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質問 # 14
A Chief Information Officer has hired Qlik to enhance the organization's inventory analytics. In the initial meeting, the client's focus was determined to be forecasting inventory levels.
Which stakeholder should be consulted first when gathering requirements?
- A. Chief Information Officer
- B. Vice President of Marketing
- C. SQL Developer
- D. Product Buyer
正解：D
解説：
In this scenario, the focus of the project is to enhance inventory analytics, specifically targeting forecasting inventory levels. The primary goal is to understand the factors influencing inventory management and to build a model that helps in predicting future inventory needs.
Option A: Product Buyeris the correct stakeholder to consult first.
Here's why:
* Direct Involvement in Inventory Management:
* The Product Buyer is typically responsible for making decisions related to purchasing and maintaining inventory levels. They have a deep understanding of the factors that influence inventory needs, such as lead times, supplier reliability, demand forecasting, and purchasing cycles.
* Knowledge of Inventory Requirements:
* Since the project's primary focus is forecasting inventory levels, the Product Buyer will provide crucial insights into the variables that affect inventory and the data needed for accurate forecasting. They can guide what historical data is essential and what external factors might need to be considered in the forecasting model.
* Alignment with Business Objectives:
* By consulting the Product Buyer, the project can ensure that the inventory forecasting models align with the company's inventory management objectives, avoiding overstocking or understocking, and thus optimizing costs.
References:
* Qlik Project Management Best Practices: In analytics projects, particularly those focused on specific operational areas like inventory management, consulting the stakeholders who are closest to the operational data and decision-making processes ensures that the solution will be relevant and effective.
質問 # 15 
Refer to the exhibits.
On executing a load script of an app, the country field needs to be normalized. The developer uses a mapping table to address the issue. The script runs successfully but the resulting table is not correct.
What should the data architect do?
- A. Use LOAD DISTINCT on the mapping table
- B. Review the values of the source mapping table
- C. Use a LEFT JOIN Instead of the APPLYMAP
- D. Create two different mapping tables
正解：B
解説：
In this scenario, the issue arises from using the applymap() function to normalize the country field values, but the result is incorrect. The reason is most likely related to the values in the source mapping table not matching the values in the Fact_Table properly.
The applymap() function in Qlik Sense is designed to map one field to another using a mapping table. If the source values in the mapping table are inconsistent or incorrect, the applymap() will not function as expected, leading to incorrect results.
Steps to resolve:
* Review the mapping table (MAP_COUNTRY): The country field in the CountryTable contains values such as "U.S.", "US", and "United States" for the same country. To correctly normalize the country names, you need to ensure that all variations of a country's name are consistently mapped to a single value (e.g., "USA").
* Apply Mapping: Review and clean up the mapping table so that all possible variants of a country are correctly mapped to the desired normalized value.
Key References:
* Mapping Tables in Qlik Sense: Mapping tables allow you to substitute field values with mapped values. Any mismatches or variations in source values should be thoroughly reviewed.
* Applymap() Function: This function takes a mapping table and applies it to substitute a field value with its mapped equivalent. If the mapped values are not correct or incomplete, the output will not be as expected.
質問 # 16
Exhibit.
One of the data sources a data architect must add for a newly developed app is an Excel spreadsheet. The Region field only has values for the first record for the region. The data architect must perform a transformation so that each row contains the correct Region.
Which function should the data architect implement to resolve this issue?
- A. CrossTable
- B. IntervalMatch
- C. Above
- D. Previous
正解：D
解説：
The given Excel spreadsheet has a Region field where the region value is only specified for the first record within each region. The data architect needs to fill in the missing region values for subsequent rows.
* Previous() Function: The Previous() function in Qlik Sense returns the value of the expression from the previous row. In this case, it can be used to fill down the Region values so that each row contains the correct region information.
* Implementation: The script can be designed to check if the current row's Region value is missing (null). If it is missing, the script can assign the value from the previous row using the Previous() function.
LOAD
If(IsNull(Region), Previous(Region), Region) AS Region,
This logic fills in the missing Region values with the value from the preceding row, which effectively resolves the issue shown in the spreadsheet.
質問 # 17 
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. Move the Country field to the first position in the field list in the LOAD statement
- B. Perform a Resident LOAD of the CustTemp table and insert an Order By clause in this table
- C. Insert a Group By clause into the LOAD statement for the CustTemp table after the FROM clause
- D. Insert an Order By clause after the FROM clause in the CustTemp table
正解：B
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 # 18
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 service account running the task is not included in the Section Access table.
- C. The data architect does not have rights to reload the app.
- D. The ACCESS Column in the Section Access table has been added in lowercase.
正解：B
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 # 19
Exhibit.
Refer to the exhibit.
A data architect is provided with five tables. One table has Sales Information. The other four tables provide attributes that the end user will group and filter by.
There is only one Sales Person in each Region and only one Region per Customer.
Which data model is the most optimal for use in this situation?
- A.
- B.
- C.
- D.
正解：C
解説：
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.
質問 # 20
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 GeoAnalytics
- B. Microsoft SQL Server
- C. Mailbox IMAP
- D. Qlik REST Connector
正解：D
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 # 21 
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 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 - B. 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 - 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. Use a Mapping Load for the Employees table
3. Load the Sales table and use ApplyMap to get the names for SalesPersonID and RegionalAcctMgrlD
正解：B
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 # 22
Refer to the exhibit.
A system creates log files and csv files daily and places these files in a folder. The log files are named automatically by the source system and change regularly. All csv files must be loaded into Qlik Sense for analysis.
Which method should be used to meet the requirements?
- A.
- B.
- C.
- D.
正解：B
解説：
In the scenario described, the goal is to load all CSV files from a directory into Qlik Sense, while ignoring the log files that are also present in the same directory. The correct approach should allow for dynamic file loading without needing to manually specify each file name, especially since the log files change regularly.
Here's whyOption Bis the correct choice:
* Option A:This method involves manually specifying a list of files (Day1, Day2, Day3) and then iterating through them to load each one. While this method would work, it requires knowing the exact file names in advance, which is not practical given that new files are added regularly. Also, it doesn't handle dynamic file name changes or new files added to the folder automatically.
* Option B:This approach uses a wildcard (*) in the file path, which tells Qlik Sense to load all files matching the pattern (in this case, all CSV files in the directory). Since the csv file extension is explicitly specified, only the CSV files will be loaded, and the log files will be ignored. This method is efficient and handles the dynamic nature of the file names without needing manual updates to the script.
* Option C:This option is similar to Option B but targets text files (txt) instead of CSV files. Since the requirement is to load CSV files, this option would not meet the needs.
* Option D:This option uses a more complex approach with filelist() and a loop, which could work, but it's more complex than necessary. Option B achieves the same result more simply and directly.
Therefore,Option Bis the most efficient and straightforward solution, dynamically loading all CSV files from the specified directory while ignoring the log files, as required.
質問 # 23 
Refer to the exhibit
A large transport company (Company A) acquires a smaller rival (Company B).
Company A has been using Qlik Sense tor 6 years to track revenue per ship journey. Ship journeys with no revenue (such as journeys to shipyards for repair) always show revenue of $0.
Company A wants to combine its data set with the data set of the acquired Company B. Company B's ship journey data shows $0 revenue in one of the following ways:
* A NULL value
* A value with one or more blank spaces (ASCII char code 32)
The data architect wants to conform the Company B data to the Company A standard, specifically regarding the use of an explicit $0 for journeys without revenue. Which script line should the data architect use?
- A.
- B.
- C.
- D.
正解：A
解説：
In this scenario, the data architect needs to conform the revenue data from Company B to match the data standard of Company A, where $0 is explicitly used to represent journeys without revenue.
Explanation of the Correct Script:
* Option A:money(replace(Revenue, chr(32), 0)) AS [Revenue Conformed]
* replace(Revenue, chr(32), 0):This part of the expression replaces any spaces (ASCII character code 32) in the Revenue field with 0.
* money(...):This function formats the resulting value as currency. Since Company B may have either null values or spaces where 0 should be, this script ensures that any blanks are replaced with 0 and then formatted as currency.
Why Option A is Correct:
* Handling Spaces:The replace() function is effective in replacing spaces with 0, conforming to Company A's standard of using $0 for non-revenue journeys.
* Handling NULL Values:The money() function is used to ensure the final output is formatted as currency. However, it's important to note that NULL values are not directly handled by the replace() function, which is why it is applied before money() to deal with spaces.
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. PivotTable
- D. Hierarchy Be longsTo
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 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 2 years of data and use best practices in scripting and visualization to calculate and display aggregated data
- C. Load only 2 years of data in an aggregated app and create a separate transaction app for occasional use
- 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 # 26
Users of a published app report incomplete visualizations. The data architect checks the app multiple times and cannot replicate the error. The error affects only one team.
Which is the most likely cause?
- A. A security rule has been applied to the sheet object.
- B. Section access restricts too many records.
- C. An Omit field has been applied.
- D. The affected users were NOT added to the Section Access table.
正解：B
解説：
In this scenario, users of a published app report incomplete visualizations, but the data architect is unable to replicate the error. This issue affects only one team, suggesting that the problem is related to how data is being restricted or filtered for that specific team.
* Section Access: This is a security feature in Qlik Sense that controls user access to specific data within an app. If Section Access is misconfigured, it can restrict access to more records than intended, leading to incomplete visualizations for certain users or teams.
* Restricting Too Many Records: If the Section Access is too restrictive, it might limit the data available to the affected users, causing the visualizations to display incomplete information. This could explain why the data architect, who likely has full access, cannot replicate the issue.
質問 # 27
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 # 28
Exhibit.
Refer to the exhibit.
The data architect needs to build a model that contains Sales and Budget data for each customer. Some customers have Sales without a Budget, and other customers have a Budget with no Sales.
During loading, the data architect resolves a synthetic key by creating the composite key.
For validation, the data architect creates a table that contains Customer, Month, Sales, and Budget columns.
What will the data architect see when selecting a month?
- A. Customer Names and Sales records for the selected month, Budgets column can contain null or non-null values
- B. Customer Names and Budaets records for the selected month. Sales column can contain null or non-null values
- C. All Customer Names for both Sales and Budget records for the selected month
- D. Customer Names and Sales records for the selected month but with only non-null values in Budget column
正解：A
解説：
In the scenario where the data model is built with a composite key (keyYearMonthCustNo) to resolve synthetic keys, the following outcomes occur:
* Sales and Budget Data Integration:
* The composite key ensures that each combination of Year, Month, and Customer is uniquely represented in the combined Sales and Budget data.
* During data selection (e.g., when a specific month is selected), Qlik Sense will show all the customer names that have either Sales or Budget data associated with that month.
* Resulting Data View:
* For the selected month, customers with sales records will display their Sales data. However, if the corresponding Budget data is missing, the Budget column will contain null values.
* Similarly, if a customer has a Budget but no Sales data for the selected month, the Sales column will show null values.
Validation Outcome:When the data architect selects a month, they will see the following:
* Customer Names and Sales recordsfor the selected month, where the Sales column will have values and the Budget column may contain null or non-null values depending on the data availability.
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. Implement an "as of calendar against the sales table and use ApplyMap to fill in the needed management data
- C. Create a link table with a compound key of Sales Rep / Transaction Date to find the correct manager and region
- 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 receives an error while running script.
What will happen to the existing data model?
- A. Newly loaded tables will be merged with the existing data model until the error is resolved.
- B. The data model will be removed from the application.
- C. The data model will be replaced with the tables that were successfully loaded before the error.
- D. The latest error-free data model will be maintained.
正解：D
解説：
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.
質問 # 31
A startup company is about have its Initial Public Offering (IPO) on the New York Stock Exchange.
This startup company has used Qlik Sense for many years for data-based decision making for Sales and Marketing efforts, as well as for input into Financial Reporting. The startup's Qlik Sense applications use variables that have different values at different points in time.
Due to the increased rigor required in record keeping for public companies, these variables must be clearly recorded in the script reload logs of the Qlik Sense applications. These logs are refreshed daily.
The data architect wants to have the variables names, with their current values,writteninto the script reload logs. Which script statement should the data architect use?
- A. LogDetail
- B. REM
- C. Tag
- D. Trace
正解：D
解説：
In the scenario where the startup company is preparing for an IPO, there is an increased need for meticulous record-keeping, including the recording of variable values used in Qlik Sense applications. The TRACE statement is the most suitable option for logging variable values during script execution.
* TRACE: This statement writes custom messages, including variable values, to the script execution log.
By using TRACE, you can ensure that every reload log contains the names and current values of all relevant variables, providing the necessary transparency and traceability.
For example, the script could include:
TRACE $(VariableName);
This command will output the variable's value in the script log, ensuring it is recorded for audit purposes.
質問 # 32
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. Date(OrderDate) AS OrderDate in both the table "Orders" and "Master Calendar"
- B. DatefFloor(OrderDate)) AS OrderDate in both the table "Orders" and "Master Calendar"
- C. TimeStamp#(OrderDate, 'M/D/YYYY hh.mm.ff') 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：A
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 # 33
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. Remove the LineNo field from both tables and use the AutoNumber function on the OrderlD field
- C. Create a composite key using OrderlD and UneNo
- D. Remove the LineNo field from Shipments and use the AutoNumber function on the OrderlD field
正解：C
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 # 34
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
解説：
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.
質問 # 35
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