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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Exploring and Creating Reports and Data Models in Oracle Analytics Publisher | 5% | - Creating reports - Creating and managing data models |
| Topic 2: Designing, Creating, and Enhancing Dashboards in Oracle Analytics | 10% | - Creating and designing dashboards - Enhancing dashboards and briefing books - Dashboard prompts and pages |
| Topic 3: Understanding Oracle Analytics Cloud | 5% | - Oracle Analytics Cloud concepts and capabilities |
| Topic 4: Connecting to Data Sources and Migrating Artifacts to Oracle Analytics Cloud | 10% | - Connecting to data sources - Migrating analytics artifacts |
| Topic 5: Managing Report Scheduling, Data Sources, Administration, and Translations in Oracle Analytics Publisher | 15% | - Publisher administration - Bursting and delivery - Report translations - Scheduling reports - Managing report data sources |
| Topic 6: Formatting Data and Managing Filters, Selection Steps, and Variables in Oracle Analytics | 10% | - Managing filters - Using prompts and variables - Managing selection steps - Formatting data |
| Topic 7: Managing and Securing Oracle Analytics Publisher in Oracle Analytics Cloud | 10% | - Managing Oracle Analytics Publisher - Securing Publisher content and access |
| Topic 8: Managing and Creating Analyses, Views, and Content in Oracle Analytics | 10% | - Creating and managing analyses - Managing analytics content - Creating and managing views |
| Topic 9: Building and Formatting Reports Using Layout Editor and Template Builder | 15% | - Using Layout Editor - Using RTF Template Builder - Creating Excel and eText templates - Formatting and customizing reports |
| Topic 10: Managing Administration, Security, and Performance in Oracle Analytics Cloud | 10% | - Security and application roles - Performance and caching - Administration |
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NEW QUESTION # 85
Which Oracle Analytics Cloud administrative feature allows scheduled execution and delivery of analyses or alerts when predefined conditions are met?
Answer: B
Explanation:
Agents are the intended Oracle Analytics capability for automatically executing analytical content according to a schedule or business condition and then delivering the resulting information to users or configured destinations. An agent can run periodically, evaluate a condition, select analytical content, identify recipients, and determine delivery destinations.
For example, an agent can evaluate an analysis each morning and deliver an alert only when inventory drops below a defined threshold. Oracle's current Oracle Analytics Cloud interface increasingly uses the term Delivery for this functionality, but the underlying scheduled/conditional delivery framework is the same capability historically identified as Agents in Classic Analytics.
Selection Steps control which members appear in analysis results. Trellis Views provide repeated comparative visualizations. Subject Areas expose governed business data from the semantic model. None of those features performs condition-driven scheduled delivery.
There is an internal error in the uploaded source: the placement of its "Correct answer" marker makes Selection Steps appear keyed, while the source explanation explicitly says that Agents automate execution and distribution based on schedules or conditions . Oracle documentation supports Agents/Deliveries, so the verified answer is A .
Reference Topic: Managing Administration, Security, and Performance - Agents, Deliveries, Conditions, and Scheduling
NEW QUESTION # 86
A business analyst wants to create governed reports while ensuring all users calculate Revenue consistently across departments. Which Oracle Analytics Cloud feature best supports this requirement?
Answer: C
Explanation:
A semantic model with shared business definitions is the correct mechanism for governed enterprise analytics. Oracle defines the semantic model as a metadata layer that maps underlying physical data sources into logical dimensions, measures, hierarchies, relationships, calculations, and presentation objects that reflect how the organization understands its business. This enables centrally defined measures such as Revenue to be calculated consistently regardless of which analyst, dashboard, or analysis consumes them.
After deployment, the semantic model is exposed to users through subject areas . Analysts can create analyses and dashboards using business-friendly objects without needing to understand physical schemas or reproduce calculation logic independently. Centralizing Revenue logic also reduces discrepancies caused by users implementing different spreadsheet formulas or workbook-specific calculations.
CSV files, personal formulas, and independently maintained workbook calculations are useful for self-service scenarios but don't provide the same level of semantic governance, consistency, security, or reusable enterprise definitions.
Therefore, option A directly satisfies the requirement for a trusted and consistently calculated Revenue metric across departments.
Reference Topic: Understanding Oracle Analytics Cloud - Semantic Models and Governed Analytics
NEW QUESTION # 87
An analyst wants an analysis to always display data for the currently logged-in user's assigned sales region without requiring manual input. Which variable type is most appropriate?
Answer: D
Explanation:
A Session Variable is the appropriate choice because its value is associated with an individual user's Oracle Analytics session. Oracle states that session variables are initialized when a user signs in and remain available throughout that user's browsing session. A non-system session variable can therefore hold contextual values such as the user's department, organizational unit, or SalesRegion .
For this scenario, an initialization block can retrieve the user's assigned region during authentication. An analysis can reference that session variable in a data filter, causing each user to receive automatically filtered results without entering a region manually. Oracle explicitly uses user department and sales region as examples of session-variable values.
A Presentation Variable is normally populated interactively through a dashboard or analysis prompt, which violates the requirement for no manual input. A semantic model/repository variable generally holds a value shared more broadly rather than a user-specific login value. A Request Variable temporarily overrides a session variable for an individual request.
Therefore, Session Variable directly provides the required user-specific, login-initialized analytical context.
Reference Topic: Formatting Data and Managing Filters, Selection Steps, and Variables - Session Variables
NEW QUESTION # 88
A finance department needs monthly invoices to be automatically distributed as separate PDF files to each customer based on customer email addresses stored in the data model. Which Oracle Analytics Publisher feature should be used?
Answer: B
Explanation:
Report Bursting is specifically designed to split a single report execution into multiple personalized documents and deliver each document according to recipient-specific information. In this scenario, the finance department can use Customer ID as the bursting Split By and Deliver By key. The bursting delivery query can retrieve each customer's email address, output format, template, locale, and other delivery parameters from the underlying data.
Oracle provides a documented invoice bursting example in which CUSTOMER_ID identifies each report segment, the output format is selected dynamically, the delivery channel is EMAIL, and the customer's email address is supplied by the bursting query. The same architecture permits thousands of invoices to be generated from one report definition and distributed independently.
Layout Editor controls visual report design but doesn't automate recipient-specific splitting. Dashboard Prompts provide runtime filtering, and Conditional Formatting changes presentation according to conditions.
Therefore, Report Bursting precisely satisfies both requirements: generating a separate PDF for each customer and automatically delivering each document to the corresponding email address.
Reference Topic: Managing Report Scheduling, Data Sources, Administration, and Translations - Bursting and Delivery
NEW QUESTION # 89
A development team has completed new dashboards and BI Publisher reports. Before deploying them to production, they need a repeatable migration process that preserves dependencies and configuration. Which approach is recommended?
Answer: B
Explanation:
Oracle Analytics Cloud uses snapshots , which can be exported as BAR files , to provide a controlled and repeatable mechanism for migrating analytics content between environments. A snapshot can package supported service content and configuration, including catalog objects such as dashboards and analyses, Publisher artifacts, semantic-model content, application-role information, and other applicable configuration.
Oracle's documented migration workflow is to create a snapshot in the source environment, export the snapshot as a .bar file, import that BAR file into the target Oracle Analytics Cloud instance, and then restore the required components. The Custom restore option allows administrators to select which elements are transferred when the target environment contains configuration that must remain unchanged.
Manual catalog copying does not provide the same dependable migration of dependencies and service configuration. Recreating dashboards in production is inefficient and prone to configuration drift. PDF files are generated outputs and cannot recreate Analytics or Publisher objects.
Therefore, the repeatable Oracle-supported deployment mechanism represented in the choices is exporting and importing a snapshot using a BAR file .
Reference Topic: Connecting to Data Sources and Migrating Artifacts - Snapshots and BAR Migration
NEW QUESTION # 90
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