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QSDA2024 Testengine & QSDA2024 Online Tests

Die Qlik QSDA2024 Zertifizierungsprüfung ist eine der beliebten und wichtigen Prüfung in der IT-Branche. Wir haben die besten Lernhilfe und den besten Online-Service. Wir bieten den IT-Fachleuten eine Abkürzung. Die online Tests zur Qlik QSDA2024 Zertifizierungsprüfung von Fast2test enthalten viele Prüfungsinhalte und Antworten, die Sie wollen. Wenn Sie die Simulationsprüfung von Fast2test bestehen, dann finden Sie, dass Fast2test bietet genau was, was Sie wollen und dass Sie sich gut auf die Qlik QSDA2024 Prüfung vorbereiten können.

Qlik QSDA2024 Prüfungsplan:

Thema
Einzelheiten

Thema 1

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.

Thema 2

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.

Thema 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.

Thema 4

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.

Thema 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.

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QSDA2024 Prüfungsfragen Prüfungsvorbereitungen, QSDA2024 Fragen und Antworten, Qlik Sense Data Architect Certification Exam - 2024

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Qlik Sense Data Architect Certification Exam - 2024 QSDA2024 Prüfungsfragen mit Lösungen (Q46-Q51):

46. Frage
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 2 years of data in an aggregated app and create a separate transaction app for occasional use
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 and use best practices in scripting and visualization to calculate and display aggregated data
D. Load only aggregated data for 2 years and apply filters on a sheet for transaction data

Antwort: B

47. Frage
Exhibit.

The Section Access security table for an app is shown. User ABCPPP opens a Qlik Sense app with a table using the field called LEVEL on one of the table columns.
Which is the result?

A. The user gets a 'Field not found' error.
B. The user gets an 'Incomplete visualization' error.
C. The table is displayed without the LEVEL column.
D. The table is removed from the user interface.

Antwort: C

Begründung:
In this scenario, the Section Access security table controls user access to data within the Qlik Sense app. The user in question, ABCPPP, has a specific entry in the security table that determines their access rights to the LEVEL field.
Understanding Section Access:
* Section Accessis used to enforce security by restricting access to certain data based on the user's credentials.
* In the security table provided, the USER role for ABCPPP is set to have access to all data (* in the LINK field), but the OMIT field is set to LEVEL. The OMIT field in Section Access specifies fields that should be omitted from the user's view.
Outcome:
* Since the OMIT field for user ABCPPP is set to LEVEL, this user will not have access to the LEVEL field in the Qlik Sense application.
Option D: The table is displayed without the LEVEL columnis the correct outcome.
* Explanation: When user ABCPPP opens the app, the LEVEL field is omitted from their view. Any table or visualization that uses the LEVEL field will have that field excluded from display. The rest of the data and columns in the table will be visible, but the LEVEL column will not be shown.
References:
* Qlik Sense Security and Section Access Documentation: The OMIT functionality in Section Access is specifically designed to remove fields from the user's access, ensuring that sensitive or unnecessary data is not exposed.

48. Frage
A data architect inherits an app that takes too long to load and overruns the data load window.
The app pulls all records (new and historical) from three large databases. The reload process puts a heavy load on the source database servers. All of the data is required for analysis.
What should the data architect do?

A. Make sure the individual reload tasks in the QMC are not running in parallel
B. Implement incremental load on each database using QVD files
C. Implement ODAG to split out the app into smaller chunks
D. Implement Direct Discovery with partial load

Antwort: B

Begründung:
The scenario describes an app that is experiencing long load times due to the need to pull all records, both new and historical, from three large databases. This situation puts a strain on both the Qlik environment and the source databases. Given that all data is required for analysis, a full reload each time can be inefficient and resource-intensive.
Implementingincremental loadis a widely recommended approach in such cases. Incremental loading allows you to load only new or changed data since the last reload, rather than reloading all the data every time. This significantly reduces the time and resources required for reloading, as only a subset of the data needs to be processed during each reload. QVD (QlikView Data) files are typically used to store the historical data, while only the new or updated records are fetched from the source databases.
This approach would help:
* Reduce the load on the source databases.
* Shorten the data reload window.
* Maintain historical data efficiently while ensuring that all new data is captured.

49. Frage
Exhibit

Refer to the exhibit.
The salesperson ID and the office to which the salesperson belongs is stored for each transaction. The data model also contains the current office for the salesperson. The current office of the salesperson and the office the salesperson was in when the transaction occurred must be visible. The current source table view of the model is shown. A data architect must resolve the synthetic key.
How should the data architect proceed?

A. Force concatenation between the tables
B. Alias Office to CurrentOffice In the CurrentOffice table
C. Inner Join the Transaction table to the CurrentOffice table
D. Comment out the Office in the Transaction table

Antwort: B

Begründung:
In the provided data model, both the CurrentOffice and Transaction tables contain the fields SalesID and Office. This leads to the creation of a synthetic key in Qlik Sense because of the two common fields between the two tables. A synthetic key is created automatically by Qlik Sense when two or more tables have two or more fields in common. While synthetic keys can be useful in some scenarios, they often lead to unwanted and unexpected results, so it's generally advisable to resolve them.
In this case, the goal is to have both the current office of the salesperson and the office where the transaction occurred visible in the data model. Here's how each option compares:
* Option A: Comment out the Office in the Transaction table:This would remove the Office field from the Transaction table, which would prevent you from seeing which office the salesperson was in when the transaction occurred. This option does not meet the requirement.
* Option B: Inner Join the Transaction table to the CurrentOffice table:Performing an inner join would merge the two tables based on the common SalesID and Office fields. However, this might result in a loss of data if there are sales records in the Transaction table that don't have a corresponding record in the CurrentOffice table or vice versa. This approach might also lead to unexpected results in your analysis.
* Option C: Alias Office to CurrentOffice In the CurrentOffice table:By renaming the Office field in the CurrentOffice table to CurrentOffice, you prevent the synthetic key from being created. This allows you to differentiate between the salesperson's current office and the office where the transaction occurred. This approach maintains the integrity of your data and allows for clear analysis.
* Option D: Force concatenation between the tables:Forcing concatenation would combine the rows of both tables into a single table. This would not solve the issue of distinguishing between the current office and the office at the time of the transaction, and it could lead to incorrect data associations.
Given these considerations, the best approach to resolve the synthetic key while fulfilling the requirement of having both the current office and the office at the time of the transaction visible is toAlias Office to CurrentOffice in the CurrentOffice table. This ensures that the data model will accurately represent both pieces of information without causing synthetic key issues.

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

Antwort: B

Begründung:
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.

51. Frage
......

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