QSDA2024試験問題でリアルに更新された問題PDF
合格させる無料保証付きクイズ2025年最新の実際に出ると確認されたQlik
質問 # 25
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 Analyst, Qlik Sense Developers, Solutions Architect
- B. IT Security Director, Human Resources Director, Qlik Sense Administrator
- C. IT Security Manager, Qlik Sense Account Manager, Enterprise Architecture Director
- D. IT Security Vice President, Human Resources Analyst, Qlik Sense Developers
正解：B
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 # 26
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：C
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 # 27
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. User sessions, source data architecture, compatibility, data model, business use case
- C. Application metadata, application theme, user sessions, load script, IT 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 # 28
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 With Connection to pass a parameter to the REST URL
- B. Use pagination of the REST Connector to create a template of the desired data
- 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 # 29
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 # 30
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. FileSize, IF, THEN, END IF
- B. FilePath, FOR EACH, Peek, Drop
- C. FilePath, IF, THEN, Drop
- D. FileExists, FOR EACH, IF
正解：D
質問 # 31
A data architect executes the following script:
What will be the result of Table.A?
- A.
- B.
- C.
- D.
正解：A
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 # 32
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：C
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 # 33
Exhibit.
A large electronics company re-assigns sales people once per year from one Department to another.
SPID is the Salesperson ID; the SPID for each individual sales person Name remains constant. The Department for a SPID may change; each change is stored in the Dynamic Dimension data.
Four tables need to be linked correctly: a transaction table, a dynamic salesperson dimension, a static salesperson dimension, and a department dimension.
Which script prefix should the data architect use?
- A. Partial Reload
- B. Semantic
- C. IntervalMatch
- D. Merge
正解：C
解説：
In the scenario described, the Dynamic Dimension data tracks changes in department assignments for salespeople over time. To correctly link the transaction data with the salesperson data and ensure that sales are associated with the correct department based on the date, an IntervalMatch function should be used.
IntervalMatchis designed to match discrete data (like transaction dates) with a range of dates. In this case, each salesperson's department assignment is valid over a period of time, and the IntervalMatch function can be used to link the transaction data with the correct department for each salesperson based on the transaction date.
* Option A (Merge):This option is incorrect as it refers to combining data sets, which doesn't address the need to handle the dynamic, date-based department assignments.
* Option B (IntervalMatch):This is the correct choice because it allows you to match each transaction with the correct department assignment based on the ChangeDate in the Dynamic Dimension data.
* Option C (Partial Reload):This refers to reloading only part of the data, which is not relevant to linking tables based on date ranges.
* Option D (Semantic):This option is not applicable as it refers to a broader approach to data modeling and interpretation rather than specifically linking data based on time intervals.
Thus,IntervalMatchis the correct method for linking the transaction data with the dynamic salesperson dimension, ensuring that each transaction is associated with the correct department based on the historical assignment data.
質問 # 34
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 # 35
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. Microsoft SQL Server
- B. Mailbox IMAP
- C. Qlik GeoAnalytics
- 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 # 36
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. Hierarchy Be longsTo
- B. Generic
- C. Peek
- 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 # 37
A data architect needs to load data from two different databases. Additional data will be added from a folder that contains QVDs, text files, and Excel files.
What is the minimum number of data connections required?
- A. Two
- B. Three
- C. Five
- D. Four
正解：A
解説：
In the scenario, the data architect needs to load data from two different databases, and additional data is located in a folder containing QVDs, text files, and Excel files.
Minimum Number of Data Connections Required:
* Database Connections:
* Each database requires a separate data connection. Therefore, two data connections are needed for the two databases.
* Folder Connection:
* A single folder data connection can be used to access all the QVDs, text files, and Excel files in the specified folder. Qlik Sense allows you to create a folder connection that can access multiple file types within that folder.
Total Connections:
* Two Database Connections: One for each database.
* One Folder Connection: To access the QVDs, text files, and Excel files.
Therefore, the minimum number of data connections required istwo.
質問 # 38
Exhibit.
Refer to the exhibit.
A major healthcare organization requests a new app with the following requirements:
* Users can filter AdmissionDate and DischargeDate by all fields in the Master Calendar table
* Use an existing QVD file, which includes dates 20 years into the future
* Users should not be able to filter on dates that have no associated encounters Which approach should the data architect take to meet these requirements?
- A. 1. Load the Master Calendar and Encounters tables
2. Perform a Join Load on the Encounters table to the Resident master calendar and alias the date fields appropriately for the Admission Date
3. Perform a Join Load on the Encounters table to the Resident master calendar and alias the date fields appropriately for the Discharge Date - B. 1. Load the master calendar
2. Create two mapping tables called AdmissionCalendar and DischargeCalendar from the Resident master calendar thatfeas all fields appropriately named
3. Load the Encounters table and use ApplyMap for the AdmissionDate and DischargeDate appropriately - C. 1. Load the master calendar as AdmissionCalendar and alias the fields to reflect they are for Admission
2. Load the master calendar as DischargeCalendar and alias the fields to reflect they are for Discharge
3. Load the Encounters table - D. 1. Load the Encounters table
2. Perform a Left Join Load on the Encounters table to the master calendar and alias the date fields appropriately for the Admission Date
3. Perform a Left Join Load on the Encounters table to the master calendar and alias the date fields appropriately for the Discharge Date
正解：C
解説：
In the scenario presented, a major healthcare organization needs an app that allows users to filter AdmissionDate and DischargeDate by all fields in the Master Calendar table, while also ensuring that users cannot filter on dates that have no associated encounters.
To meet these requirements, the most appropriate approach is to:
* Load the Master Calendar twice,once as AdmissionCalendar and once as DischargeCalendar. Each instance should have its fields appropriately aliased to reflect whether they pertain to Admission or Discharge dates.
* Load the Encounters tableas usual, but now you have two separate calendar tables that can be linked to the appropriate date fields (AdmissionDate and DischargeDate) in the Encounters table.
This approach ensures:
* Users can filter both AdmissionDate and DischargeDateindependently using the fields in their respective calendar tables.
* Only relevant datesassociated with actual encounters will be available for filtering, as the calendars are linked specifically to the AdmissionDate and DischargeDate fields.
* Efficiency and clarityin the data model, as the fields from the Master Calendar are distinctly assigned to either Admission or Discharge, avoiding any confusion or incorrect filtering.
This method avoids unnecessary complexity and directly meets the healthcare organization's requirements in a straightforward and scalable manner.
質問 # 39 
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 # 40
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. Trace
- D. Tag
正解：C
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 # 41
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. IntervalMatch
- B. CrossTable
- C. Previous
- D. Above
正解：C
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 # 42
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 ACCESS Column in the Section Access table has been added in lowercase.
- C. The service account running the task is not included in the Section Access table.
- D. The data architect does not have rights to reload the app.
正解：C
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 # 43
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 but with only non-null values in Budget column
- 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, Budgets column can contain null or non-null values
正解：D
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 # 44
A data architect needs to load large amounts of data from a database that is continuously updated.
* New records are added, and existing records get updated and deleted.
* Each record has a LastModified field.
* All existing records are exported into a QVD file.
* The data architect wants to load the records into Qlik Sense efficiently.
Which steps should the data architect take to meet these requirements?
- A. 1. Load the existing data from the QVD.
2. Load new and updated data from the database. Concatenate with the table loaded from the QVD.
3. Create a separate table for the deleted rows and use a WHERE NOT EXISTS to remove these records. - B. 1. Load the new and updated data from the database.
2. Load the existing data from the QVD without the updated rows that have just been loaded from the database and concatenate with the new and updated records.
3. Load all records from the key field from the database and use an INNER JOIN on the previous table. - C. 1. Load the existing data from the QVD.
2. Load the new and updated data from the database without the rows that have just been loaded from the QVD and concatenate with data from the QVD.
3. Load all records from the key field from the database and use an INNER JOIN on the previous table. - D. 1. Use a partial LOAD to load new and updated data from the database.
2. Load the existing data from the QVD without the updated rows that have just been loaded from the database and concatenate with the new and updated records.
3. Use the PEEK function to remove the deleted rows.
正解：A
解説：
When dealing with a database that is continuously updated with new records, updates, and deletions, an efficient data load strategy is necessary to minimize the load time and keep the Qlik Sense data model up-to- date.
Explanation of Steps:
* Load the existing data from the QVD:
* This step retrieves the already loaded and processed data from a previous session. It acts as a base to which new or updated records will be added.
* Load new and updated data from the database. Concatenate with the table loaded from the QVD:
* The next step is to load only the new and updated records from the database. This minimizes the amount of data being loaded and focuses on just the changes.
* The new and updated records are then concatenated with the existing data from the QVD, creating a combined dataset that includes all relevant information.
* Create a separate table for the deleted rows and use a WHERE NOT EXISTS to remove these records:
* A separate table is created to handle deletions. The WHERE NOT EXISTS clause is used to identify and remove records from the combined dataset that have been deleted in the source database.
質問 # 45
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 UneNo
- 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 LineNo, and remove OrderlD and LineNo from Shipments
- D. Remove the LineNo field from Shipments and use the AutoNumber function on the OrderlD field
正解：A
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 # 46
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Qlik QSDA2024 認定試験の出題範囲：
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