Summary:
Universal Containers has been live with Revenue Cloud for 90 days and now sales leaders want visibility into KPIs such as MRR, ARR, renewal rates, upcoming renewals, and overall account financial health. These metrics are tied directly to subscription performance and renewal forecasting. Salesforce provides Subscription and Revenue Lifecycle Analytics as an out-of-the-box analytics package designed to deliver these exact recurring-revenue insights without custom development.

Correct Option:

B — Subscription and Revenue Lifecycle Analytics
This dashboard suite provides prebuilt insights into subscription trends, recurring revenue metrics (MRR/ARR), churn, renewal performance, and account-level financial health. It also includes renewal forecasting and tracking of accounts with upcoming renewals. It is specifically built for SaaS and recurring-revenue models, making it the best match for the requested analytics.

Incorrect Options:

A — Order Analytics:
Order Analytics primarily focuses on order fulfillment, order progression, order fallout, and operational efficiency. It does not provide recurring revenue KPIs like MRR, ARR, or renewal rates. Therefore, it doesn’t meet the sales leaders’ needs for subscription and renewal metrics.

C — Pricing Analytics:
Pricing Analytics centers around pricing performance, price optimization, discounting behavior, margin visibility, and deal analytics. It does not track renewal rates, recurring revenue metrics, or subscription lifecycle performance, so it is not suitable for this requirement.

Reference:
Salesforce Revenue Cloud Documentation — Subscription and Revenue Lifecycle Analytics, Einstein Analytics for Revenue Cloud, Recurring Revenue Dashboards.

Summary:
Monthly Recurring Revenue (MRR) is a forward-looking metric that represents the normalized, monthly value of a subscription. For a ramped deal, MRR is calculated based on the current period's commitment, not the total contract value. On January 15, 2026, the subscription is in its first year (Year 1), which has a committed quantity of 20. Therefore, the annual recurring revenue for the current period is 20 * $1,000 = $20,000. To find the MRR, this annual amount is divided by 12 to get a monthly figure.

Correct Option:

A: MRR = (20 × $1,000) / 12
This is the correct formula. MRR reflects the predictable revenue for a single month based on the current state of the subscription.

Since the "today's date" (January 15, 2026) falls within the first year of the term (June 24, 2025, to June 23, 2026), the active commitment is 20 units.

The annual value for this commitment is 20 × $1,000 = $20,000. Dividing this by 12 months gives the normalized Monthly Recurring Revenue of $20,000 / 12 ≈ $1,666.67.

Incorrect Option:

B: MRR = (20 × $1,000) / 36
This is incorrect. Dividing by 36 (the total number of months in the 3-year term) calculates an average monthly value over the entire contract's lifetime. MRR is not an average of the total contract value; it is the revenue recognized for the current billing period. This method undervalues the current revenue commitment.

C: MRR = ((20 × $1,000) + (30 × $1,000) + (40 × $1,000)) / 36
This is incorrect. This formula sums the total contract value across all three years and then divides by the total months to get a straight-line average. This is not how MRR is calculated. MRR is based on the active subscription period, not an average of future, unfulfilled commitments. This would inaccurately inflate the current MRR by including future years' quantities.

Reference:
Salesforce Help: "About Ramped Deals" - While not providing an explicit MRR formula, the concept of ramped deals is that the subscription asset has multiple Asset State Periods, each with its own quantity. MRR is derived from the quantity and price of the currently active period, which aligns with the logic in Option A. Standard SaaS finance principles define MRR as the monthly value of the current recurring revenue.

Summary:
Contracts AI extracts clauses and field data from contract documents using Contract Extraction Templates. These templates determine which fields, clauses, and metadata are pulled from uploaded PDFs. If a new custom field—such as Payment Method—needs to be included after the first extraction, the consultant must update the extraction template so the AI engine knows to look for and map this new field. Only modifying the extraction template enables AI to retrieve it on subsequent runs.

Correct Option:

A — Modify the contract extraction template to define the attribute mapping and context mapping for payment method
Contract extraction templates are the core configuration layer for Contracts AI. They control which fields are extracted, how values are mapped, and what contextual rules guide the extraction. By adding the new custom field to the template’s attribute mapping and context mapping, AI will identify and extract the field from the PDF. This is the correct and supported method for updating extraction requirements.

Incorrect Option:

B — Modify the AI prompt template
AI prompt templates control the language and structure used when presenting extracted clause summaries or generating contract intelligence, not the extraction rules themselves. Prompt templates do not define field mappings or determine which custom fields Contracts AI retrieves from documents. Changing the prompt would not enable extraction of the payment method field.

C — Log a support case with Salesforce
Salesforce support is not required to add new fields to Contract Extraction Templates. The consultant has full admin-level capability to update extraction templates directly in the Contracts AI settings. This option would be unnecessary and would not provide any configuration benefit, as custom fields are meant to be self-managed in templates.

Reference:
Salesforce CLM & Contracts AI Documentation → Contract Extraction Templates, Attribute Mapping & Context Mapping for Field Extraction.

Summary:
After a merger, there is a need to distinguish assets managed by the native Revenue Cloud (CPQ/Billing) lifecycle from those managed by a custom application. Revenue Cloud automatically manages a specific field on the Asset object to indicate if it is under its governance. This system-maintained field is the most reliable and out-of-the-box method to differentiate the two asset types without requiring custom development or manual data entry.

Correct Option:

A: Use HasLifecycleManagement to identify the source as Revenue Cloud or Custom App.
This is the correct out-of-the-box solution. The HasLifecycleManagement field is a standard checkbox field on the Asset object.

When an asset is created and managed by Revenue Cloud (via an order activation or amendment), this field is automatically set to true.

Assets created by the custom Cloud Kicks app would have this field remain false, providing a clear, system-defined distinction between the two sources.

Incorrect Option:

B: Use a custom field to identify the source as Revenue Cloud or Custom App.
While this would work, it is not an "out-of-the-box" solution. It requires a developer or administrator to create a new field, modify the custom Cloud Kicks app to populate it, and potentially use triggers or processes to ensure data integrity. This adds unnecessary complexity when a standard, automatic field already exists for this purpose.

C: Use Asset Action Source to identify the source as Revenue Cloud or Custom App.
This is incorrect. The AssetActionSource is a field on the Asset Action object, not the Asset itself. It is used to track the specific source (like an API or UI) that created an Asset Action record during an event like an activation or amendment. It does not provide a persistent, high-level classification for the asset's overall management system.

Reference:
Salesforce Help: "Asset Fields" - The official documentation for the Asset object lists the HasLifecycleManagement field and describes it as "Indicates whether the asset is managed by Salesforce CPQ (true) or not (false)." This is the definitive out-of-the-box method to differentiate Revenue Cloud-managed assets from others.

Summary:
When a product catalog requires real-time filtering based on customer attributes like tier, location, or purchase history, Revenue Cloud uses context definitions to pass this data into qualification rules. These context definitions provide the necessary variables that rules evaluate during product selection in the Browse Catalog or Product Discovery interface. Proper configuration ensures that only eligible products are displayed to the sales rep, streamlining the quoting process and reducing errors.

Correct Option:

A — Configure a Product Discovery context definition with nodes and attribute mappings for account tier and location, so this data can be evaluated by the rules during Browse Catalog
This is correct because a Product Discovery context definition allows mapping of external data (such as fields from the Account or Opportunity) into the context that qualification rules can evaluate. By configuring nodes and attribute mappings for relevant customer data (e.g., tier and location), the system dynamically filters products in real time, ensuring accurate and personalized catalog visibility during quoting.

Incorrect Options:

B — Create a Sales Transaction context definition to control product visibility based on user profile
While Sales Transaction context definitions exist, controlling product visibility based solely on user profile does not address dynamic filtering by customer-specific data like tier, location, or purchase history. This option is not sufficient for real-time customer-based eligibility.

C — Use the Product2 object to create custom fields and page layouts
Custom fields and layouts on Product2 do not provide dynamic, context-aware filtering. Page layouts are static and cannot enforce eligibility rules in real time during the quote process. Context definitions are required for this level of dynamic behavior.

Reference:
Salesforce Revenue Cloud Documentation → Product Discovery Context Definitions, Qualification Rules and Context Mapping, Dynamic Product Filtering in Quotes.