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Qlik QSDA2024 Exam Syllabus Topics:
Topic
Details
Topic 1
- Data Connectivity: This part evaluates how data analysts identify necessary data sources and connectors. It focuses on selecting the most appropriate methods for establishing connections to various data sources.
Topic 2
- Data Transformations: This section examines the skills of data analysts and data architects in creating data content based on specific requirements. It also covers handling null and blank data and documenting Data Load scripts.
Topic 3
- Data Model Design: In this section, data analysts and data architects are tested on their ability to determine relevant measures and attributes from each data source.
Topic 4
- Validation: This section tests data analysts and data architects on how to validate and test scripts and data. It focuses on selecting the best methods for ensuring data accuracy and integrity in given scenarios.
Topic 5
- Identify Requirements: This section assesses the abilities of data analysts in defining key business requirements. It includes tasks such as identifying stakeholders, selecting relevant metrics, and determining the level of granularity and aggregation needed.
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Qlik Sense Data Architect Certification Exam - 2024 Sample Questions (Q25-Q30):
NEW QUESTION # 25
Exhibit.
The Section Access security table for an app is shown. User ABCPPP opens a Qlik Sense app with a table using the field called LEVEL on one of the table columns.
Which is the result?
- A. The user gets an 'Incomplete visualization' error.
- B. The table is removed from the user interface.
- C. The table is displayed without the LEVEL column.
- D. The user gets a 'Field not found' error.
Answer: C
Explanation:
In this scenario, the Section Access security table controls user access to data within the Qlik Sense app. The user in question, ABCPPP, has a specific entry in the security table that determines their access rights to the LEVEL field.
Understanding Section Access:
* Section Accessis used to enforce security by restricting access to certain data based on the user's credentials.
* In the security table provided, the USER role for ABCPPP is set to have access to all data (* in the LINK field), but the OMIT field is set to LEVEL. The OMIT field in Section Access specifies fields that should be omitted from the user's view.
Outcome:
* Since the OMIT field for user ABCPPP is set to LEVEL, this user will not have access to the LEVEL field in the Qlik Sense application.
Option D: The table is displayed without the LEVEL columnis the correct outcome.
* Explanation: When user ABCPPP opens the app, the LEVEL field is omitted from their view. Any table or visualization that uses the LEVEL field will have that field excluded from display. The rest of the data and columns in the table will be visible, but the LEVEL column will not be shown.
References:
* Qlik Sense Security and Section Access Documentation: The OMIT functionality in Section Access is specifically designed to remove fields from the user's access, ensuring that sensitive or unnecessary data is not exposed.
NEW QUESTION # 26
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"
Answer: B
Explanation:
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.
NEW QUESTION # 27
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. 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. - D. 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.
Answer: A
Explanation:
When dealing with a database that is continuously updated with new records, updates, and deletions, an efficient data load strategy is necessary to minimize the load time and keep the Qlik Sense data model up-to- date.
Explanation of Steps:
* Load the existing data from the QVD:
* This step retrieves the already loaded and processed data from a previous session. It acts as a base to which new or updated records will be added.
* Load new and updated data from the database. Concatenate with the table loaded from the QVD:
* The next step is to load only the new and updated records from the database. This minimizes the amount of data being loaded and focuses on just the changes.
* The new and updated records are then concatenated with the existing data from the QVD, creating a combined dataset that includes all relevant information.
* Create a separate table for the deleted rows and use a WHERE NOT EXISTS to remove these records:
* A separate table is created to handle deletions. The WHERE NOT EXISTS clause is used to identify and remove records from the combined dataset that have been deleted in the source database.
NEW QUESTION # 28
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. Merge
- B. Partial Reload
- C. Semantic
- D. IntervalMatch
Answer: D
Explanation:
In the scenario described, the Dynamic Dimension data tracks changes in department assignments for salespeople over time. To correctly link the transaction data with the salesperson data and ensure that sales are associated with the correct department based on the date, an IntervalMatch function should be used.
IntervalMatchis designed to match discrete data (like transaction dates) with a range of dates. In this case, each salesperson's department assignment is valid over a period of time, and the IntervalMatch function can be used to link the transaction data with the correct department for each salesperson based on the transaction date.
* Option A (Merge):This option is incorrect as it refers to combining data sets, which doesn't address the need to handle the dynamic, date-based department assignments.
* Option B (IntervalMatch):This is the correct choice because it allows you to match each transaction with the correct department assignment based on the ChangeDate in the Dynamic Dimension data.
* Option C (Partial Reload):This refers to reloading only part of the data, which is not relevant to linking tables based on date ranges.
* Option D (Semantic):This option is not applicable as it refers to a broader approach to data modeling and interpretation rather than specifically linking data based on time intervals.
Thus,IntervalMatchis the correct method for linking the transaction data with the dynamic salesperson dimension, ensuring that each transaction is associated with the correct department based on the historical assignment data.
NEW QUESTION # 29
Sales managers need to see an overview of historical performance and highlight the current year's metrics.
The app has the following requirements:
* Display the current year's total sales
* Total sales displayed must respond to the user's selections
Which variables should a data architect create to meet these requirements?
- A.
- B.
- C.
- D.
Answer: C
Explanation:
To meet the requirements of displaying the current year's total sales in a way that responds to user selections, the correct approach involves using both SET and LET statements to define the necessary variables in the data load editor.
Explanation of Option C:
* SET vCurrentYear = Year(Today());
* The SET statement is used here to assign the current year to the variable vCurrentYear. The SET statement treats the variable as a text string without evaluation. This is appropriate for a variable that will be used as part of an expression, ensuring the correct year is dynamically set based on the current date.
* LET vCurrentYTDSales = '=SUM({$<Year={'$(vCurrentYear)'}>} [Sales Amount])';
* The LET statement is used here to assign an evaluated expression to the variable vCurrentYTDSales. This expression calculates the Year-to-Date (YTD) sales for the current year by filtering the Year field to match vCurrentYear. The LET statement ensures that the expression inside the variable is evaluated, meaning that when vCurrentYTDSales is called in a chart or KPI, it dynamically calculates the YTD sales based on the current year and any user selections.
Key Points:
* Dynamic Year Calculation: Year(Today()) dynamically calculates the current year every time the script runs.
* Responsive to Selections: The set analysis syntax {$<Year={'$(vCurrentYear)'}>} ensures that the sales totals respond to user selections while still focusing on the current year's data.
* Appropriate Use of SET and LET: The combination of SET for storing the year and LET for storing the evaluated sum expression ensures that the variables are used effectively in the application.
NEW QUESTION # 30
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