The ACCESS Column in the Section Access table has been added in lowercase.
  • C. The service account running the task is not included in the Section Access table.
  • D. The data architect does not have rights to reload the app.

Answer: C

Explanation:
When implementing Section Access in Qlik Sense, it is crucial that all accounts that need to access the data- including the service account that performs the scheduled reload-are included in the Section Access table. If the service account is not included, Qlik Sense will not be able to access any data, leading to a failure in the reload process.
Here's a breakdown of why the other options are less likely:
* A. The ACCESS column in the Section Access table has been added in lowercase:This would generally result in a syntax error, but it would not allow the script to execute successfully without causing an immediate failure, unrelated to Section Access.
* C. A user name listed in the Section Access table is spelled incorrectly:While this could lead to some users not having the correct access, it would not cause the entire reload to fail. The issue here is broader, affecting the entire application load process.
* D. The data architect does not have rights to reload the app:If the architect did not have rights, the script would not run successfully even without Section Access.
The correct issue in this scenario is thatthe service account running the task is not included in the Section Access table. This is a common cause of load failures after adding Section Access. To resolve this, ensure that the service account is added with sufficient privileges in the Section Access table


NEW QUESTION # 34
A company needs to analyze daily sales data from different countries. They also need to measure customer satisfaction of products as reported on a social media website. Thirty (30) reports must be produced with an average of 20,000 rows each. This process is estimated to take about 3 hours.
Which option should the data architect use to build this solution?

  • A. Mailbox IMAP
  • B. Microsoft SQL Server
  • C. Qlik REST Connector
  • D. Qlik GeoAnalytics

Answer: C

Explanation:
In this scenario, the company needs to analyze daily sales data from different countries and also measure customer satisfaction of products as reported on a social media website. This suggests that the data is likely coming from different sources, including possibly an API or a web service (social media website).
TheQlik REST Connectoris the appropriate tool for this job. It allows you to connect to RESTful web services and retrieve data directly into Qlik Sense. This is especially useful for integrating data from various online sources, such as social media platforms, which typically expose data via REST APIs. The REST Connector enables the extraction of large datasets from these sources, which is necessary given the requirement to produce 30 reports with an average of 20,000 rows each.
* Microsoft SQL Serveris not suitable for fetching data from web services or social media platforms.
* Qlik GeoAnalyticsis used for mapping and geographical data visualization, not for connecting to RESTful services.
* Mailbox IMAPis for connecting to email servers and is not applicable to the data extraction needs described here.
Thus,Qlik REST Connectoris the correct answer for this scenario.


NEW QUESTION # 35
Exhibit.

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?

  • A. Hierarchy Be longsTo
  • B. Peek
  • C. Generic
  • D. PivotTable

Answer: C

Explanation:
In this scenario, the data architect wants to transform the input dataset, which is in a key-value pair structure, into a table where each attribute becomes a column with its corresponding value under the relevant key.
Understanding the Requirement:
* Theinputdata consists of three fields: Key, Attribute, and Value.
* The desiredoutputstructure has the Key as a primary identifier, and the Attributes (like Color, Diameter, Height, etc.) are spread across the columns, with corresponding values filled in each row.
Best Method to Achieve this Transformation:
* The appropriate method to convert key-value pairs into a structured table where each unique attribute becomes a separate column is theGeneric Loadfunction in Qlik Sense.
Why Generic?
* Generic Loadis specifically designed for situations where data is stored in a key-value format (like the one provided) and needs to be converted into a more traditional tabular format, with attributes as columns.
* It creates a separate table for each combination of Key and Attribute, effectively "pivoting" the attribute values into columns in the output table.
How it Works:
* When applying a GENERIC LOAD to the input dataset, Qlik Sense will generate multiple tables, one for each Attribute. However, in the final data model, Qlik Sense automatically joins these tables by the Key field, effectively producing the desired output structure.
References:
* Qlik Sense Documentation on Generic Load: The documentation outlines how to use the Generic Load to handle key-value pairs and pivot them into a more traditional table format.


NEW QUESTION # 36

Refer to the exhibit
A large transport company (Company A) acquires a smaller rival (Company B).
Company A has been using Qlik Sense tor 6 years to track revenue per ship journey. Ship journeys with no revenue (such as journeys to shipyards for repair) always show revenue of $0.
Company A wants to combine its data set with the data set of the acquired Company B. Company B's ship journey data shows $0 revenue in one of the following ways:
* A NULL value
* A value with one or more blank spaces (ASCII char code 32)
The data architect wants to conform the Company B data to the Company A standard, specifically regarding the use of an explicit $0 for journeys without revenue. Which script line should the data architect use?

  • A.
  • B.
  • C.
  • D.

Answer: D

Explanation:
In this scenario, the data architect needs to conform the revenue data from Company B to match the data standard of Company A, where $0 is explicitly used to represent journeys without revenue.
Explanation of the Correct Script:
* Option A:money(replace(Revenue, chr(32), 0)) AS [Revenue Conformed]
* replace(Revenue, chr(32), 0):This part of the expression replaces any spaces (ASCII character code 32) in the Revenue field with 0.
* money(...):This function formats the resulting value as currency. Since Company B may have either null values or spaces where 0 should be, this script ensures that any blanks are replaced with 0 and then formatted as currency.
Why Option A is Correct:
* Handling Spaces:The replace() function is effective in replacing spaces with 0, conforming to Company A's standard of using $0 for non-revenue journeys.
* Handling NULL Values:The money() function is used to ensure the final output is formatted as currency. However, it's important to note that NULL values are not directly handled by the replace() function, which is why it is applied before money() to deal with spaces.


NEW QUESTION # 37
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