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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Managing and Creating Analyses, Views, and Content in Oracle Analytics | 10% | - Creating and managing views - Creating and managing analyses - Managing analytics content |
| Topic 2: Understanding Oracle Analytics Cloud | 5% | - Oracle Analytics Cloud concepts and capabilities |
| Topic 3: Managing and Securing Oracle Analytics Publisher in Oracle Analytics Cloud | 10% | - Managing Oracle Analytics Publisher - Securing Publisher content and access |
| Topic 4: Managing Administration, Security, and Performance in Oracle Analytics Cloud | 10% | - Administration - Performance and caching - Security and application roles |
| Topic 5: Formatting Data and Managing Filters, Selection Steps, and Variables in Oracle Analytics | 10% | - Managing selection steps - Managing filters - Using prompts and variables - Formatting data |
| Topic 6: Managing Report Scheduling, Data Sources, Administration, and Translations in Oracle Analytics Publisher | 15% | - Publisher administration - Managing report data sources - Scheduling reports - Bursting and delivery - Report translations |
| Topic 7: Connecting to Data Sources and Migrating Artifacts to Oracle Analytics Cloud | 10% | - Migrating analytics artifacts - Connecting to data sources |
| Topic 8: Designing, Creating, and Enhancing Dashboards in Oracle Analytics | 10% | - Creating and designing dashboards - Dashboard prompts and pages - Enhancing dashboards and briefing books |
| Topic 9: Exploring and Creating Reports and Data Models in Oracle Analytics Publisher | 5% | - Creating reports - Creating and managing data models |
| Topic 10: Building and Formatting Reports Using Layout Editor and Template Builder | 15% | - Formatting and customizing reports - Using RTF Template Builder - Creating Excel and eText templates - Using Layout Editor |
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NEW QUESTION # 23
A reporting analyst creates a dashboard that includes multiple analyses sharing the same filter criteria. Users complain that updating each analysis separately is difficult. Which design approach improves maintainability?
Answer: B
Explanation:
A shared Dashboard Prompt with Presentation Variables centralizes user input and allows multiple dashboard analyses to reference the same runtime selection. Oracle defines a Presentation Variable as a variable populated through a dashboard column prompt or variable prompt. Each time the user changes the prompt selection, the presentation variable is assigned the selected value or values.
Analyses on the dashboard can then reference the variable in filters, expressions, titles, or other supported locations. This allows a department, fiscal period, region, or comparable filter criterion to be selected once and reused across multiple dashboard components.
The architecture substantially improves maintainability because the dashboard-level interaction is managed centrally rather than requiring a separate prompt or manually maintained filter mechanism for every analysis.
Independent prompts would recreate the exact maintenance problem stated in the scenario. Conditional Formatting changes visual presentation rather than providing shared filtering, while exporting analyses creates static outputs and eliminates the interactive dashboard experience.
Therefore, the correct design is A. Use a shared Dashboard Prompt with Presentation Variables .
Reference Topic: Designing, Creating, and Enhancing Dashboards - Dashboard Prompts and Presentation Variables
NEW QUESTION # 24
An author wants to limit which members are displayed in an analysis without changing the resulting values like a filter would. Which two statements describe selection steps?
Answer: A,B
Explanation:
Selection steps differ fundamentally from conventional filters in their position within analytical processing.
Oracle states that filters are evaluated before aggregation , whereas selection steps are applied after the query has been aggregated . As a result, selection steps determine which members are displayed without recalculating the previously established aggregate values.
For example, assume a hierarchy contains members totaling 100. If an ordinary filter removes one member before aggregation, the measure total can change. If a selection step removes that member after aggregation, the displayed membership changes while the aggregate can remain 100. This behavior is precisely why selection steps are valuable for analytical member manipulation such as Keep Only, Add, Remove, or condition-based selections.
Within classic analyses, Oracle supports creation of selection steps for attribute columns and hierarchical columns . Measure columns can be used to formulate conditions for selection steps, but selection steps aren't directly created for measures themselves.
Selection steps don't replace filters; both serve distinct analytical purposes. They also aren't restricted to pivot tables.
Therefore, C and D correctly identify their applicable column types and their post-aggregation effect on displayed members.
Reference Topic: Formatting Data and Managing Filters, Selection Steps, and Variables - Selection Steps
NEW QUESTION # 25
A business user wants to compare current-quarter sales against only the top five performing product categories. Which feature best supports this requirement?
Answer: C
Explanation:
A Top N selection/filtering operation is the feature designed to retain only the highest-performing members according to a selected measure. In this case, Product Category is the dimension and current-quarter Sales is the measure used to rank the categories. Configuring the analytical selection to retain the top five categories produces exactly the required comparison population.
Oracle Analytics supports both selection-step member refinement and explicit Top/Bottom N filtering capabilities. The current Oracle Analytics documentation includes Top N examples that identify the highest attribute values based on a measure, which is precisely the analytical pattern in this question.
The important concept is that the ranking is based on the measure rather than visual styling. Conditional Formatting could visually distinguish high or low Sales values but wouldn't restrict the result to five product categories. Repository Initialization Blocks initialize semantic-model variables and have no role in ranking members. Dashboard Page Style controls appearance and layout rather than result membership.
Therefore, Selection Step with Top N filtering is the best supplied answer because it limits the displayed product categories according to their current-quarter Sales ranking and retains only the five highest performers.
Reference Topic: Formatting Data and Managing Filters, Selection Steps, and Variables - Top N Analysis
NEW QUESTION # 26
A global enterprise is implementing Oracle Analytics Cloud and wants executives to view the same dashboard while automatically seeing only data for their own business unit. Which implementation best follows Oracle best practices?
Answer: B
Explanation:
The recommended architecture is to maintain a single governed dashboard and enforce data visibility through application roles and row-level security in the semantic model . Oracle Analytics allows semantic- model data filters to be assigned to specific application roles. When a user queries an object protected by those filters, Oracle Analytics incorporates the applicable security predicates into the generated query, restricting results to the rows that user's roles are authorized to access.
For example, executives can all open the same Revenue dashboard while a Business Unit data filter automatically restricts each executive to the appropriate organizational data. This approach centralizes dashboard maintenance and security policy while avoiding duplicated content.
Dashboard prompts are useful for interactive filtering, but user-controlled prompts are not a substitute for authorization. Separate dashboards or subject areas for every executive create unnecessary maintenance and introduce the risk of inconsistent logic.
Oracle specifically recommends applying data filters to application roles rather than individual users for scalable row-level security. Therefore, C is the correct governed implementation.
Reference Topic: Managing Administration, Security, and Performance - Application Roles and Row- Level Security
NEW QUESTION # 27
An analyst wants to highlight sales values above target in green and values below target in red without changing the underlying data. Which feature should be used?
Answer: C
Explanation:
Conditional Formatting is specifically designed to modify the visual representation of data when defined conditions are satisfied while leaving the underlying values unchanged. The analyst can establish rules such as Sales greater than or equal to Target and apply one format, while values below Target receive a different format.
Oracle Analytics supports conditional rules based on measures, attributes, thresholds, percentages, and expressions. Formatting can include font characteristics, cell presentation, images, backgrounds, and visualization-specific properties. Oracle's examples include stoplight-style formatting in which low, medium, and high values receive different visual treatments.
This functionality affects presentation only; it doesn't alter the source Sales values or change the actual calculation. That directly satisfies the requirement.
A Repository Variable stores centrally maintained values and isn't itself a visualization rule. Selection Steps determine which members appear after query aggregation. A Subject Area Filter limits the data returned or made available rather than simply changing its appearance.
Consequently, the appropriate feature for showing above-target values in green and below-target values in red is Conditional Formatting , making A correct.
Reference Topic: Formatting Data and Managing Filters, Selection Steps, and Variables - Conditional Formatting
NEW QUESTION # 28
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