Data-Con-101 · Domain 5 of 6

Data-Con-101 Domain 5: Data Enhancements, Sharing, and Analysis

Data Enhancements, Sharing, and Analysis covers building insights on unified data, exposing Data 360 data to CRM and external systems, reporting with Data 360 reports and Tableau, and applying Einstein Studio and generative AI tooling to customer scenarios.

Exam weight
18%
Official objectives
4
Estimated questions estimate
~11
Suggested study time estimate
13–15 hrs

About 13 min read Reviewed October 2026

Overview

This section covers what happens after profiles are unified. Its four objectives ask you to demonstrate how to enhance data and build insights on unified data, explain how to reference Data 360 data from other systems, view and build reports and dashboards using Data 360, and apply predictive and generative AI tooling to customer scenarios.

Enhancement tools include calculated insights, which compute metrics such as lifetime value or engagement scores in batch, and streaming insights for near real-time windows. Data graphs package a profile with its related data for fast lookups. Sharing tools include enrichments such as related lists and copy fields on CRM records, the Query and Connect APIs, and Zero Copy sharing. Analysis covers Data 360 reports and dashboards, Tableau Next and CRM Analytics. AI tooling covers Einstein Studio models, bring-your-own models and LLMs, search indexes, retrievers and prompt templates grounded in Data 360.

How it's tested

Questions describe a metric, a place where users need data, a reporting request or an AI goal. You choose the insight type, enrichment, API, reporting tool or AI capability that meets the requirement with standard features and the right freshness.

  • Build the metric

    A business measure is described, and you choose a calculated insight, streaming insight or data graph and define its dimensions and measures.

  • Show it elsewhere

    Users in Sales Cloud, Service Cloud or an external tool need Data 360 data, and you pick an enrichment, API or Zero Copy share.

  • Apply the AI tool

    A predictive or generative goal is given, and you match it to Einstein Studio, a search index, a retriever or a grounded prompt.

Reasoning it through

A service manager wants agents to see each customer's total spend over the last 12 months on the Contact record. The value requires aggregating orders across sources for unified individuals, so a calculated insight fits. A copy field enrichment then writes the insight value to a Contact field, which agents see without leaving Service Cloud and without custom code.

Objectives 4

Official objectives from the Salesforce outline, in outline order.

5.1 Demonstrate how to enhance data and build insights on unified data.Calculated insights, streaming insights and data graphs.
What it means
Calculated insights use SQL or a visual builder to compute measures, such as total spend or order count, grouped by dimensions such as unified individual. They run on a schedule over historical data. Streaming insights compute metrics over short time windows from engagement data as it arrives. Data graphs combine a profile with related objects for fast retrieval.
What to understand
A calculated insight needs at least one measure and one dimension, and it can reference unified profiles and their related data model objects through defined relationships. Use calculated insights for lifetime and period metrics used in segments and enrichments. Use streaming insights for near real-time triggers, such as three cart views in ten minutes, often paired with data actions. Data graphs suit real-time personalization and agent grounding where an app needs a whole profile in one call.
How it shows up
A metric and its freshness need are described, and you choose calculated insight, streaming insight or data graph.
Common confusion
Using a streaming insight for a 12-month lifetime value; streaming insights work over short windows of recent events.
5.2 Explain how to reference Data 360 data from other systems.Enrichments, APIs and Zero Copy sharing.
What it means
Data 360 data can be surfaced in Salesforce CRM through enrichments: related lists show Data 360 records such as engagement events on a Contact or Account, and copy fields write Data 360 values such as insights into CRM fields. External systems can use the Query API, Connect API, data graphs, Zero Copy sharing, or receive data through activations and data actions.
What to understand
Related list enrichments suit browsing many related Data 360 records without copying them. Copy field enrichments suit single values that users or automation need on the CRM record, such as a churn score. APIs suit custom applications that read profiles or run queries. Zero Copy sharing suits data warehouse teams who want to query unified data directly. Choose the option that avoids custom code when a standard feature fits.
How it shows up
Users or a system need Data 360 data in another place, and you choose enrichment, API or sharing method.
Common confusion
Building an Apex integration for a value that a copy field enrichment could place on the record.
5.3 View and build reports and dashboards using Data 360.Data 360 reports, Tableau Next and CRM Analytics.
What it means
Data 360 data can be analyzed with Salesforce reports and dashboards built on Data 360 objects, with Tableau Next and Tableau using semantic models over Data 360 data, and with CRM Analytics. Each serves a different audience, from operational users in Lightning to analysts building governed visual models.
What to understand
Data 360 reports use report types based on data model objects and calculated insights, so you can show segment populations, profile counts or insight values in familiar dashboards. Tableau Next builds on Data 360 and semantic definitions for richer analytics and agent-assisted analysis. Choose reports for quick operational views in Salesforce, and Tableau tools for deeper exploration or large cross-source analysis. Report on harmonized objects, not raw lake objects, so definitions stay consistent.
How it shows up
A stakeholder needs a view of Data 360 data, and you choose Data 360 reports, Tableau Next or another analytics tool.
Common confusion
Expecting standard CRM report types to include Data 360 objects automatically without Data 360 report types.
5.4 Apply predictive and generative AI tooling capabilities of Data 360 to customer scenarios.Einstein Studio, BYO models, search indexes and grounded prompts.
What it means
Einstein Studio lets teams build predictive models on Data 360 data without code or connect models hosted on platforms such as Amazon SageMaker, Google Vertex AI and Databricks. For generative AI, Data 360 indexes unstructured content into search indexes with vector or hybrid search, and retrievers pass relevant chunks to prompt templates and Agentforce.
What to understand
Predictive scenarios, such as churn propensity or next best product, use Einstein Studio models whose scores write back to Data 360 for segments, insights and flows. Generative scenarios use grounded prompt templates that pull profile fields, insights or retriever results so outputs reflect the customer's data. Unstructured documents such as manuals, transcripts and emails must be ingested and indexed before retrieval. Bring-your-own LLM options let customers use approved models while keeping the Einstein Trust Layer controls.
How it shows up
A customer's AI goal is described, and you choose the predictive model, search index, retriever or grounded prompt approach.
Common confusion
Using a calculated insight for a prediction; insights compute known values, while models estimate future outcomes.

High-yield concepts

The terms, rules and formulas this domain turns into questions most often.

  • Calculated insight

    A scheduled metric defined by measures and dimensions over Data 360 data.

    Remember
    Requires at least one measure and one dimension.
    Tested as
    Metric building questions.
    Trap
    Using it for real-time triggers.

  • Streaming insight

    A metric computed over short time windows on incoming engagement data.

    Remember
    Pairs with data actions for near real-time responses.
    Tested as
    Real-time scenarios.
    Trap
    Using it for long historical periods.

  • Data graph

    A precomputed bundle of a profile and its related records for fast access.

    Remember
    Supports real-time personalization and agent grounding.
    Tested as
    Low-latency lookup questions.
    Trap
    Thinking it replaces calculated insights.

  • Related list enrichment

    Shows Data 360 records as a related list on a CRM record page.

    Remember
    No data is copied into CRM fields.
    Tested as
    Surfacing many related records.
    Trap
    Using it for a single value.

  • Copy field enrichment

    Copies Data 360 values, such as insights, into CRM fields.

    Remember
    Lets CRM automation and reports use the value.
    Tested as
    Placing one score on a record.
    Trap
    Building custom code instead.

  • Query API and Connect API

    APIs that let applications query Data 360 objects and profiles.

    Remember
    Used by custom apps and integrations.
    Tested as
    External system access questions.
    Trap
    Choosing APIs when an enrichment fits.

  • Data 360 reports

    Salesforce reports and dashboards built on Data 360 report types.

    Remember
    Use harmonized objects and insights.
    Tested as
    Operational reporting questions.
    Trap
    Reporting on raw lake objects.

  • Tableau Next

    Salesforce's analytics experience built on Data 360 with semantic models and AI assistance.

    Remember
    Suits deeper analysis on harmonized data.
    Tested as
    Analytics tool selection.
    Trap
    Assuming it needs data exported from Data 360.

  • Einstein Studio

    Workspace for building no-code predictive models and connecting BYO models and LLMs.

    Remember
    Predictions write back to Data 360.
    Tested as
    Predictive AI scenarios.
    Trap
    Confusing it with Prompt Builder.

  • Search index

    Vector or hybrid index over unstructured content in Data 360.

    Remember
    Content must be ingested and chunked first.
    Tested as
    RAG and document search scenarios.
    Trap
    Using it for structured field filters.

  • Retriever

    Component that queries a search index and returns relevant chunks to a prompt or agent.

    Remember
    Connects search indexes to Prompt Builder and Agentforce.
    Tested as
    Grounding questions.
    Trap
    Expecting prompts to read indexes directly without one.

Side by side

Concepts candidates mix up, compared on the point that decides the answer.

Insight and enhancement options

ConceptMeaningKey differenceCommon trap
Calculated insight Batch metric over history Scheduled, complex logic Choosing it for minute-level triggers
Streaming insight Metric over recent time windows Near real time, simple logic Using it for lifetime value
Data graph Profile plus related data bundled Fast whole-profile lookup Treating it as a metric

Surfacing Data 360 data elsewhere

ConceptMeaningKey differenceCommon trap
Related list enrichment Data 360 records on a CRM page View many records, no copy Using it for one field
Copy field enrichment Data 360 values written to CRM fields Usable by CRM automation Expecting real-time sync for every field
Query or Connect API Programmatic access For custom apps Choosing code when a standard feature fits
Zero Copy sharing Warehouse queries Data 360 directly For data teams Exporting files instead

Predictive vs generative tools

ConceptMeaningKey differenceCommon trap
Einstein Studio model Predicts outcomes such as churn Numeric scores and classes Using a prompt template for scoring
Search index and retriever Finds relevant unstructured content Grounding with documents Using it for exact field values
Grounded prompt template Generates text using Data 360 context Personalized generated output Thinking it trains a model

Exam traps

Each trap is an answer that looks right. Learn the clue that should change your mind.

  1. Streaming for lifetime

    What candidates pick
    Build lifetime value as a streaming insight.
    Why it looks right
    Streaming sounds more current.
    Why it's wrong
    Streaming insights cover short windows of recent events; lifetime metrics need calculated insights over history.

    Clue to watch for The metric spans months or years.

  2. Apex for a score

    What candidates pick
    Write Apex to fetch a churn score onto the Contact page.
    Why it looks right
    Custom code gives full control.
    Why it's wrong
    A copy field enrichment places Data 360 values on CRM records without code.

    Clue to watch for The requirement is a single value on a CRM record.

  3. Insight as prediction

    What candidates pick
    Use a calculated insight to predict next month's purchases.
    Why it looks right
    Insights produce numbers.
    Why it's wrong
    Calculated insights summarize known data; predictions need an Einstein Studio or BYO model.

    Clue to watch for The goal involves estimating a future outcome.

  4. Prompt reads files

    What candidates pick
    Point a prompt template at uploaded PDF manuals directly.
    Why it looks right
    The documents are in Salesforce.
    Why it's wrong
    Content must be ingested as unstructured data, indexed and accessed through a retriever for grounding.

    Clue to watch for The scenario involves answering from documents.

  5. Raw lake reporting

    What candidates pick
    Build dashboards on data lake objects for consistent KPIs.
    Why it looks right
    Lake objects hold all the data.
    Why it's wrong
    Harmonized model objects and insights provide consistent definitions across sources; lake objects keep source quirks.

    Clue to watch for Several sources feed the same KPI.

  6. Export for analytics

    What candidates pick
    Export Data 360 data to files so Tableau can use it.
    Why it looks right
    File exports are familiar.
    Why it's wrong
    Tableau Next and Zero Copy sharing work on Data 360 data without file exports.

    Clue to watch for Analysts need governed access to unified data.

Answer framework

Use these steps for scenarios about insights, sharing, reporting or AI on Data 360 data.

  1. Decide whether the requirement is a known metric, access from another system, a visual report or an AI prediction or generation.
  2. For metrics, choose calculated insights for history, streaming insights for recent windows and data graphs for fast whole-profile access.
  3. For access, prefer related list or copy field enrichments in CRM, then APIs for custom apps and Zero Copy sharing for warehouses.
  4. For reporting, use Data 360 reports for operational views and Tableau Next for deeper analysis on harmonized data.
  5. For AI, use Einstein Studio or BYO models for predictions, and search indexes, retrievers and grounded prompts for generated answers.

Practice question

An insurer wants its Agentforce service agent to answer policyholder questions using thousands of policy PDF documents and to personalize answers with the caller's coverage details. What should the Data 360 consultant recommend?

Want more like this? The Data-Con-101 practice test has questions for every domain.

Checklist

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Study strategy

Divide this section into four blocks that match the objectives. For insights, complete the Data 360 Insights module and write two calculated insights in SQL, such as total spend per unified individual and count of orders in the last 90 days. Then describe one streaming insight with a time window and note which data action it could trigger.

For sharing, complete the Data 360 Enrichments module and practice deciding between related lists and copy fields. Add the APIs and Zero Copy sharing to a single table of options for reaching Data 360 data from elsewhere.

For reporting, build a report on a Data 360 report type in a playground if you can, and read the Tableau Next quick look. For AI, complete the machine learning predictions and AI model modules, then the unstructured data project. Trace a full path from document ingestion to search index, retriever and prompt template. In practice questions, first label the requirement as metric, access, report or AI, which narrows the options quickly.

FAQ

How much of the exam is Data Enhancements, Sharing, and Analysis?

Data Enhancements, Sharing, and Analysis is 18% of the exam, about eleven of the 60 scored questions as a planning estimate.

What is the minimum for a calculated insight?

At least one measure, such as a sum or count, and at least one dimension, such as the unified individual ID, defined over related Data 360 objects.

What is the difference between related list and copy field enrichments?

Related lists display Data 360 records on a CRM page without copying them. Copy fields write specific Data 360 values into CRM fields for use in automation and reports.

Is Tableau Next tested in depth?

The guide asks you to view and build reports and dashboards using Data 360. Know what Tableau Next offers and when to choose it over standard reports, not detailed authoring steps.

What does bring your own model mean?

Connecting a predictive model or LLM hosted on another platform, such as SageMaker, Vertex AI or Databricks, to Einstein Studio so it can use Data 360 data.

How does unstructured data reach Agentforce?

Documents are ingested as unstructured data, chunked and indexed in a search index. A retriever queries the index and passes relevant chunks to the prompt or agent.

Other CRM & Business Applications certifications

Where the same subject shows up in other exams. Cards marked "Same subject" open the matching domain.

Test your Domain 5 knowledge

You have covered insights, enrichments, reporting and Data 360 AI tooling. Check how accurately you choose the right tool for each requirement in timed practice.

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