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QSDA2024 Pass Guarantee - QSDA2024 Online Version

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Qlik QSDA2024 Exam Syllabus Topics:

Topic
Details

Topic 1

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.

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 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 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

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.

Qlik Sense Data Architect Certification Exam - 2024 Sample Questions (Q37-Q42):

NEW QUESTION # 37
Exhibit.

Refer to the exhibit.
A data architect is loading the tables and a synthetic key is generated.
How should the data architect resolve the synthetic key?

A. Create a composite key using OrderlD and LineNo, and remove OrderlD and LineNo from Shipments
B. Create a composite key using OrderlD and UneNo
C. Remove the LineNo field from Shipments and use the AutoNumber function on the OrderlD field
D. Remove the LineNo field from both tables and use the AutoNumber function on the OrderlD field

Answer: B

Explanation:
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.

NEW QUESTION # 38
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?

Answer: C

Explanation:
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.

NEW QUESTION # 39
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. Trace
B. Tag
C. LogDetail
D. REM

Answer: A

Explanation:
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.

NEW QUESTION # 40
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. An Omit field has been applied.
C. Section access restricts too many records.
D. The affected users were NOT added to the Section Access table.

Answer: C

Explanation:
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.

NEW QUESTION # 41

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

A. 0
B. 1
C. 2
D. 3

Answer: B

Explanation:
The expression sum([OrderNetAmount]) sums the values in the OrderNetAmount field across the dataset.
Given that the dataset includes an inline table that is joined with another, the expression calculates the sum of OrderNetAmount for all selected rows. In this scenario, all values in LineNo are selected, which doesn't affect the summation of OrderNetAmount because LineNo isn't directly used in the sum calculation.
Step-by-step Calculation:
* The Orders table contains the OrderNetAmount for each order. The values provided are 90, 500, 100, and 120.
* Adding these values together:90+500+100+120=81090 + 500 + 100 + 120 = 81090+500+100+120=810
* However, after the Left Join operation with the OrderDetails table, some of these rows might be duplicated if the join results in multiple matches. But since the field being summed, OrderNetAmount, is from the original Orders table and not affected by the details in OrderDetails, the sum still remains consistent with the original values in the Orders table.
Thus, the sum of OrderNetAmount is 149014901490, based on the combined effects of the original data structure and the join operation.

NEW QUESTION # 42
......

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