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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Managing and Securing Oracle Analytics Publisher in Oracle Analytics Cloud | 10% | |
| Topic 2: Building and Formatting Reports Using Layout Editor and Template Builder | 15% | |
| Topic 3: Designing, Creating, and Enhancing Dashboards in Oracle Analytics | 10% | |
| Topic 4: Exploring and Creating Reports and Data Models in Oracle Analytics Publisher | 5% | |
| Topic 5: Understanding Oracle Analytics Cloud | 5% | |
| Topic 6: Managing Administration, Security, and Performance in Oracle Analytics Cloud | 10% | |
| Topic 7: Managing Report Scheduling, Data Sources, Administration, and Translations in Oracle Analytics Publisher | 15% | |
| Topic 8: Connecting to Data Sources and Migrating Artifacts to Oracle Analytics Cloud | 10% | |
| Topic 9: Formatting Data and Managing Filters, Selection Steps, and Variables in Oracle Analytics | 10% | |
| Topic 10: Managing and Creating Analyses, Views, and Content in Oracle Analytics | 10% |
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NEW QUESTION # 102
What happens when a user drills on an attribute column in the Analysis Editor?
Answer: E
Explanation:
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
NEW QUESTION # 103
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?
Answer: C
Explanation:
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
NEW QUESTION # 104
During production validation, users report that migrated dashboards open successfully but some analyses display "Subject Area not found." What should the administrator verify first?
Answer: B
Explanation:
A "Subject Area not found" condition points first to the analytical metadata layer rather than to dashboard rendering. Analyses reference subject areas exposed by the deployed semantic model. If the expected semantic model wasn't migrated or deployed correctly, if the relevant presentation subject area is missing, or if its name/mapping no longer matches what the analysis references, the dashboard itself can open while individual analyses fail.
Oracle's OAC migration workflow explicitly treats the Data Model and Subject Areas as snapshot components. During certain migration procedures, administrators may deliberately exclude these components from snapshot restoration and separately upload an updated semantic model. This makes verification of the deployed semantic model and its exposed subject areas a critical production-validation step.
Browser compatibility does not create or remove subject-area metadata. Email configuration applies to delivery, and Publisher Runtime Configuration controls report-processing behavior rather than Classic Analysis subject-area resolution.
Therefore, the administrator should first verify that the expected semantic model is deployed and the referenced subject areas and mappings exist correctly , making B the technically supported response.
Reference Topic: Connecting to Data Sources and Migrating Artifacts - Semantic Model and Subject Area Migration
NEW QUESTION # 105
Which statement best describes a Repository Variable in Oracle Analytics Cloud?
Answer: A
Explanation:
A Semantic Model (Repository) Variable stores a centrally defined value that can be referenced by analyses, dashboards, filters, expressions, and other semantic-model logic. Oracle defines repository variables as values maintained within the semantic model rather than supplied interactively by individual report consumers.
Repository variables can be static or dynamic . A static repository variable retains its configured value until an administrator changes it. A dynamic repository variable is refreshed from data returned by an initialization query. Typical shared values include organizational constants, current reporting periods, or other centrally managed values that should be applied consistently.
Option A instead describes a Presentation Variable, which can be populated by a user's prompt selection.
Option B more closely describes a Session Variable, whose value is specific to a user's session and is commonly initialized when the user logs in. Option D doesn't describe repository-variable scope because a repository variable can persist across many analysis executions.
Oracle's current documentation calls these Semantic Model (Repository) Variables , preserving the repository terminology used in Classic Analytics. Therefore, the most accurate description is a centrally managed value reusable across analytical content .
Reference Topic: Formatting Data and Managing Filters, Selection Steps, and Variables - Repository Variables
NEW QUESTION # 106
An author repeatedly uses the same state filter in multiple analyses and wants to reuse the filter. What should the author do?
Answer: E
Explanation:
The author should save the filter as a named filter in the catalog . Oracle Analytics distinguishes between inline filters and named filters. An inline filter belongs to one specific analysis, whereas a named filter is stored independently in the Oracle Analytics Catalog and can subsequently be referenced by multiple compatible analyses.
This is particularly useful for centrally reused business criteria. For example, an organization might create a named filter representing a specific State, Quarter, business unit, or product group. Report authors can then apply that saved filter to multiple analyses rather than recreate the filter definition repeatedly. Oracle also notes that when a named filter is edited and saved, changes can propagate wherever the named filter is referenced, improving consistency and maintainability.
Changing AND/OR grouping merely modifies logical relationships between existing filters and doesn't make the filter independently reusable. Drilling changes the current analytical detail level. A dashboard prompt provides runtime filtering but isn't required simply to reuse the same predetermined filter definition.
Therefore, the appropriate reusable object is a Named Filter saved in the Catalog .
Reference Topic: Formatting Data and Managing Filters, Selection Steps, and Variables - Named Filters
NEW QUESTION # 107
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