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質問 # 13
A 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 exists
2. 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?
- A. FilePath, FOR EACH, Peek, Drop
- B. FilePath, IF, THEN, Drop
- C. FileSize, IF, THEN, END IF
- D. FileExists, FOR EACH, IF
正解：D
質問 # 14
exhibit.
A data architect is validating that the script section, as shown in the exhibit, is working properly. They need to stop the script with a preview of the value used with the Load statement.
Where should the data architect put the debugger breakpoint?
- A.
- B.
- C.
- D.
正解：A
解説：
In this scenario, the data architect needs to validate the script and specifically ensure that the vMaxDate variable is being correctly utilized in the LOAD statement. The goal is to stop the script execution at a point where the variable's value can be previewed.
Understanding the Options:
* Option Aplaces the breakpoint just after the assignment of the variable vMaxDate in the Where clause but before any data is loaded.
* Option B, C, and Drepresent placements of the breakpoint after the LOAD statement begins processing the Resident table, which means that the variable vMaxDate would have already been utilized.
Correct Breakpoint Placement:
* Option Ais the correct choice because placing the breakpoint at this point allows you to preview the value of vMaxDate right before it is used in the Where clause. This placement ensures that the script execution halts before loading the data, allowing you to validate whether vMaxDate is correctly defined and whether it correctly filters the data based on the [Date] field.
* If the breakpoint were placed after the LOAD statement (as in Options B, C, or D), the script would have already attempted to load the data, making it too late to inspect the variable's value before it's used.
References:
* Qlik Sense Debugging Best Practices: When debugging, it is crucial to set breakpoints before the execution of a critical operation where the values of variables or fields are used to ensure that they hold the expected data.
質問 # 15
A data architect needs to write the expression for a measure on a KPI to show the sales person with the highest sales. The sort order of the values of the fields is unknown. When two or more sales people have sold the same amount, the expression should return all of those sales people.
Which expression should the data architect use?
- A.
- B.
- C.
- D.
正解：A
解説：
The requirement is to create a measure that identifies the salesperson with the highest sales. If multiple salespeople have the same highest sales amount, the measure should return all of those salespeople.
Explanation of Option A:
* Rank(Sum(Sales), 1):The Rank() function is used to rank salespersons based on the sum of their sales.
The rank 1 indicates the top position.
* Aggr() Function:This function aggregates the data and returns the results grouped by the SalesPerson field.
* IF() Condition:The IF condition checks if the salesperson's rank is 1 (highest sales).
* Concat(DISTINCT ...):The Concat() function concatenates all the salespersons who have the highest sales, separated by spaces or another delimiter, ensuring that all top performers are returned.
Example:
If three salespersons have the highest sales, this expression will return all three names separated by a space.
質問 # 16
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, 0
- C. 20210131, 2020/01/31, 31/01/2019, 31/12/2022
- D. 20210131, 2020/01/31, 31/01/2019
正解：C
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 # 17
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 aggregated data for 2 years and apply filters on a sheet for transaction data
- 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 in an aggregated app and create a separate transaction app for occasional use
- D. Load only 2 years of data and use best practices in scripting and visualization to calculate and display aggregated data
正解：B
質問 # 18
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, 'M/D/YYYY hh.mm.ff') AS OrderDate in both the table "Orders" and "Master Calendar"
- B. Date(OrderDate) AS OrderDate in both the table "Orders" and "Master Calendar"
- C. DatefFloor(OrderDate)) AS OrderDate in both the table "Orders" and "Master Calendar"
- D. TimeStamp(OrderDate) AS OrderDate in both the table "Orders" and "Master Calendar"
正解：B
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 # 19
A company's analytics team is migrating from QlikView to Qlik Sense. During the transition there is an opportunity to improve overall reporting.
Which set of criteria must the data architect consider while planning for the migration?
- A. QlikView archival, source data architecture, load script, data model, business use case
- B. Application metadata, application theme, user sessions, load script, IT use case
- C. User sessions, source data architecture, compatibility, data model, business use case
- D. Application size, application theme, storytelling, data model, IT use case
正解：A
解説：
During the transition from QlikView to Qlik Sense, the analytics team has the opportunity to improve the overall reporting. To ensure a smooth migration while optimizing the new environment, the data architect needs to consider several key factors.
Option Cis the best choice because it encompasses the essential aspects of a migration project:
* QlikView Archival:
* Archiving QlikView applications is crucial to ensure that historical data and applications are preserved and can be referenced if needed in the future. This step is important to maintain continuity and provide a fallback option if required during the transition.
* Source Data Architecture:
* Understanding the existing source data architecture is critical to ensure that the new Qlik Sense applications can seamlessly connect to the data sources. This also helps in identifying opportunities to optimize or re-architect the data pipelines for better performance in Qlik Sense.
* Load Script:
* The load script from QlikView might need to be revised or optimized for Qlik Sense. It's important to ensure that the script is compatible and takes advantage of Qlik Sense's capabilities, such as improved data handling, better inline transformations, and enhanced scripting functions.
* Data Model:
* Reviewing and possibly redesigning the data model is essential during the migration. Qlik Sense's associative engine allows for more flexibility, and this is an opportunity to improve the data model for better performance, scalability, and user experience.
* Business Use Case:
* Understanding the business use case is vital to ensure that the new Qlik Sense applications meet the business requirements effectively. This includes making sure that the new reports and dashboards are aligned with the business goals and provide the necessary insights.
References:
* Qlik Migration Guide: When migrating from QlikView to Qlik Sense, it's important to consider not just the technical aspects but also the business implications and opportunities for improvement.
* Qlik Documentation on Data Modeling and Load Script Optimization: These resources provide best practices on how to optimize load scripts and data models during migration to ensure smooth operation and better performance in Qlik Sense.
質問 # 20
A data architect implements Section Access on an app to reduce the data for each user when the user logs in.
Each user is allowed to see their specific territory only.
The app is set for a scheduled reload every three hours. Without Section Access added, the app loads successfully. When Section Access is added and the script runs, the app fails to load.
What is causing this issue?
- A. A user name listed in the Section Access table is spelled incorrectly.
- B. The data architect does not have rights to reload the app.
- C. The ACCESS Column in the Section Access table has been added in lowercase.
- D. The service account running the task is not included in the Section Access table.
正解：D
解説：
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
質問 # 21
Exhibit.
Refer to the exhibit.
A data architect is loading the tables and a synthetic key is generated.
How should the data architect resolve the synthetic key?
- A. Create a composite key using OrderlD and LineNo, and remove OrderlD and LineNo from Shipments
- B. Create a composite key using OrderlD and UneNo
- C. Remove the LineNo field from both tables and use the AutoNumber function on the OrderlD field
- D. Remove the LineNo field from Shipments and use the AutoNumber function on the OrderlD field
正解：B
解説：
In this scenario, the data architect is loading two tables, Orders and Shipments, into Qlik Sense, and a synthetic key is being generated due to the presence of shared fields (OrderID and LineNo) between these tables.
Understanding the Issue:
* Synthetic Keys: Qlik Sense automatically creates synthetic keys when two or more tables share multiple fields with the same names. While synthetic keys aren't necessarily problematic, they can sometimes lead to incorrect or unexpected data associations and should be resolved when possible to maintain clarity and control over the data model.
* The tables Orders and Shipments share the fields OrderID and LineNo. In this context, these fields together uniquely identify each record, so they are both necessary for accurate data linkage.
Correct Resolution Approach:
Option C: Create a composite key using OrderID and LineNois the best approach.
Here's why:
* Composite Key Creation:
* By creating a composite key that combines OrderID and LineNo (e.g., OrderID & '-' & LineNo), you ensure that each line in the orders and shipments tables is uniquely identified. This composite key will accurately link the related records from the Orders and Shipments tables.
* Avoiding Synthetic Keys:
* By manually creating this composite key, you eliminate the need for Qlik Sense to generate a synthetic key, thereby simplifying the data model and ensuring that data associations are clear and controlled.
* Retaining Both Fields:
* This approach allows you to keep both OrderID and LineNo as separate fields in your tables if needed for other analyses or reporting purposes, while using the composite key for linking the tables.
References:
* Qlik Sense Data Modeling Best Practices: When dealing with multiple fields that are used together to uniquely identify records, it is recommended to create composite keys rather than relying on Qlik Sense's synthetic keys for clarity and better control.
質問 # 22
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 # 23
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. Inner Join the Transaction table to the CurrentOffice table
- B. Force concatenation between the tables
- C. Alias Office to CurrentOffice In the CurrentOffice table
- D. Comment out the Office in the Transaction table
正解：C
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 # 24
A table is generated resulting from the following script:
When the data architect selects a date, some, but NOT all, orders for that date are shown.
How should the data architect modify the script to show all orders for the selected date?
- A.
- B.
- C.
- D.
正解：A
解説：
The issue described is that not all orders for a selected date are shown. This issue arises because the original script uses the Date(OrderTime) function, which only extracts the date part of the OrderTime timestamp, potentially resulting in incorrect matching when filtering by date due to the time component still being present in the underlying data.
Explanation of Option D:
* Floor(OrderTime): The Floor() function truncates the OrderTime timestamp to remove the time component, leaving only the date part. This ensures that all orders on the same date are treated equally, without any interference from the time component.
* Date(Floor(OrderTime), 'YYYY-MM-DD'): The Date() function formats the floored value into a date format (YYYY-MM-DD), which is essential for consistent date comparison.
This approach ensures that when you select a date in the application, all orders for that date are shown, as the time component has been effectively removed.
質問 # 25
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：A
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 # 26 
Refer to the exhibit.
A data architect needs to create a data model for a new app. Users must be able to see:
* Total sales for each customer
* Total sales for a given state
* Customers that have not had any sales
* Names of salesperson and regional account managers
* Total number of sales by date
Which steps should the data architect perform to meet these requirements?
Which steps should the data architect perform to meet these requirements?
- A. 1. Load the Sales table
2. Load the Customers table
3. Load the Employees table twice; name it and alias the EmployeelD field appropriately each time - B. 1. Load the Customers table and alias the CustID field as CustomerlD
2. Use a Mapping Load for the Employees table
3. Load the Sales table and use ApplyMap to get the names for SalesPersonID and RegionalAcctMgrlD - C. 1. Use a Mapping Load for the Employees table
2. Load the Sales table and use ApplyMap to get the names for SalesPersonID and RegionalAcctMgrlD
3. Use a Left Join Load to add the customer details for the Sales table - D. 1. Load the Customers table and alias the CustID field as CustomerlD
2. Load the Employees table
3. Load the Sales table and alias the SalesPersonID and RegionalAcctMgrlD fields as EmployeelD
正解：A
解説：
In the provided scenario, the data architect needs to create a data model that supports various analyses, including total sales for each customer, total sales by state, identifying customers with no sales, and displaying the names of salespersons and regional account managers.
Here's whyOption Cis the correct choice:
* Loading the Sales Table:The Sales table contains key information related to sales transactions, including SaleID, CustomerID, Amount, SaleDate, SalesPersonID, and RegionalAcctMgrID. This table must be loaded first as it will be central to the analysis.
* Loading the Customers Table:The Customers table includes customer details such as CustID, CustName, Address, City, State, and Zip. Loading this table and linking it to the Sales table via the CustomerID field allows you to perform analyses such as total sales per customer and total sales by state. Importantly, loading the customers separately will also allow the identification of customers without any sales.
* Loading the Employees Table Twice:The Employees table must be loaded twice because it is used to look up two different roles in the sales process: the SalesPersonID and the RegionalAcctMgrID. When loading the table twice:
* The first instance of the Employees table will be used to map the SalesPersonID to EmployeeName.
* The second instance will be used to map the RegionalAcctMgrID to EmployeeName.
* Aliasing the EmployeeID field appropriately in each instance is crucial to prevent creating synthetic keys and to ensure the correct association with the roles in the sales process.
This approach ensures that the data model will correctly support all the required analyses, including identifying customers without sales, which is crucial for meeting the business requirements.
* Option AandOption Bpropose using a mapping load and ApplyMap, which can complicate the model and does not directly address all the business requirements.
* Option Dinvolves aliasing fields in a way that could create unnecessary complexity and might not accurately reflect the relationships in the data.
Thus,Option Cis the correct answer as it best meets the requirements while maintaining a clear and functional data model.
質問 # 27
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：D
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 # 28
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. Implement incremental load on each database using QVD files
- B. Implement ODAG to split out the app into smaller chunks
- C. Make sure the individual reload tasks in the QMC are not running in parallel
- D. Implement Direct Discovery with partial load
正解：A
解説：
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.
質問 # 29
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. Use the IntervalMatch function with the transaction date and the HR table to generate point in time data
- B. Create a link table with a compound key of Sales Rep / Transaction Date to find the correct manager and region
- C. Implement an "as of calendar against the sales table and use ApplyMap to fill in the needed management data
- D. Build a star schema around the sales table, and use the Hierarchy function to join the HR data to the model
正解：A
解説：
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.
質問 # 30
A data architect needs to develop three separate apps (Sales, Finance, and Operations). The three apps share numerous identical calculation expressions.
The goals include:
* Reducing duplicate script
* Saving time on expression modifications
* Increasing reusable Qlik developer assets.
The data architect creates a common script and stores it on a file server that Qlik Sense can access. How should the data architect complete the requirements?
- A. Call batch file
- B. Execute server script
- C. Include script function
- D. Macro on server
正解：C
解説：
When developing multiple Qlik Sense applications (Sales, Finance, Operations) that share numerous identical calculation expressions, it is crucial to have a centralized, reusable script to avoid redundancy, save time on modifications, and increase the reusability of the assets.
The best approach in Qlik Sense to achieve these goals is to use theIncludescript function. This function allows the data architect to reference a script file that is stored on a file server. The Include function willinject the contents of the external script file into the Qlik Sense script at the point where the Include statement is called. This means that all three apps (Sales, Finance, Operations) can include this common script, and any updates made to the script will automatically apply to all apps that include it.
This method provides a highly maintainable solution because:
* No Duplicate Script:The shared logic is maintained in a single file, eliminating redundancy.
* Ease of Modifications:Any changes made to the script are propagated to all applications that include it.
* Reusable Assets:The script can be reused across different applications, enhancing efficiency and consistency.
質問 # 31
Exhibit.
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?
- A. 0
- B. 1
- C. 2
- D. 3
正解：A
解説：
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, length
Each 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.
質問 # 32
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 aggregated data for 2 years and apply filters on a sheet for transaction data
- 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 in an aggregated app and create a separate transaction app for occasional use
- D. Load only 2 years of data and use best practices in scripting and visualization to calculate and display aggregated data
正解：B
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 # 33 
Refer to the exhibit.
A data architect needs to load data from Customers.qvd and sort the Country field in ascending order. Which method should be used?
- A. Insert an Order By clause after the FROM clause in the CustTemp table
- B. Insert a Group By clause into the LOAD statement for the CustTemp table after the FROM clause
- C. Perform a Resident LOAD of the CustTemp table and insert an Order By clause in this table
- D. Move the Country field to the first position in the field list in the LOAD statement
正解：C
解説：
When loading data from a QVD file into a Qlik Sense application, if you need to sort the data by a specific field (in this case, the Country field), the Order By clause can be used. However, the Order By clause cannot be directly applied during the initial load from the QVD. Instead, the data should first be loaded into a temporary table and then sorted in a subsequent resident load.
* Initial Load from QVD:The data is first loaded into a temporary table (CustTemp) without any sorting.
* Resident Load with Order By:After the initial load, you perform a Resident Load from the CustTemp table and apply the Order By clause to sort the data by the Country field in ascending order.
LOAD
Address,
City,
CompanyName,
ContactName,
Country,
_CustomerID,
DivisionID,
DivisionName,
Fax,
Phone,
PostalCode,
StateProvince
RESIDENT CustTemp
ORDER BY Country;
This method ensures that the data is sorted correctly without violating Qlik Sense's loading rules.
質問 # 34
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