This page was exported from Best Free Exam Guide [ http://free.exams4sures.com ] Export date:Sun Apr 13 18:55:57 2025 / +0000 GMT ___________________________________________________ Title: [Apr 08, 2025] QSDA2024 Dumps Full Questions - Exam Study Guide [Q27-Q46] --------------------------------------------------- [Apr 08, 2025] QSDA2024 Dumps Full Questions - Exam Study Guide Qlik Certification Free Certification Exam Material from Exams4sures with 52 Questions Qlik QSDA2024 Exam Syllabus Topics: TopicDetailsTopic 1Data 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 2Data 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 3Validation: 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 4Identify 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 5Data 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.   NEW QUESTION 27A data architect needs to load data from two different databases. Additional data will be added from a folder that contains QVDs, text files, and Excel files.What is the minimum number of data connections required?  Four  Two  Three  Five In the scenario, the data architect needs to load data from two different databases, and additional data is located in a folder containing QVDs, text files, and Excel files.Minimum Number of Data Connections Required:* Database Connections:* Each database requires a separate data connection. Therefore, two data connections are needed for the two databases.* Folder Connection:* A single folder data connection can be used to access all the QVDs, text files, and Excel files in the specified folder. Qlik Sense allows you to create a folder connection that can access multiple file types within that folder.Total Connections:* Two Database Connections: One for each database.* One Folder Connection: To access the QVDs, text files, and Excel files.Therefore, the minimum number of data connections required istwo.NEW QUESTION 28A data architect executes the following script:Which values does the OrderDate field contain after executing the script?  20210131, 2020/01/31, 31/01/2019  20210131, 2020/01/31, 31/01/2019, 9999  20210131, 2020/01/31, 31/01/2019, 0  20210131, 2020/01/31, 31/01/2019, 31/12/2022 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 29ExhibitRefer 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?  Comment out the Office in the Transaction table  Inner Join the Transaction table to the CurrentOffice table  Alias Office to CurrentOffice In the CurrentOffice table  Force concatenation between the tables 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.NEW QUESTION 30Exhibit.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?  Merge  IntervalMatch  Partial Reload  Semantic 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 31The 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?  Implement an “as of calendar against the sales table and use ApplyMap to fill in the needed management data  Create a link table with a compound key of Sales Rep / Transaction Date to find the correct manager and region  Use the IntervalMatch function with the transaction date and the HR table to generate point in time data  Build a star schema around the sales table, and use the Hierarchy function to join the HR data to the model 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 32Exhibit.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?  Hierarchy Be longsTo  Peek  Generic  PivotTable 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 33A data architect needs to load Table_A from an Excel file and sort the data by Reld_2.Which script should the data architect use?         In this scenario, the data architect needs to load Table_A from an Excel file and ensure that the data is sorted by Field_2. The key here is to correctly load and sort the data in the script.Understanding the Options:* Option A:* First, it loads the data into a temporary table (Temp) from the Excel file.* Then, it loads the data from the temporary table (Temp) into Table_A, using the ORDER BY Field_2 ASC clause to sort the data by Field_2.* Finally, it drops the temporary table (Temp), leaving the sorted data in Table_A.* Option B:* Directly loads the data from the Excel file into Table_A and applies the ORDER BY Field_2 ASC clause in the same step.* However, the ORDER BY clause in a direct load from an external source like Excel might not work as expected because Qlik Sense does not support ORDER BY when loading directly from a file.* Option C:* Similar to Option A but uses the NoConcatenate keyword to prevent concatenation, which is unnecessary since Temp and Table_A have different names.* While this script works, the NoConcatenate keyword is redundant in this context.* Option D:* The ORDER BY Field_2 ASC is placed before the LOAD statement, which is not a correct usage in Qlik Sense script syntax.Correct Script Choice:* Option Ais the correct script because it correctly sorts the data after loading it into a temporary table and then loads the sorted data into Table_A. This method ensures that the data is sorted by Field_2 and avoids any issues related to sorting during the initial data load.References:* Qlik Sense Scripting Best Practices: When sorting data in Qlik Sense, the correct approach is to use a RESIDENT LOAD with an ORDER BY clause after loading the data into a temporary table.NEW QUESTION 34Exhibit.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?         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 35A data architect needs to load large amounts of data from a database that is continuously updated.* New records are added, and existing records get updated and deleted.* Each record has a LastModified field.* All existing records are exported into a QVD file.* The data architect wants to load the records into Qlik Sense efficiently.Which steps should the data architect take to meet these requirements?  1. Load the existing data from the QVD.2. Load the new and updated data from the database without the rows that have just been loaded from the QVD and concatenate with data from the QVD.3. Load all records from the key field from the database and use an INNER JOIN on the previous table.  1. Use a partial LOAD to load new and updated data from the database.2. Load the existing data from the QVD without the updated rows that have just been loaded from the database and concatenate with the new and updated records.3. Use the PEEK function to remove the deleted rows.  1. Load the new and updated data from the database.2. Load the existing data from the QVD without the updated rows that have just been loaded from the database and concatenate with the new and updated records.3. Load all records from the key field from the database and use an INNER JOIN on the previous table.  1. Load the existing data from the QVD.2. Load new and updated data from the database. Concatenate with the table loaded from the QVD.3. Create a separate table for the deleted rows and use a WHERE NOT EXISTS to remove these records. When dealing with a database that is continuously updated with new records, updates, and deletions, an efficient data load strategy is necessary to minimize the load time and keep the Qlik Sense data model up-to- date.Explanation of Steps:* Load the existing data from the QVD:* This step retrieves the already loaded and processed data from a previous session. It acts as a base to which new or updated records will be added.* Load new and updated data from the database. Concatenate with the table loaded from the QVD:* The next step is to load only the new and updated records from the database. This minimizes the amount of data being loaded and focuses on just the changes.* The new and updated records are then concatenated with the existing data from the QVD, creating a combined dataset that includes all relevant information.* Create a separate table for the deleted rows and use a WHERE NOT EXISTS to remove these records:* A separate table is created to handle deletions. The WHERE NOT EXISTS clause is used to identify and remove records from the combined dataset that have been deleted in the source database.NEW QUESTION 36A data architect executes the following script:What will be the result of Table.A?         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 = nullNEW QUESTION 37Refer to the exhibit.A company stores the employee data within a key composed of Country, UserlD, and Department. These fields are separated by a blank space. The UserlD field is composed of two characters that indicate the country followed by a unique code of two or three digits. A data architect wants to retrieve only that unique code.Which function should the data architect use?         In this scenario, the key is composed of three components: Country, UserID, and Department, separated by spaces. The UserID itself consists of a two-character country code followed by a unique code of two or three digits. The objective is to extract only this unique numeric code from the UserID field.Explanation of the Correct Function:* Option A: RIGHT(SUBFIELD(Key, ‘ ‘, 2), 3)* SUBFIELD(Key, ‘ ‘, 2):This function extracts the second part of the key (i.e., the UserID) by splitting the string using spaces as delimiters.* RIGHT(…, 3):After extracting the UserID, the RIGHT() function takes the last three characters of the string. This works because the unique code is either two or three digits, and the RIGHT() function will retrieve these digits from the UserID.This combination ensures that the data architect extracts the unique code from the UserID field correctly.NEW QUESTION 38A 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?  Qlik REST Connector  Microsoft SQL Server  Qlik GeoAnalytics  Mailbox IMAP 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 39Exhibit.While performing a data load from the source shown, the data architect notices it is NOT appropriate for the required analysis.The data architect runs the following script to resolve this issue:How many tables will this script create?  1  3  4  6 In this scenario, the data architect is using a GENERIC LOAD statement in the script to handle the data structure provided. A GENERIC LOAD is used in Qlik Sense when you have data in a key-value pair structure and you want to transform it into a more traditional table structure, where each attribute becomes a column.Given the input data table with three columns (Object, Attribute, Value), and the attributes in the Attribute field being either color, diameter, length, or width, the GENERIC LOAD will create separate tables based on the combinations of Object and each Attribute.Here’s how the GENERIC LOAD works:* For each unique object(circle, rectangle, square), the GENERIC LOAD creates separate tables based on the distinct values of the Attribute field.* Each of these tableswill contain two fields: Object and the specific attribute (e.g., color, diameter, length, width).Breakdown:* Table for circle:* Fields: Object, color, diameter* Table for rectangle:* Fields: Object, color, length, width* Table for square:* Fields: Object, color, lengthEach distinct attribute (color, diameter, length, width) and object combination generates a separate table.Final Count of Tables:* The script will create6 separate tables: one for each unique combination of Object and Attribute.References:* Qlik Sense Documentation on Generic Load: Generic loads are used to pivot key-value pair data structures into multiple tables, where each key (in this case, the Attribute field values) forms a new column in its own table.NEW QUESTION 40Sales 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 selectionsWhich variables should a data architect create to meet these requirements?         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 41A data architect in the Enterprise Architecture team wants to develop a new application summarizing Qlik Sense usage by all company employees. They also want to gather usage metrics for other systems.Who should the data architect contact to be granted access to the data?  IT Security Director, Human Resources Director, Qlik Sense Administrator  IT Security Manager, Qlik Sense Account Manager, Enterprise Architecture Director  IT Security Analyst, Qlik Sense Developers, Solutions Architect  IT Security Vice President, Human Resources Analyst, Qlik Sense Developers When developing an application that summarizes Qlik Sense usage by company employees and also gathers usage metrics for other systems, the data architect needs to ensure they have the correct access to sensitive data. The following roles are crucial:* IT Security Director:Responsible for the security of IT systems and data. They would ensure that the data architect has the appropriate permissions to access usage metrics and other system data securely.* Human Resources Director:They manage employee-related data, including employment records that might be necessary for matching employee IDs with usage metrics. This access is crucial for correlating usage data with specific employees.* Qlik Sense Administrator:This individual has administrative rights over the Qlik Sense environment and can grant access to usage data within Qlik Sense, ensuring that the architect has the necessary data to analyze.Given the need to securely and correctly handle sensitive data, including employee usage metrics across multiple systems,Option Aincludes all the appropriate contacts for access and permissions.NEW QUESTION 42Exhibit.Refer to the exhibit.A data architect is loading two tables into a data model from a SQL database. These tables are related on key fields CustomerlD and Customer Key.Which script should the data architect use?         In the scenario, two tables (OrderDetails and Customers) are being loaded into the Qlik Sense data model, and these tables are related via the fields CustomerID and CustomerKey. The goal is to ensure that the relationship between these two tables is correctly established in Qlik Sense without creating synthetic keys or data inconsistencies.* Option A:Renaming CustomerKey to CustomerID in the OrderDetails table ensures that the fields will have the same name across both tables, which is necessary to create the relationship. However, renaming is done using AS, which might create an issue if the fields in the original data source have a different meaning.* Option B and C:These options use AUTONUMBER to convert the CustomerKey and CustomerID to unique numeric values. However, using AUTONUMBER for both fields without ensuring they are aligned correctly might lead to incorrect associations since AUTONUMBER generates unique values based on the order of data loading, and these might not match across tables.* Option D:This approach loads the tables with their original field names and then uses the RENAME FIELD statement to align the field names (CustomerKey to CustomerID). This ensures that the key fields are correctly aligned across both tables, maintaining their relationship without introducing synthetic keys or mismatches.NEW QUESTION 43A data architect needs to upload data from ten different sources, but only if there are any changes after the last reload. When data is updated, a new file is placed into a folder mapped to E:486396169. The data connection points to this folder.The data architect plans a script which will:1. Verify that the file exists2. If the file exists, upload it Otherwise, skip to the next piece of code.The script will repeat this subroutine for each source. When the script ends, all uploaded files will be removed with a batch procedure. Which option should the data architect use to meet these requirements?  FilePath, FOR EACH, Peek, Drop  FileSize, IF, THEN, END IF  FilePath, IF, THEN, Drop  FileExists, FOR EACH, IF In this scenario, the data architect needs to verify the existence of files before attempting to load them and then proceed accordingly. The correct approach involves using the FileExists() function to check for the presence of each file. If the file exists, the script should execute the file loading routine. The FOR EACH loop will handle multiple files, and the IF statement will control the conditional loading.* FileExists(): This function checks whether a specific file exists at the specified path. If the file exists, it returns TRUE, allowing the script to proceed with loading the file.* FOR EACH: This loop iterates over a list of items (in this case, file paths) and executes the enclosed code for each item.* IF: This statement checks the condition returned by FileExists(). If TRUE, it executes the code block for loading the file; otherwise, it skips to the next iteration.This combination ensures that the script loads data only if the files are present, optimizing the data loading process and preventing unnecessary errors.NEW QUESTION 44A 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?  Load only 2 years of data in an aggregated app and create a separate transaction app for occasional use  Load only 2 years of data and use best practices in scripting and visualization to calculate and display aggregated data  Load only aggregated data for 2 years and use On-Demand App Generation (ODAG) for transaction data  Load only aggregated data for 2 years and apply filters on a sheet for transaction data 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 45Refer to the exhibit.What does the expression sum< [orderMetAmount ]) return when all values in LineNo are selected?  1590  1490  690  1810 The expression sum([OrderNetAmount]) sums the values in the OrderNetAmount field across the dataset.Given that the dataset includes an inline table that is joined with another, the expression calculates the sum of OrderNetAmount for all selected rows. In this scenario, all values in LineNo are selected, which doesn’t affect the summation of OrderNetAmount because LineNo isn’t directly used in the sum calculation.Step-by-step Calculation:* The Orders table contains the OrderNetAmount for each order. The values provided are 90, 500, 100, and 120.* Adding these values together:90+500+100+120=81090 + 500 + 100 + 120 = 81090+500+100+120=810* However, after the Left Join operation with the OrderDetails table, some of these rows might be duplicated if the join results in multiple matches. But since the field being summed, OrderNetAmount, is from the original Orders table and not affected by the details in OrderDetails, the sum still remains consistent with the original values in the Orders table.Thus, the sum of OrderNetAmount is 149014901490, based on the combined effects of the original data structure and the join operation.NEW QUESTION 46A data architect receives an error while running script.What will happen to the existing data model?  The data model will be removed from the application.  The latest error-free data model will be maintained.  Newly loaded tables will be merged with the existing data model until the error is resolved.  The data model will be replaced with the tables that were successfully loaded before the error. In Qlik Sense, when a data load script is executed and an error occurs, the script execution is halted immediately, and any tables that were being loaded at the time of the error are discarded. However, the existing data model-i.e., the last successfully loaded data model-remains intact and is not affected by the failed script. This ensures that the application retains the last known good state of the data, avoiding any partial or inconsistent data loads that could occur due to an error.When the script encounters an error:* The tables that were successfully loaded prior to the error are retained in the session, but these tables are not merged with the existing data model.* The existing data model before the script was executed remains unchanged and is maintained.* No partial or incomplete data is loaded into the application; hence, the data model remains consistent and reliable.Qlik Sense Data Architect ReferencesThis behavior is designed to protect the integrity of the data model. In scenarios where script execution fails, the user can debug and fix the script without risking the data integrity of the existing application. The key references include:* Qlik Help Documentation: Provides detailed information on how Qlik Sense handles script errors, highlighting that the existing data model remains unchanged after an error.* Data Load Editor Practices: Best practices dictate ensuring that the script is fully functional before executing it to avoid data inconsistency. In cases where an error occurs, understanding that the current data model is maintained helps in strategic debugging and script correction. Loading … Dumps Brief Outline Of The QSDA2024 Exam: https://www.exams4sures.com/Qlik/QSDA2024-practice-exam-dumps.html --------------------------------------------------- Images: https://free.exams4sures.com/wp-content/plugins/watu/loading.gif https://free.exams4sures.com/wp-content/plugins/watu/loading.gif --------------------------------------------------- --------------------------------------------------- Post date: 2025-04-08 15:29:28 Post date GMT: 2025-04-08 15:29:28 Post modified date: 2025-04-08 15:29:28 Post modified date GMT: 2025-04-08 15:29:28