RCAMS

Jack Harris Jack Harris

0 Course Enrolled • 0 Course Completed

Biography

Newest Valid QSDA2024 Exam Vce offer you accurate Top Exam Dumps | Qlik Qlik Sense Data Architect Certification Exam - 2024

Qlik QSDA2024 is one of the important certification exams. TrainingQuiz's experienced IT experts through their extensive experience and professional IT expertise have come up with IT certification exam study materials to help people pass Qlik Certification QSDA2024 Exam successfully. TrainingQuiz's providing learning materials can not only help you 100% pass the exam, but also provide you a free one-year update service.

Qlik QSDA2024 Exam Syllabus Topics:

Topic
Details

Topic 1

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 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 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 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

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.

>> Valid QSDA2024 Exam Vce <<

Top QSDA2024 Exam Dumps | QSDA2024 Latest Exam Pattern

As we know, there are nothing best, only something better for we are keeping developing and face competion all the time. Taht is why our QSDA2024 study guide is regularly updated by our experts for keeping it always compatible to the needs and requirements of our worthy customers all over the world. The result is that you will always find our QSDA2024 Exam Braindumps are the latest and valid. Come to buy our QSDA2024 learning quiz, you will pass your exam easily!

Qlik Sense Data Architect Certification Exam - 2024 Sample Questions (Q50-Q55):

NEW QUESTION # 50
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. The data architect does not have rights to reload the app.
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. A user name listed in the Section Access table is spelled incorrectly.

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 # 51
Refer to the exhibit.

A system creates log files and csv files daily and places these files in a folder. The log files are named automatically by the source system and change regularly. All csv files must be loaded into Qlik Sense for analysis.
Which method should be used to meet the requirements?

Answer: A

Explanation:
In the scenario described, the goal is to load all CSV files from a directory into Qlik Sense, while ignoring the log files that are also present in the same directory. The correct approach should allow for dynamic file loading without needing to manually specify each file name, especially since the log files change regularly.
Here's whyOption Bis the correct choice:
* Option A:This method involves manually specifying a list of files (Day1, Day2, Day3) and then iterating through them to load each one. While this method would work, it requires knowing the exact file names in advance, which is not practical given that new files are added regularly. Also, it doesn't handle dynamic file name changes or new files added to the folder automatically.
* Option B:This approach uses a wildcard (*) in the file path, which tells Qlik Sense to load all files matching the pattern (in this case, all CSV files in the directory). Since the csv file extension is explicitly specified, only the CSV files will be loaded, and the log files will be ignored. This method is efficient and handles the dynamic nature of the file names without needing manual updates to the script.
* Option C:This option is similar to Option B but targets text files (txt) instead of CSV files. Since the requirement is to load CSV files, this option would not meet the needs.
* Option D:This option uses a more complex approach with filelist() and a loop, which could work, but it's more complex than necessary. Option B achieves the same result more simply and directly.
Therefore,Option Bis the most efficient and straightforward solution, dynamically loading all CSV files from the specified directory while ignoring the log files, as required.

NEW QUESTION # 52
A data architect executes the following script:

What will be the result of Table.A?

Answer: D

Explanation:
In the script provided, there are two tables being loaded inline: Table_A and Table_B. The script uses the Join function to combine Table_B with Table_A based on the common field Field_1. Here's how the join operation works:
* Table_Ainitially contains three records with Field_1 values of 01, 01, and 02.
* Table_Bcontains two records with Field_1 values of 01 and 03.
When Join(Table_A) is executed, Qlik Sense will perform an inner join by default, meaning it will join rows from Table_B to Table_A where Field_1 matches in both tables. The result is:
* For Field_1 = 01, there are two matches in Table_A and one match in Table_B. This results in two records in the joined table where Field_4 and Field_5 values from Table_B are repeated for each match in Table_A.
* For Field_1 = 02, there is no corresponding Field_1 = 02 in Table_B, so the Field_4 and Field_5 values for this record will be null.
* For Field_1 = 03, there is no corresponding Field_1 = 03 in Table_A, so the record from Table_B with Field_1 = 03 is not included in the final joined table.
Thus, the correct output will look like this:
* Field_1 = 01, Field_2 = AB, Field_3 = 10, Field_4 = 30%, Field_5 = 500
* Field_1 = 01, Field_2 = AC, Field_3 = 50, Field_4 = 30%, Field_5 = 500
* Field_1 = 02, Field_2 = AD, Field_3 = 75, Field_4 = null, Field_5 = null

NEW QUESTION # 53
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: D

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 # 54
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 # 55
......

The 24/7 support team is just an e-mail away for our customers so that they can contact us anytime. Our team will solve all of their issues as quickly as possible. Free demos and up to 1 year of free updates of our Qlik Exams are also available at TrainingQuiz. Buy updated and Real QSDA2024 Exam Questions now and earn your dream QSDA2024 certification with TrainingQuiz!

Top QSDA2024 Exam Dumps: https://www.trainingquiz.com/QSDA2024-practice-quiz.html