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| Section | Weight | Objectives |
|---|---|---|
| Understanding Oracle Analytics Cloud | 5% | - Oracle Analytics Cloud concepts and capabilities |
| Building and Formatting Reports Using Layout Editor and Template Builder | 15% | - Using RTF Template Builder - Using Layout Editor - Formatting and customizing reports - Creating Excel and eText templates |
| Managing Report Scheduling, Data Sources, Administration, and Translations in Oracle Analytics Publisher | 15% | - Publisher administration - Report translations - Bursting and delivery - Scheduling reports - Managing report data sources |
| Managing and Creating Analyses, Views, and Content in Oracle Analytics | 10% | - Managing analytics content - Creating and managing analyses - Creating and managing views |
| Managing and Securing Oracle Analytics Publisher in Oracle Analytics Cloud | 10% | - Managing Oracle Analytics Publisher - Securing Publisher content and access |
| Exploring and Creating Reports and Data Models in Oracle Analytics Publisher | 5% | - Creating reports - Creating and managing data models |
| Designing, Creating, and Enhancing Dashboards in Oracle Analytics | 10% | - Enhancing dashboards and briefing books - Creating and designing dashboards - Dashboard prompts and pages |
| Connecting to Data Sources and Migrating Artifacts to Oracle Analytics Cloud | 10% | - Migrating analytics artifacts - Connecting to data sources |
| Formatting Data and Managing Filters, Selection Steps, and Variables in Oracle Analytics | 10% | - Using prompts and variables - Managing filters - Formatting data - Managing selection steps |
| Managing Administration, Security, and Performance in Oracle Analytics Cloud | 10% | - Administration - Performance and caching - Security and application roles |
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질문 # 24
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?
정답：A
설명：
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
질문 # 25
Which Oracle Analytics Publisher configuration controls default processing options such as temporary file handling, font settings, and report output behavior?
정답：A
설명：
Runtime Configuration controls Publisher's system-level processing and document-generation behavior.
Oracle's Runtime Configuration framework includes settings for different output types, PDF processing and security, FO processing, memory and performance behavior, template processing, report delivery properties, and associated font mappings.
Oracle's runtime configuration file also illustrates settings such as system-temp-dir, PDF compression, PDF security, and font definitions. In the administrative interface, the Runtime Configuration page provides corresponding system-level properties, with some settings also configurable at individual report level. When report-level and system-level settings differ, Oracle can apply the more specific report-level setting where documented.
A Subject Area is a governed analytical data presentation layer and has nothing to do with Publisher document-processing defaults. Presentation Variables capture runtime prompt values. Selection Steps refine displayed members of an analysis.
The uploaded source's explanation explicitly identifies Runtime Configuration as defining system-wide Publisher processing and output-generation behavior. Its extracted "Correct answer" marker is misplaced relative to the options; Oracle documentation confirms that A. Runtime Configuration is the technically correct answer.
Reference Topic: Managing Report Scheduling, Data Sources, Administration, and Translations - Runtime Configuration
질문 # 26
A report designer needs to display sales performance by Region, Product Category, and Quarter in a highly interactive visual comparison. Which view is most appropriate?
정답：B
설명：
A Trellis View is the most appropriate visualization for comparing the same measure across multiple dimensional categories. A trellis displays a series of related visualizations in a grid, allowing users to compare equivalent graphical structures across dimension members.
For example, the designer could use Region or Product Category as an outer-edge dimension and Quarter or another analytical dimension within the repeated visualizations. Because every trellis cell follows a consistent graphical framework, differences in sales performance become easier to identify across categories.
Oracle distinguishes between Simple Trellis and Advanced Trellis use cases. Oracle recommends Simple Trellis for general comparisons and Advanced Trellis when analyzing trends. The documentation also advises controlling dimensional density so that each repeated graph remains readable.
A Narrative View renders textual descriptions generated from query results and isn't optimized for comparative multidimensional visualization. A static table can present all values but provides less immediate visual comparison. A KPI is generally intended to summarize performance against targets rather than display a repeated multidimensional comparison across Region, Product Category, and Quarter.
Therefore, Trellis View best satisfies the scenario.
Reference Topic: Managing and Creating Analyses, Views, and Content - Trellis Views
질문 # 27
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?
정답：B,E
설명：
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
질문 # 28
What happens when a user drills on an attribute column in the Analysis Editor?
정답：C
설명：
Drilling on an attribute column performs what Oracle describes as a filter drill . When the user clicks a drillable attribute value, Oracle adds the next lower level to the displayed analysis and creates a filter corresponding to the member on which the user drilled.
Oracle's documentation states that drilling in an attribute column affects all relevant views. After the value is clicked, a column is added to the analysis and a filter is automatically created and listed on the Criteria tab . This distinguishes attribute-column drilling from drilling in hierarchical columns, where expanding or collapsing hierarchy members generally affects only the particular hierarchy view without creating the same analysis-wide filter drill behavior.
The drill operation therefore modifies the analytical context; the underlying criteria don't simply remain unchanged. Oracle also doesn't automatically save the resulting drill filter as a reusable named filter, nor does a drill operation create a dashboard prompt. Those are separate authoring operations.
Consequently, the specific outcome described by Oracle is exactly option A: drilling an attribute column automatically introduces the corresponding filter and adds the lower-level column to the analysis criteria.
Reference Topic: Managing and Creating Analyses, Views, and Content - Drilling in Analysis Results
질문 # 29
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