[2024年12月] 実際問題を使ってQSDA2024無料問題集サンプルと問題と練習テストエンジン [Q21-Q39]

[2024年12月] 実際問題を使ってQSDA2024無料問題集サンプルと問題と練習テストエンジン

合格させるQlik QSDA2024試験問題でテスト復刻エンジンとPDF

質問 # 21
Exhibit.

A chart for monthly hospital admissions and discharges incorrectly displays the month and year values on the x-axis.
The date format for the source data field "Common Date" is M/D/YYYY. This format was used in a calculated field named "Month-Year" in the data manager when the data model was first built.
Which expression should the data architect use to fix this issue?

• A. Date(InMontht[Common Date]),'MMM-YYYY')
• B. Date([Comraon Date],'MMM-YYYY')
• C. Date(MonthStart([Common Date]),'MMM-YYYY')
• D. Date(MonthsStart([Common Date]),'VMM-YYYY')

正解：C

解説：
The issue described relates to the incorrect display of month and year values on the x-axis of a chart. The source data has dates in the M/D/YYYY format, and a calculated field named Month-Year was created using this date format.
To correct the issue:
* The correct approach is to use the MonthStart() function, which returns the first date of the month for the provided date. This ensures consistency in month-year representation.
* The Date() function is then used to format the result of MonthStart() to the desired format of MMM- YYYY (e.g., Feb-2018).
Explanation of the Correct Expression:
* MonthStart([Common Date]): This ensures that all dates within a month are treated as the first day of that month, which is critical for accurate monthly aggregation.
* Date(..., 'MMM-YYYY'): This formats the result to show just the month and year in the correct format.
Using this expression ensures that the x-axis correctly displays the month-year values.

質問 # 22

Refer to the exhibit.
What does the expression sum< [orderMetAmount ]) return when all values in LineNo are selected?

• A. 0
• B. 1
• C. 2
• D. 3

正解：A

解説：
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.

質問 # 23
A data architect needs to retrieve data from a REST API. The data architect needs to loop over a series of items that are being read using the REST connection.
What should the data architect do?

• A. Use pagination of the REST Connector to create a template of the desired data
• B. Use With Connection to pass a parameter to the REST URL
• C. Recreate the SQL Statement with the correct parameters
• D. Use the REST Connector with pagination mechanism

正解：D

解説：
When retrieving data from a REST API, particularly when the dataset is large or the data is segmented across multiple pages (which is common in REST APIs), the REST Connector in Qlik Sense needs to be configured to handle pagination.
Pagination is the process of dividing the data retrieved from the API into pages that can be loaded sequentially or as required. Qlik Sense's REST Connector supports pagination by allowing the dataarchitect to set parameters that will sequentially retrieve each page of data, ensuring that the complete dataset is retrieved.
Key Steps:
* REST Connector Setup: Configure the REST connector in Qlik Sense and specify the necessary API endpoint.
* Pagination Mechanism: Use the built-in pagination mechanism to define how the connector should retrieve the subsequent pages (e.g., by using query parameters like page or offset).

質問 # 24
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. Peek
• B. Generic
• C. Hierarchy Be longsTo
• D. PivotTable

正解：B

解説：
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.

質問 # 25
A data architect executes the following script:

What will be the result of Table.A?

• A.
• B.
• C.
• D.

正解：D

解説：
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

質問 # 26
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. Alias Office to CurrentOffice In the CurrentOffice table
• B. Comment out the Office in the Transaction table
• C. Inner Join the Transaction table to the CurrentOffice table
• D. Force concatenation between the tables

正解：A

解説：
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.

質問 # 27
A data architect wants reflect a value of the variable in the script log for tracking purposes. The variable is defined as:

Which statement should be used to track the variable's value?

• A.
• B.
• C.
• D.

正解：B

解説：
In Qlik Sense, the TRACE statement is used to print custom messages to the script execution log. To output the value of a variable, particularly one that is dynamically assigned, the correct syntax must be used to ensure that the variable's value is evaluated and displayed correctly.
* The variable vMaxDate is defined with the LET statement, which means it is evaluated immediately, and its value is stored.
* When using the TRACE statement, to output the value of vMaxDate, you need to ensure the variable's value is expanded before being printed. This is done using the $() expansion syntax.
* The correct syntax is TRACE #### $(vMaxDate) ####; which evaluates the variable vMaxDate and inserts its value into the log output.
Key Qlik Sense Data Architect References:
* Variable Expansion:In Qlik Sense scripting, $(variable_name) is used to expand and insert the value of the variable into expressions or statements. This is crucial when you want to output or use the value stored in a variable.
* TRACE Statement:The TRACE command is used to write messages to the script log. It is commonly used for debugging purposes to track the flow of script execution or to verify the values of variables during script execution.

質問 # 28
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 2 years of data and use best practices in scripting and visualization to calculate and display aggregated data
• C. Load only aggregated data for 2 years and apply filters on a sheet for transaction data
• D. Load only aggregated data for 2 years and use On-Demand App Generation (ODAG) for transaction data

正解：D

解説：
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.

質問 # 29
Refer to the exhibit.

A data architect needs to build a dashboard that displays the aggregated sates for each sales representative. All aggregations on the data must be performed in the script.
Which script should the data architect use to meet these requirements?

• A.
• B.
• C.
• D.

正解：D

解説：
The goal is to display the aggregated sales for each sales representative, with all aggregations being performed in the script. Option C is the correct choice because it performs the aggregation correctly using a Group by clause, ensuring that the sum of sales for each employee is calculated within the script.
* Data Load:
* The Data table is loaded first from the Sales table. This includes the OrderID, OrderDate, CustomerID, EmployeeID, and Sales.
* Next, the Emp table is loaded containing EmployeeID and EmployeeName.
* Joining Data:
* A Left Join is performed between the Data table and the Emp table on EmployeeID, enriching the data with EmployeeName.
* Aggregation:
* The Summary table is created by loading the EmployeeName and calculating the total sales using the sum([Sales]) function.
* The Resident keyword indicates that the data is pulled from the existing tables in memory, specifically the Data table.
* The Group by clause ensures that the aggregation is performed correctly for each EmployeeName, summarizing the total sales for each employee.
Key Qlik Sense Data Architect References:
* Resident Load: This is a method to reuse data that is already loaded into the app's memory. By using a Resident load, you can create new tables or perform calculations like aggregation on the existing data.
* Group by Clause: The Group by clause is essential when performing aggregations in the script. It groups the data by specified fields and performs the desired aggregation function (e.g., sum, count).
* Left Join: Used to combine data from two tables. In this case, Left Join is used to enrich the sales data with employee names, ensuring that the sales data is associated correctly with the respective employee.
Conclusion:Option C is the most appropriate script for this task because it correctly performs the necessary joins and aggregations in the script. This ensures that the dashboard will display the correct aggregated sales per employee, meeting the data architect's requirements.

質問 # 30
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. Qlik REST Connector
• B. Mailbox IMAP
• C. Microsoft SQL Server
• D. Qlik GeoAnalytics

正解：A

解説：
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.

質問 # 31
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?

• A.
• B.
• C.
• D.

正解：B

解説：
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.

質問 # 32
A 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?

• A. IT Security Director, Human Resources Director, Qlik Sense Administrator
• B. IT Security Vice President, Human Resources Analyst, Qlik Sense Developers
• C. IT Security Manager, Qlik Sense Account Manager, Enterprise Architecture Director
• D. IT Security Analyst, Qlik Sense Developers, Solutions Architect

正解：A

解説：
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.

質問 # 33
A 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?

• A.
• B.
• C.
• D.

正解：A

解説：
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.

質問 # 34
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, 9999
• B. 20210131, 2020/01/31, 31/01/2019, 31/12/2022
• C. 20210131, 2020/01/31, 31/01/2019
• D. 20210131, 2020/01/31, 31/01/2019, 0

正解：B

解説：
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'.

質問 # 35
Refer 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?

• A.
• B.
• C.
• D.

正解：B

解説：
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.

質問 # 36
Exhibit.

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?

• A.
• B.
• C.
• D.

正解：B

解説：
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.

質問 # 37
Exhibit.

Refer to the exhibit.
A business analyst informs the data architect that not all analysis types over time show the expected data.
Instead they show very little data, if any.
Which Qlik script function should be used to resolve the issue in the data model?

• A. TimeStamp(OrderDate) AS OrderDate in both the table "Orders" and "Master Calendar"
• B. TimeStamp#(OrderDate, 'M/D/YYYY hh.mm.ff') AS OrderDate in both the table "Orders" and "Master Calendar"
• C. DatefFloor(OrderDate)) AS OrderDate in both the table "Orders" and "Master Calendar"
• D. Date(OrderDate) AS OrderDate in both the table "Orders" and "Master Calendar"

正解：D

解説：
In the provided data model, there is an issue where certain types of analysis over time are not showing the expected data. This problem is often caused by a mismatch in the data formats of the OrderDate field between the Orders and MasterCalendar tables.
* Option A:DatefFloor(OrderDate)) would round down to the nearest date boundary, which might not address the root cause if the issue is related to different date and time formats.
* Option B:TimeStamp#(OrderDate, 'M/D/YYYY hh.mm.ff') ensures that the date is interpreted correctly as a timestamp, but this does not resolve potential mismatches in date format directly.
* Option C:TimeStamp(OrderDate) will keep both date and time, which may still cause mismatches if the MasterCalendar is dealing purely with dates.
* Option D:Date(OrderDate) formats the OrderDate to show only the date portion (removing the time part). This function will ensure that the date values are consistent across the Orders and MasterCalendar tables by converting the timestamps to just dates. This is the most straightforward and effective way to ensure consistency in date-based analysis.
In Qlik Sense, dates and timestamps are stored as dual values (both text and numeric), and mismatches can lead to incomplete or incorrect analyses. By using Date(OrderDate) in both the Orders and MasterCalendar tables, you ensure that the analysis will have consistent date values, resolving the issue described.

質問 # 38
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 2 years of data and use best practices in scripting and visualization to calculate and display aggregated data
• C. Load only aggregated data for 2 years and apply filters on a sheet for transaction data
• D. Load only aggregated data for 2 years and use On-Demand App Generation (ODAG) for transaction data

正解：D

質問 # 39
......

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