Practice Free 1z0-1177-26 Governed Analytics Professional with Oracle Analytics Cloud Exam Questions Answers With Explanation

Pixel-Perfect Reporting with Bursting is specifically designed for high-volume personalized document generation and distribution. Oracle Analytics Publisher executes the underlying report query, splits the resulting data according to a configured Split By key, generates a separate document for each resulting block, and determines formatting and delivery through the bursting delivery definition.

This architecture is well suited to producing 3,200 individualized sales reports. A sales-representative identifier can act as the bursting key, allowing one report definition and one data-extraction process to generate separate reports for each representative. Each segment can also use destination-specific delivery information, templates, output formats, locale settings, and delivery channels.

Oracle documents analogous bursting use cases such as generating individual cost-center reports for managers, customer invoices with customer-specific delivery preferences, and payslips for all employees from one extract. The same design pattern applies directly to thousands of sales representatives.

SMTP only provides an email transport mechanism; it doesn't perform report segmentation. Smart View is an Office-based analytics client. Agents automate analytical delivery but aren't the Publisher document-splitting technology required here.

Therefore, Pixel-Perfect Reporting with Bursting is the scalable solution.

Reference Topic: Managing Report Scheduling, Data Sources, Administration, and Translations — Report Bursting

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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

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A reusable enterprise analysis intended for multiple departments should normally be stored in a Shared Folder within the Oracle Analytics Catalog. Shared Folders are designed for analytics content that needs to be consumed collaboratively and governed through centrally managed permissions.

Oracle recommends storing shared analytics objects in Shared Folders and assigning appropriate access rights to users or application roles. Administrators can create a structured shared-folder hierarchy and configure folder-level permissions so that contained objects inherit the required access controls. This is substantially easier to govern than maintaining separate copies of an analysis for individual users or departments.

By contrast, My Folders/personal folders are intended for content belonging to an individual user and aren't the appropriate location for enterprise-wide reusable objects. Browser storage and temporary caches aren't catalog repositories and provide neither durable analytics-content management nor centralized authorization.

A Shared Folder therefore enables the same analysis to be maintained once while granting departments the appropriate read, modify, or other access rights through catalog security.

Accordingly, option A provides the appropriate combination of reuse, centralized management, and security .

Reference Topic: Managing and Creating Analyses, Views, and Content — Shared Catalog Folders

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A List of Values (LOV) parameter provides the controlled runtime selection required in this scenario. In Oracle Analytics Publisher, a developer first defines a List of Values, which can contain either fixed values or values returned dynamically by a SQL query. The developer then creates a Menu parameter and associates that parameter with the LOV.

For a Department prompt, the LOV might return Department Name as the user-facing label and Department ID as the value passed into the report query. The selected value can then be referenced through a bind variable, allowing the underlying data set to retrieve only data corresponding to the user's department selection.

Oracle Publisher parameters support interaction with report data at runtime, while Menu parameters specifically present predefined lists. This provides controlled input and prevents users from entering arbitrary department identifiers.

Bursting is concerned with splitting and distributing report output. Translation functionality localizes report content. Layout Editor controls report presentation rather than query parameters.

Therefore, the feature that satisfies a predefined department selection before query execution is the List of Values parameter mechanism , making D correct.

Reference Topic: Exploring and Creating Reports and Data Models — Parameters and Lists of Values

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Query Builder provides a graphical mechanism for defining relational SQL data sets. When multiple tables are involved, the developer must first establish the logical relationship between them. Typically, this means creating a join using matching columns , such as a parent table's primary key and a child table's corresponding foreign key.

After the tables and relationships are established, the required output fields are selected by choosing the check boxes beside the column names in Query Builder's design interface. Those selected fields become part of the generated SELECT list. Oracle's Query Builder documentation and examples show precisely this pattern: create the relationship between tables, select the desired columns, optionally define conditions and sorting, and then save the generated SQL.

A report definition doesn't need to be created before building the query. Dashboard definitions are unrelated to Publisher SQL Query datasets. Sample-data generation occurs after the query has been defined sufficiently to execute, and parameters are optional rather than prerequisites for creating table relationships.

Accordingly, the two essential query-definition operations are joining the related tables and selecting the required columns , making C and E correct.

Reference Topic: Exploring and Creating Reports and Data Models — Query Builder, Joins, and Column Selection

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Report Scheduling provides the required automation. Oracle Analytics Publisher's Schedule Report Job interface lets an administrator specify a report, configure its output, select one or more delivery destinations, and define exactly when or how frequently the job executes.

For this scenario, the administrator would configure a recurring schedule for Monday at 6:00 AM, select PDF as the output format, and configure Email as the delivery destination with the management recipients. Oracle's official scheduling tutorial demonstrates the same fundamental workflow: create a report job, select PDF output, configure an email destination, and define the execution schedule.

Runtime Configuration controls system-level processing and output properties; it isn't the scheduler. Template Builder is used to design RTF report templates, while an SQL Data Set retrieves report data and doesn't determine when the report executes.

The uploaded source also identifies Report Scheduling as the mechanism that automates report execution at specified intervals and supports email and other delivery channels. Therefore, the correct answer is B. Report Scheduling .

Reference Topic: Managing Report Scheduling, Data Sources, Administration, and Translations — Report Jobs and Scheduling

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Action Links provide context-sensitive navigation and action execution within Oracle Analytics. They can navigate to BI content, open external web resources, or invoke other supported actions. Presentation variables can participate in navigation scenarios so that contextual values captured at runtime can be supplied when moving between analytical content.

Oracle also explicitly supports conditional Action Links . When an action link is associated with an analysis column, its availability can be controlled by one or more conditions. For example, an escalation action could appear only when Revenue falls below a specified threshold. Oracle documentation describes this as conditionally enabling or displaying the action link according to data-driven criteria.

The remaining statements impose restrictions that do not exist. Action destinations are not limited merely because analyses reside in different catalog folders. Action Links are not restricted to analyses based on one subject area and certainly are not limited to dashboard prompts. Oracle explicitly supports navigation actions that open web pages and other external resources.

Consequently, B and C describe supported Action Link capabilities: runtime contextual navigation and conditional availability.

Reference Topic: Designing, Creating, and Enhancing Dashboards — Action Framework and Conditional Action Links

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The required capabilities are both available through Column Properties ? Column Format . Custom Headings allows the report author to override the default folder or column heading presented in the analysis. This affects the displayed label without modifying the underlying semantic-model column definition. Oracle also permits variables and formatting to be incorporated into custom heading values where appropriate.

Value Suppression controls how consecutive duplicate values are displayed. When the same attribute value occurs repeatedly in adjacent result rows, suppression can display the value only once instead of repeating it on every row. This improves readability in analyses containing grouped or naturally repetitive dimensional data.

Content Paging concerns result retrieval and rendering rather than headings or duplicates. Fixed Headers keeps headings visible during scrolling but does not rename them. Save as Default has a different purpose and does not directly meet either stated requirement.

Oracle's documentation explicitly associates Custom Headings and Value Suppression with the Column Format tab, directly validating B and C.

Reference Topic: Managing and Creating Analyses, Views, and Content — Column Properties and Column Formatting

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Oracle Virtual Private Database (VPD) implements fine-grained, database-level access control by dynamically restricting rows returned to a user. A VPD policy associates a policy function with a protected table, view, or synonym. When a SQL statement references that object, Oracle Database automatically applies the policy-generated predicate to the statement.

For example, a query that conceptually requests every row from a sales table can transparently become a query restricted to the current user's authorized sales region. This makes VPD particularly suitable for reporting environments where multiple users execute similar reports but must see different subsets of the underlying data.

Oracle Analytics Publisher can preserve user identity through proxy-authenticated connections, while the database's VPD policy performs the row-level enforcement. This is fundamentally stronger than filtering sensitive rows only after they have already been returned to Publisher.

Catalog permissions solve a different security problem: they determine who can access or modify report objects. Bursting creates personalized documents but doesn't constitute database row-level security.

Therefore, VPD's defining role in this scenario is database-enforced row-level security , making A correct.

Reference Topic: Managing and Securing Oracle Analytics Publisher — VPD and Row-Level Data Security

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An RTF Subtemplate is designed specifically to centralize reusable report-layout content. Oracle defines a subtemplate as formatting functionality that can be defined once and then reused either multiple times within one layout or across multiple report-layout templates.

Common subtemplate use cases explicitly include headers, footers, address blocks, reusable formatting, formulas, and conditional layout structures . In this scenario, the corporate header and footer can be defined once in an RTF subtemplate, uploaded to the Publisher Catalog, and referenced from each report template. When the common content must change—for example, because of branding or legal requirements—the organization can update the reusable subtemplate rather than manually editing dozens of separate RTF templates.

Separate SQL Data Models control report data, not shared presentation content. Catalog folders can organize templates but don't eliminate duplicate header/footer definitions. Dashboard Prompts are unrelated to Publisher document-template reuse.

Oracle's documentation identifies common headers and footers as primary examples of subtemplate usage. Therefore, the maintainable solution is RTF Subtemplates , making D correct.

Reference Topic: Building and Formatting Reports Using Layout Editor and Template Builder — RTF Subtemplates

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

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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

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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

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D is the intended answer among the supplied choices , because Guided Navigation and dashboard-section conditions are designed to display dashboard content conditionally. Oracle Analytics supports associating conditions with dashboard sections so that a section appears only when the configured condition is satisfied. Historically this behavior has been described as Guided Navigation.

However, the question mixes conditional display with role-based authorization . Oracle's current documentation provides an even more direct mechanism for role-specific dashboard visibility: Dashboard Section Permissions . An administrator can grant or deny section access to specific users and application roles. If the strict requirement is “Warehouse Manager role members only,” section permissions are the stronger access-control mechanism.

That option isn't present in the question. Of the choices supplied, Dashboard Prompt is a filtering mechanism, KPI Watchlist monitors performance metrics, and Analysis Filter controls data rather than dashboard-section access. Guided Navigation is therefore the closest and intended examination answer for conditional display.

For production security design, role-based section permissions should be distinguished from merely conditionally rendering content.

Reference Topic: Designing, Creating, and Enhancing Dashboards — Conditional Sections, Guided Navigation, and Permissions

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Successful authentication proves the user's identity, but it does not automatically authorize access to Publisher catalog content. Oracle explicitly distinguishes authentication from authorization: after a user is authenticated, access to Publisher resources is controlled through application-role mappings, permission grants, catalog permissions, and associated security policies.

Following migration, the administrator should therefore verify that users or groups are correctly mapped to the expected application roles and that those roles retain the necessary catalog permissions on the shared Publisher folders, reports, data models, and any dependent catalog objects. Oracle notes that a role with functional Publisher privileges still can't act on catalog objects until the corresponding catalog permissions are granted.

Database optimizer statistics might affect query performance but wouldn't normally produce a Publisher authorization error. Dashboard layout controls presentation, and browser caching doesn't determine application-role authorization.

The uploaded source's own explanation confirms this authentication-versus-authorization distinction, although its extracted Correct Answer marker is displaced. Oracle documentation independently confirms that Catalog permissions and application role assignments are the first security area to inspect.

Reference Topic: Managing and Securing Oracle Analytics Publisher — Authentication, Authorization, Roles, and Catalog Permissions

A dashboard prompt can affect an embedded analysis only when the analysis is configured to accept the prompt's value. A protected filter deliberately prevents a dashboard prompt from overriding that filter. Oracle describes protected versus unprotected filters specifically as a mechanism controlling whether dashboard prompt values can determine an analysis's results. Therefore, a protected filter is a direct explanation for one of the non-responsive reports.

The second issue is a mismatch between the prompt column formula and the analysis filter formula . Prompt wiring depends on the relevant column identity and formula being compatible. If the analysis uses a modified formula while the prompt targets another formula, Oracle cannot automatically apply the prompt as intended.

By contrast, an Is Prompted filter is specifically intended to receive a prompt value and is therefore normally part of a working configuration. Cache expiration does not explain selective prompt incompatibility, and simply containing measure columns is not itself the documented cause.

The two failed analyses should therefore be inspected for protected filters and formula mismatches between their filtering expressions and the dashboard prompt.

Reference Topic: Formatting Data and Managing Filters, Selection Steps, and Variables — Dashboard Prompt Interaction

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A List of Values (LOV) mechanism and a Date parameter provide the appropriate controls for this requirement. For sales region, users should select from a predefined, controlled collection of valid business values rather than entering arbitrary text. In Publisher, this is normally implemented using a Menu parameter associated with an LOV . The LOV can contain fixed values or be populated dynamically through a SQL query, thereby ensuring that the regions presented to users correspond to valid source-data values.

For the specific-date requirement, Publisher provides the Date parameter type. A Date parameter exposes an appropriate date-selection interface and passes a date value to the underlying data set using the configured date datatype and Java date format.

Boolean parameters are intended for true/false-style conditions rather than selecting geographical members. Expression and XML are not the appropriate interactive parameter controls for these requirements. Technically, Oracle terminology treats the LOV as the value source associated with a Menu parameter; therefore, option C represents the intended region-selection mechanism in this question.

Reference Topic: Exploring and Creating Reports and Data Models in Oracle Analytics Publisher — Parameters and Lists of Values

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Publisher bursting depends on the bursting delivery query to supply the information needed to format and distribute every split report segment. Oracle states that the delivery query returns a mapping containing elements such as the bursting key, template, locale, output format, delivery channel, output name, and channel-specific parameters.

For email delivery, the recipient email address is supplied through the delivery parameters returned by this query. Oracle's own invoice bursting example retrieves CST_EMAIL_ADDRESS from the customer table and places it in the appropriate email parameter. If the query doesn't return a valid email address for a particular bursting key, Publisher lacks the required destination information for that recipient.

The presence of a chart has no bearing on email delivery. Dashboard prompts are unrelated to scheduled bursting, and query caching doesn't supply recipient addresses.

Therefore, when specific recipients are missing email addresses in the bursting-query results, the failure is attributable to incomplete or invalid delivery information in the bursting control query , making B correct.

Reference Topic: Managing Report Scheduling, Data Sources, Administration, and Translations — Bursting Delivery Queries

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The Shuttle buttons are used to transfer data fields between the Available and Selected areas of the Table Wizard. During table construction, Template Builder presents the fields available from the loaded report data. The designer selects the required fields and uses the directional shuttle controls to move those fields into the collection that will become columns in the generated table.

This interface pattern lets the designer explicitly determine which elements appear in the table and often also provides controls to reorder selected fields before the table structure is generated. It avoids the need to manually construct every table cell and Publisher field mapping.

Group Above concerns grouping the resulting data rather than initially selecting the table's columns. Sort By determines ordering of records after the relevant fields are selected. Preview allows the author to inspect generated output and isn't used to transfer fields. Word Properties relates to Microsoft Word document behavior rather than Table Wizard field selection.

The uploaded course material also identifies Shuttle buttons as the correct control for moving fields between Available and Selected lists. Consequently, option C is the direct functional match.

Reference Topic: Building and Formatting Reports Using Layout Editor and Template Builder — Table Wizard

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The intended answer is B and D , but the terminology is version-specific. Oracle SQL Developer historically used the connection type Cloud PDB for wallet-based Autonomous Database connections. The user selected Cloud PDB, browsed to the downloaded client-credentials ZIP file, supplied the wallet/keystore password, selected the database service, tested the connection, and saved it.

Oracle subsequently renamed this connection type Cloud Wallet . Current Oracle documentation specifies Cloud Wallet, the client-credentials configuration file, database user credentials, and an appropriate service name. Oracle explicitly notes that Cloud PDB is the older name , including in SQL Developer 18.2-era workflows. Therefore, option B remains the intended examination choice because Cloud Wallet isn't offered among the options.

The service list represents database service names such as high, medium, or low service configurations; it doesn't represent OCI compartments. ODBC and Basic connections aren't the wallet-based configuration described.

Accordingly, B and D match the historical workflow reflected by the question, while candidates should recognize that Cloud Wallet is the current SQL Developer terminology .

Reference Topic: Connecting to Data Sources — SQL Developer Connections to Autonomous Data Warehouse

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Template Builder for Microsoft Word provides dedicated translation tools for extracting translatable text from an RTF template into an XLIFF (.xlf) file . Oracle's documented procedure is to open the template with Template Builder installed and, from the Template Builder tools, choose Translation ? Extract Text . Publisher then extracts the template's translatable boilerplate strings and creates an XLIFF file.

Typical translatable strings include report titles, table headers, field labels, and static textual content intended to appear in generated output. Data supplied dynamically at runtime isn't extracted as template translation text.

The resulting XLIFF file can then be provided to a translator or edited with an XLIFF-compatible tool. After translation, it is associated with the original report template for the appropriate locale. Preview Translation is used later to test an already-created translation; it doesn't generate the initial XLIFF file. Uploading a localized RTF file represents a different localization strategy. Changing the user's report locale also doesn't perform text extraction.

Thus, Translation ? Extract Text is the specific Template Builder command that creates the XLIFF translation file.

Reference Topic: Building and Formatting Reports Using Layout Editor and Template Builder — XLIFF Translation

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Oracle Analytics Publisher layouts can contain multiple report components within the same layout , allowing different representations of a report's data to be assembled into a single formatted document. The Layout Editor Components pane includes visual and tabular components such as charts, pivot tables, tables, images, and other elements that can be dragged into the report layout.

For this finance-report requirement, the author can place a summary chart near the top, use a pivot table for multidimensional financial analysis, and include a conventional table for transaction-level detail. Layout grids and other layout controls can be used to position these components appropriately within the report.

A Dashboard Prompt is intended for runtime filtering and doesn't construct Publisher layout sections. Repository Variables provide centrally managed values rather than report-design components. Data Gateway supplies remote source connectivity.

Oracle documentation explicitly states that Layout Editor provides a drag-and-drop interface and that its Components pane includes objects such as charts and pivot tables. This architecture supports combining the required presentation elements within one report.

Therefore, C. Multiple layout components within a single report is correct.

Reference Topic: Building and Formatting Reports Using Layout Editor and Template Builder — Layout Components

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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

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Before migrating a semantic model to Oracle Analytics Cloud, database connection information in every relevant connection pool must be updated for the cloud database. Oracle specifically requires the Call Interface to be Oracle Call Interface (OCI) for Oracle Database connections. In addition, for Oracle Analytics Cloud migration scenarios, the Data Source Name must contain the full connect string for the database containing the replicated data.

Oracle's current semantic-model migration guidance is explicit that a net service name isn't sufficient in this OAC scenario. This differs from some Oracle Analytics Server deployments where tnsnames.ora aliases can be supported. The cloud migration workflow therefore requires administrators to examine each connection pool, not merely the first connection associated with a database object.

ODBC isn't the recommended Oracle Database interface for this configuration, and restarting the BI Server after every individual connection-pool modification isn't part of the specified preparation procedure.

Thus, the required combination is OCI as the call interface and the complete database connect string as the Data Source Name , exactly matching D and E.

Reference Topic: Connecting to Data Sources and Migrating Artifacts — Preparing Semantic Models/RPDs for Oracle Analytics Cloud

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For device delivery, Oracle Analytics distinguishes between Active Delivery Profile and Specific Devices . Active Delivery Profile lets each user determine delivery behavior through personal delivery preferences. When the Agent must instead target explicitly chosen device types, the author selects Specific Devices and identifies the permitted devices. This satisfies the requirement to avoid reliance on the user's active profile.

For cache seeding, Oracle documents a dedicated Agent workflow. After choosing the analysis or dashboard page and establishing recipients, the administrator clears ordinary user destinations and selects the Analytics Server/BI Server Cache destination. Scheduled execution then populates the query cache using the results generated by the Agent.

The terminology in the question uses Oracle BI Server Cache , while current Oracle Analytics Server documentation refers to Oracle Analytics Server Cache ; functionally, this is the same cache-seeding destination represented by option E.

Selecting Never wouldn't schedule cache seeding. Home Page and Dashboard creates analytics alerts rather than restricting delivery to email devices. Active Delivery Profile does the opposite of the requirement, and Printer isn't the standard cache-seeding destination.

Therefore, the correct selections are Specific Devices and Oracle BI Server Cache .

Reference Topic: Managing Administration, Security, and Performance — Agent Destinations and Cache Seeding

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