Explanation:

✅ Correct Answer (A):
The Purpose-Structure-Surface (PSS) framework is a recognized set of design principles, often applied in user experience (UX) and data visualization, that aligns perfectly with the discovery phase of a CRM Analytics project.

Purpose: The "Why." This phase focuses on understanding the user's goals and needs. What business questions does the dashboard need to answer? What is the user trying to achieve? This is the most critical part of the discovery session.

Structure: The "What." This phase is about determining the metrics, KPIs, and data elements required to fulfill the purpose. What data needs to be included? How should the charts and tables be organized to tell a clear story?

Surface: The "How." This is the final phase, focusing on the visual design. How should the dashboard look? What colors, fonts, and chart types should be used to make the information clear and easy to understand? This is the least important part of the discovery phase.

By following PSS, the team ensures the dashboard is meaningful and useful (Purpose) before focusing on its layout (Structure) and visual appeal (Surface).

❌ Incorrect Answers:

B. Priority - Logic - Level of Granularity
Reason: While these concepts are important in analytics, they are not the main principles of "Deep Design Thinking" in this context. "Priority" and "Level of Granularity" fall under the "Structure" principle, and "Logic" is an underlying principle of the entire build, but they are not the overarching framework.

C. Clarity - Efficiency - Consistency
Reason: These are excellent principles for the final dashboard design and user experience. They are aspects of the "Surface" and "Structure" phases but do not represent the full, user-centric discovery process. They are principles of good design rather than a framework for defining the core business problem and solution.

Explanation:

“Tablet” or “Phone” layout—where only minWidth and maxWidth have been set—may be displayed on a desktop if the dashboard is embedded in a small frame, or if the browser window is small.

Why:
CRM Analytics selects a layout based on the rendered viewport/iframe size. When a dashboard is embedded in a narrow iframe or the browser window is reduced, device-specific layouts (like Phone/Tablet) can be triggered—even on a desktop. Salesforce notes that embedded views can default to a mobile/device layout depending on dimensions, and dashboard layouts use width constraints (minWidth/maxWidth) in their JSON.

Why not A:
There isn’t a special “dashboard” vs. “phone” property parity to make mobile behave “like desktop.” In CRM Analytics you define layouts (e.g., Default/Mobile) with properties such as minWidth/maxWidth—not a “dashboard” property for desktop and a “phone” property for mobile.
Why not B:
If no mobile-eligible layout matches, the dashboard doesn’t throw a mobile error; it falls back to another layout based on size. The issue is layout selection, not an error condition on mobile while staying fine on desktop.

References:
Salesforce Help: Embedded view defaults to Mobile layout in Browser (layout chosen by embed size).
Salesforce Dev Docs: gridLayouts Properties (use of maxWidth/minWidth in layout selection/arrangement).
Salesforce Dev Docs: Dashboard JSON Overview (how layouts are defined in dashboard JSON).

Explanation:

When CRM Analytics (data sync/direct data) pulls currency fields from Salesforce, the platform converts them by default to the org’s default/corporate currency unless you explicitly preserve original currency values. If UC’s corporate currency is USD, synced currency fields will appear in USD on datasets/dashboards even if the source records are in GBP.

FYI:
Salesforce added a setting for Salesforce Direct Data in recipes to preserve original currency values (i.e., skip the automatic conversion to the default org currency). If you don’t use that, you get corporate-currency values.

Why the others are wrong
A. “ANS local currency is USD” — There’s no CRM Analytics setting called “ANS local currency.” Not a relevant concept/config.
B. “Integration User currency is USD” — The authoritative behavior is that data sync converts to the org’s default/corporate currency by default, not the integration user’s personal currency. (Some blogs/community posts mention the integration user, but Salesforce’s own docs state conversion is to default org currency.)

Reference
Salesforce Help (Release Notes): Preserve Original Currency Values for Salesforce Direct Data — “By default, all currency fields are converted to the default org currency on all data syncs.”
Salesforce Help: Set Your Corporate Currency — explains corporate currency for multi-currency orgs.

Explanation:

In CRM Analytics, access to dashboards is controlled at the app level.
Row-Level Security (RLS) limits what data a user can see within a dataset or dashboard.
App sharing determines which dashboards and datasets a user can even open.
Therefore:
You can use the same dataset (with RLS to restrict sales reps’ data visibility) for both dashboards.
But you must separate dashboards into different apps to restrict dashboard-level access.
This prevents sales reps from even seeing or accessing the National Performance dashboard.

Why not B:
Creating two datasets is unnecessary and introduces maintenance complexity. Both dashboards can share one dataset with proper RLS.
Why not C:
Placing both dashboards in the same app exposes the National Performance dashboard to all users with app access — even if RLS hides the data, they can still open and view its structure, which violates the requirement.

Reference:
Salesforce Help: Share CRM Analytics Apps, Dashboards, and Datasets
Salesforce Trailhead: Control Access to CRM Analytics Data (App-level vs. Row-level security)

Explanation:

🟢 Correct Answer (C):
Components: Components in CRM Analytics are reusable dashboard elements. A component can be a single widget or a group of widgets that can be created once and then added to multiple dashboards. This is the best practice for maintaining consistency and efficiency.

Maintenance: If the team needs to update the widget (e.g., change the visualization, add a new measure, or adjust the filter), they only need to edit the single component. The change will automatically be reflected on all dashboards where the component is used. This prevents the need for manual, repetitive updates.

🔴 Incorrect Answers:

A. Create/Edit a lens and add each dashboard.
Reason: A lens is a way to explore a dataset. While you can create a widget from a lens, the lens itself is not a reusable dashboard element that can be centrally maintained across multiple dashboards. If you change a lens, it won't automatically update widgets created from it on different dashboards.

B. Create/Edit the widgets individually on each dashboard.
Reason: This is the most inefficient and error-prone method. It's what the team is likely doing now. Each time a change is needed, they have to manually replicate the change on three different dashboards. This approach is not scalable and increases the risk of inconsistencies.