Explanation:

✅ Correct Option: B
The dataset explorer allows modification of both field labels and default fields. Designers can rename API labels into user-friendly names directly within the dataset, and the default fields (and their order) that appear in a values table are defined at this level. This ensures consistent labeling and ordering across dashboards without manual edits in every widget.

❌ Option A
Changing field order in the JSON of a value table widget is not the recommended or sustainable approach. While JSON editing allows some customization, it only applies to a single dashboard widget and doesn’t fix the root cause: how default fields are set at the dataset level. This creates inconsistency when using fields in new widgets.

❌ Option C
Field labels cannot be changed only in widget properties. Editing labels in widget properties affects presentation for that widget alone but does not address the broader issue of API labels appearing across the dataset. Without modifying the dataset explorer, labels will continue to display in API form elsewhere.

Summary:
To resolve both the API label display and random field order issues, the consultant should point the designer to the dataset explorer, where field labels and defaults are set. This provides consistent naming and ordering across all dashboards, rather than relying on per-widget or JSON-level fixes.

Reference:
Salesforce Help: Dataset Explorer in CRM Analytics

Explanation:

In Salesforce CRM Analytics, conditional formatting (e.g., color-coding based on thresholds) and widget-specific number formats (e.g., currency, decimals) for a dashboard are stored in the Extended Metadata (XMD) file, not the dashboard JSON. When the administrator manually copy-pasted the dashboard JSON from the developer sandbox to the full sandbox, they omitted the XMD file, causing the loss of these formatting settings. The XMD file ( < dashboardname >. xmd . json ) defines rules like conditional highlighting and number formats, and without it, the dashboard reverts to default settings, resulting in the observed issue.

Why the Other Options Are Incorrect:

Option A (Analytics Dataset XMD was NOT included): The dataset XMD governs field-level metadata (e.g., labels, default aggregations) for the dataset, not dashboard-specific formatting like conditional formatting or widget number formats. Since the issue pertains to dashboard appearance, not dataset field display, the dataset XMD is irrelevant.
Option B (The recipe that generated the dataset also needs to be included): The recipe is responsible for data transformations to create the dataset, not for dashboard formatting. The dataset was successfully deployed via a change set, and the issue is specific to dashboard formatting, making the recipe’s inclusion unnecessary.

References:
Salesforce Help: "Deploy Dashboards in CRM Analytics"
Developer Docs: "Extended Metadata (XMD) for Dashboards"

Explanation:

This question tests the consultant's understanding of how to manage the refresh schedule for an entire templated app and its associated data pipeline in CRM Analytics.

Why C is Correct:
Templated apps (like Sales Analytics) are designed as integrated, pre-built solutions. When you install such an app, it creates a complex, interdependent set of assets (dataflows, recipes, datasets, lenses, dashboards, and Einstein Discovery models). The App Install History page provides a centralized "master schedule" for the entire app. Scheduling from this location ensures that all the underlying components run in the correct, managed sequence. Scheduling the app itself guarantees that dataflows run first to bring in raw data, then recipes transform it, and finally, any dependent Einstein Discovery models are retrained—all automatically and in the right order.

Why A is Incorrect:
While technically possible, this is a highly inefficient, error-prone, and non-scalable approach. Manually scheduling each individual asset (every recipe, dataflow, connector, and Einstein model) is tedious. More importantly, it breaks the managed dependencies. You risk a recipe trying to run before its source dataflow has finished, or an Einstein model retraining before its source dataset is updated, leading to data inconsistencies and failures.

Why B is Incorrect:
This is an improvement over option A but is still incomplete. Scheduling only the recipes and dataflows would update the core datasets. However, it would not automatically trigger the refresh of the Einstein Discovery models and prediction definitions. These are separate assets that rely on the updated datasets. Using the App Schedule is the only method that encompasses the entire pipeline, including Einstein assets.

Key Concept
Managed App Schedules: The key concept here is that a templated app is a managed package of analytics content. The platform provides a top-level scheduling mechanism (App Install History) specifically to handle the orchestration of all its components. This is the Salesforce-recommended and most robust method for ensuring a consistent and reliable data refresh for the entire application.
Orchestration: A critical part of a consultant's role is understanding data pipeline dependencies. The App Schedule handles the orchestration automatically, eliminating the need for complex manual workflow management.

Explanation:

Why this is correct:
Asset Visibility lets app Managers/Editors hide specific dashboards (and other assets) within an app so that they’re not visible to Viewers. Hidden assets remain visible to users with Manager/Editor access (your sales managers) but are hidden from Viewers (your sales reps). This is exactly the use case: keep in-progress dashboards visible to managers only without duplicating content or changing app shares.

Why the others are wrong:
A. Remove from ‘Run App’ navigation list — Hiding from nav isn’t security; reps could still access direct URLs or see assets via other entry points. Asset Visibility is the supported control for per-asset visibility within an app.
B. Duplicate dashboards/datasets into a separate app — This adds maintenance overhead, risks data drift, and isn’t necessary. Asset Visibility solves the requirement without duplicating assets. (Salesforce recommends using Asset Visibility to “control who sees what in an app.”)

References:
Salesforce Help, Control Who Sees What in an App with Asset Visibility (release notes/help): describes the Hide action for assets in an app and its effect by access level.
Salesforce Help, Build CRM Analytics Dashboards: points to Asset Visibility for per-asset control in apps.

Explanation:

This question tests the understanding of how to assign CRM Analytics licenses and permissions efficiently and in accordance with Salesforce best practices.

Why C is Correct:
Permission sets are the standard and recommended way to grant granular access to features in Salesforce without modifying user profiles. In this scenario, the client has three distinct user personas:
Builders (3 users): Need CRM Analytics Data Manager and CRM Analytics Dataflow Manager permissions to build datasets and dashboards.
Dashboard Creators (15 users): Need CRM Analytics Creator permission to create dashboards but not manage datasets.
Viewers (~982 users): Need CRM Analytics Consumer permission to only view dashboards.

The consultant should recommend creating three separate permission sets, each containing the corresponding CRM Analytics permission license (Data Manager, Creator, or Consumer). These permission sets are then assigned to the respective users. This is scalable, manageable, and follows the principle of using permission sets for functional access rather than creating multiple profiles.

Why A is Incorrect:
App permissions (Viewer, Editor, Manager) control what a user can do within a specific Analytics app (e.g., view, modify, or manage the app's dashboards and datasets). However, these permissions are secondary. A user must first be assigned a CRM Analytics permission license (via a profile or permission set) to even log in to the Analytics Studio. You cannot assign app permissions to users who do not have a base license. Option A addresses the second step without solving the fundamental licensing requirement.

Why B is Incorrect:
While creating three new Salesforce profiles would technically work, it is considered a poor practice and is not scalable. Profiles are complex and control a very wide range of system permissions and settings across the entire Salesforce org. Creating multiple profiles for the sole purpose of managing CRM Analytics access is an administrative burden and can lead to unnecessary complexity in the overall user management system. Permission sets are the modern, flexible, and recommended tool for this specific purpose.

Reference
Salesforce Help: Assign CRM Analytics Permissions Licenses
This documentation outlines the process, stating: "You grant access to CRM Analytics by assigning permission sets that contain CRM Analytics permissions licenses to users." It explicitly recommends using permission sets to assign the core licenses: CRM Analytics Consumer, CRM Analytics Creator, CRM Analytics Data Manager, and CRM Analytics Dataflow Manager.

Key Concept: The process is a two-step assignment:

License Assignment: A user gets a functional license (Consumer, Creator, etc.) via a Permission Set.
App-Level Permissions: After being licensed, the user is then granted specific permissions (Viewer, Editor, Manager) within individual Analytics apps to control what they can see and do inside that app.