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NEW QUESTION # 40
Refer to the exhibit.
An app that will track experiments for rodents (e.g., rats and mice) that navigate mazes (labyrinths) is being developed. Individual rodents are catalogued in the Rodent table, while the Mazes table has metadata for the mazes. The MazeEscapes table holds a record of each attempt at a maze by a rodent. A business analyst needs to build a KPI that will allow users to see how many rodents have made at least one attempt at any maze.
How should the analyst construct the KPI?
Answer: A
Explanation:
In this case, we need to count how many unique rodents have made at least one attempt at any maze. Since the relationship between the Rodent table and the MazeEscapes table is a one-to-many relationship (each rodent may attempt several mazes), the best approach is to base the calculation on the MazeEscapes table, as it holds the actual records of rodent attempts.
By creating a new field RodentID_Counter in the MazeEscapes table, the analyst ensures that each rodent's attempt is captured. Using the Count (Distinct RodentID_Counter) as the KPI expression then accurately counts each unique rodent that has made at least one attempt, without counting multiple attempts by the same rodent.
Key Concepts:
Distinct Count: The distinct count ensures that rodents who have made multiple maze attempts are only counted once.
MazeEscapes Table: This table holds the records of each attempt, making it the correct source for this KPI.
Why the Other Options Are Less Suitable:
B . Sum (RodentID_Counter) in the Rodent table: This would incorrectly sum the number of rodents rather than counting unique attempts, leading to inaccurate results.
C . Sum (RodentID_Counter) in the MazeEscapes table: Summing the counter would not account for the distinct rodents, leading to incorrect counting.
D . RodentID_Counter in the Rodent table: This would not correctly reflect the rodents that have made an attempt, as not every rodent in the Rodent table may have attempted a maze.
References for Qlik Sense Business Analyst:
Distinct Count in KPI: It is best practice to use Count(Distinct ...) when working with KPIs that require unique counts, such as counting distinct rodents in this scenario.
Thus, A is the verified answer because it ensures that each rodent is counted once, based on their attempts in the MazeEscapes table.
NEW QUESTION # 41
A customer is developing over 100 apps, each with several sheets that contain multiple visualizations and text objects. The customer wants to standardize all colors used every object across every app. The customer also needs to be able to change these colors quickly, as required.
Which steps should the business analyst take to make sure the color palette is easily maintained in every app?
Answer: D
Explanation:
In scenarios where a customer needs to standardize colors across multiple apps and be able to update them quickly, using variables in combination with an include statement is the most flexible and maintainable approach.
A . Design all base objects as master visualizations and link each object in each app to the relevant master visualization.
While master visualizations help with consistency within a single app, they don't offer an easy way to update all apps globally. You would need to manually update the colors in every master visualization in each app, which is not efficient for large-scale management.
B . Develop the first app with every variation of object and visualization and duplicate this app.
Duplicating apps will create maintenance challenges. Each app would need to be updated individually if colors or other settings change, which is not scalable for over 100 apps.
C . Create all color expressions as variables in a text file, load it in each app with an include statement, and use these variables in the color property of all objects.
This is the most efficient solution. By storing color definitions in a text file and loading them with an include statement, the business analyst can update the colors in one place, and these updates will be reflected across all apps that use the file. This method ensures easy maintenance and flexibility.
D . Store color definitions within a .qvd file and load it as a data island.
While using a .qvd file is possible, it's not as straightforward as using variables and an include statement. Data islands are typically used for selection purposes, and this method would introduce unnecessary complexity in managing colors.
Key Qlik Sense Business Analyst References:
Variables are widely used in Qlik Sense for managing repeated expressions or values like colors. They can be defined once and reused throughout the app.
Include statements allow external files (like text files containing variables) to be loaded into apps, ensuring that updates made to the text file are automatically reflected in all apps that use it. This creates a flexible and scalable solution for managing standardization across multiple apps.
Thus, the best way to maintain a standardized color palette across all apps is to create all color expressions as variables in a text file and load them into each app using an include statement.
NEW QUESTION # 42
A business analyst needs to rapidly prototype an application design for a prospective customer. The only dataset provided by the customer contains 30 fields, but has over one billion rows. It will take too long to keep loading in its entirety while the analyst develops the data model.
Which action should the business analyst complete in the Data manager?
Answer: D
Explanation:
When working with large datasets, such as the one containing over a billion rows in this scenario, loading the entire dataset can be time-consuming, especially during the development phase. Qlik Sense provides a Filter data option in the Data Manager, which allows business analysts to work with a subset of the data during development. This is particularly useful for rapidly prototyping the application design.
Key Concepts:
Filter Data Option: This feature in Qlik Sense allows the analyst to load a smaller sample of the dataset for analysis and development purposes. By filtering out unnecessary rows, the business analyst can quickly build and prototype the application without waiting for the full dataset to load. Once the design is finalized, the full dataset can be reloaded.
Prototyping with Reduced Data: It's often more efficient to work with a smaller dataset during the design phase. This allows for faster iterations and design cycles, especially when working with large datasets.
Why the Other Options Are Less Suitable:
A . Split the dataset and create a normalized star schema of associated tables: This would involve complex data modeling that is not necessarily related to the immediate need of reducing the size of the dataset for prototyping. While star schemas can optimize data models, it's not the quickest way to reduce the number of rows for initial testing.
B . Deselect text columns with unique data values to reduce the memory footprint: This may reduce the memory usage but won't necessarily address the issue of reducing the number of rows. Also, the text columns might be important for the analysis and should not be removed without careful consideration.
D . Truncate text fields longer than 256 characters to create preview fields: Truncating text fields will not significantly reduce the dataset size or the load time. It may also result in losing critical information, which is not ideal for prototyping.
References for Qlik Sense Business Analyst:
Rapid Prototyping: Qlik Sense encourages rapid development of applications by allowing business analysts to work with subsets of the data. The Filter Data option is an important tool for managing large datasets efficiently.
Data Manager Tools: The Data Manager in Qlik Sense provides several tools for reducing the dataset size, and filtering is one of the key options for improving performance during development.
Using the Filter data option allows the business analyst to focus on a smaller subset of data, enabling quicker prototyping and iteration, which makes option C the most effective solution.
NEW QUESTION # 43
A business analyst is working with retail data for consumer products. The customer is interested in the following:
* Ability to look for outliers on sales volume and margin %
* Ability to understand the clustering of products visually
* Ability to see products that are above the median sales volume
Which action should the business analyst take to implement these requirements?
Answer: B
Explanation:
A scatter plot is the best choice for visualizing outliers, clustering, and products that are above the median sales volume. Scatter plots are excellent for showing relationships between two variables (like sales volume and margin %) while providing the ability to highlight outliers. By using K-Means clustering to color the products, the business analyst can visually group similar products. Additionally, adding a median sales volume reference line makes it easy to identify which products are above or below the median.
Key Concepts:
Scatter Plot: This visualization is ideal for detecting outliers and understanding the distribution and clustering of data points.
K-Means Clustering: This technique groups similar data points (products) based on their values, which helps to identify patterns visually.
Median Reference Line: Adding a reference line for median sales volume ensures that the analyst can easily see which products are performing above or below the median.
Why the Other Options Are Less Suitable:
A . Pivot table: While a pivot table could show aggregated data, it is not as effective for visualizing outliers or clustering as a scatter plot.
B . Treemap: A treemap can show hierarchical relationships but is not as effective for identifying clusters or outliers.
C . Combo chart: A combo chart is not as well-suited for visualizing clustering and outliers as a scatter plot.
References for Qlik Sense Business Analyst:
Outliers and Clustering Visualization: Qlik Sense recommends scatter plots for visualizing relationships between two measures and for detecting outliers and clustering in the data.
Thus, creating a scatter plot with K-Means clustering and a median sales volume reference line is the best approach, making D the verified answer.
NEW QUESTION # 44
A business analyst is creating an app for the team. A set of selections must be applied every time an app is opened. Which action should the business analyst take to meet this requirement?
Answer: B
Explanation:
In Qlik Sense, default bookmarks allow a set of selections to be applied automatically whenever the app is opened. By creating a bookmark and setting it as the default, the business analyst ensures that the required selections are applied every time the app is opened, which meets the requirement of consistently applying the same selections for all users.
Key Concepts:
Default Bookmark: A default bookmark automatically applies the saved selections when an app is opened, ensuring consistency without manual input from users.
Bookmarking: This feature allows users to save specific selections or states of a dashboard for later use.
Why the Other Options Are Less Suitable:
A . Section Access: Section Access controls data access and security, not default selections.
B . Mashup with API: While this could technically work, it's unnecessarily complex and requires custom coding.
C . Sheet Action: A sheet action could apply a bookmark but would not ensure that the selections are applied at the time of app opening.
References for Qlik Sense Business Analyst:
Default Bookmark for Predefined Selections: This feature is commonly used to ensure that specific selections are always applied when an app is opened.
Thus, D is the best solution for applying default selections, making it the correct answer.
NEW QUESTION # 45
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