Overview
Solution Positioning is the consulting conversation that happens before anyone opens Data 360 Setup. Its four objectives cover the core vocabulary and business value of Data 360, its role as the data foundation for predictive and generative AI, the use cases a customer should tackle first, and the principles of data ethics and governance.
Salesforce renamed Data Cloud to Data 360 in October 2025, and this exam followed: it was previously the Data Cloud Consultant certification and is now the Data 360 Consultant exam (Data-Con-101). Expect the terms data stream, data lake object, data model object, unified individual, calculated insight, segment, activation and data action, and the idea that Data 360 sits on the Salesforce platform so CRM apps, Agentforce and analytics read the same harmonized data. Positioning also includes Zero Copy access to warehouses such as Snowflake and Databricks, and the credit-based consumption model.
How it's tested
Questions read like discovery notes from a customer meeting. A retailer has customer data in commerce, service and loyalty systems and wants a single view, personalization or AI grounding. You choose the Data 360 capability, first use case or governance principle that fits the stated goal and constraints.
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Name the layer
A description of raw, mapped or unified data is given, and you identify whether it is a data stream, data lake object, data model object or unified profile.
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Pick the first use case
Several candidate projects are listed, and you choose the one with clear value, available data and a measurable outcome.
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Responsible data use
A request involves sensitive or unconsented data, and you choose the option that respects consent, minimization and purpose limits.
A bank wants service agents and an Agentforce agent to see a customer's recent card disputes, app logins and open cases in one place. The records live in three systems with different IDs. CRM reports cannot join them. Data 360 ingests each source, maps it to the Customer 360 Data Model, unifies the person through identity resolution and exposes that context to agents and prompts, so it is the fitting recommendation.
Objectives 4
Official objectives from the Salesforce outline, in outline order.
1.1 Describe Data 360's key terminology and business value.Core objects, the data flow and why customers buy Data 360.
- What it means
- Data 360 moves data through clear stages. Data streams bring source data in as data source objects and land it in data lake objects. Mapping places that data into data model objects from the Customer 360 Data Model. Identity resolution builds unified individuals, and calculated insights, segments, activations and data actions turn the result into value.
- What to understand
- Know each object and the order they appear: connector, data stream, data lake object, data model object, unified profile, insight, segment, activation. The business value is a trusted, real-time view of each customer that every Salesforce app, Agentforce and analytics tool can use without copying data between silos. Usage is billed through consumption credits, so design choices such as refresh frequency and query volume affect cost.
- How it shows up
- A description of a data stage or a business outcome is given, and you match it to the correct Data 360 term or capability.
- Common confusion
- Treating data lake objects and data model objects as the same thing; lake objects hold ingested data, model objects are the harmonized semantic layer.
1.2 Articulate Data 360's foundational role in generative and predictive AI.Why AI needs unified, governed customer data.
- What it means
- Predictive models and large language models are only as useful as the data behind them. Data 360 supplies harmonized structured data, unstructured content indexed for vector search, and data graphs that package a customer's related records, so Agentforce, Prompt Builder and Einstein Studio models work from current, trusted context.
- What to understand
- Generative AI uses Data 360 for grounding: retrievers query search indexes over unstructured content, and prompt templates can pull unified profile fields and insights. Predictive AI uses Einstein Studio to build models on Data 360 data or connect external models through bring-your-own-model options. In both cases the value comes from one governed copy of customer data rather than separate extracts for each AI project.
- How it shows up
- A customer wants AI answers or predictions based on its own records, and you identify how Data 360 supplies the data.
- Common confusion
- Assuming the language model itself stores customer data; Data 360 provides context at request time through grounding.
1.3 Identify initial use cases for Data 360.Choosing a first project that proves value quickly.
- What it means
- A good first use case has a clear owner, data that is accessible, a measurable result and a short path to activation. Common starting points are a unified customer profile for service agents, a marketing segment built from combined commerce and engagement data, or grounding an Agentforce agent with customer context.
- What to understand
- Consultants should scope one or two outcomes, list the data sources each needs and check that those sources can be connected with standard connectors or Zero Copy. Avoid starting with every system in the company. Map the use case to a value metric, such as reduced handle time or better campaign conversion, so the customer can judge success and plan the next phase.
- How it shows up
- A customer lists several ambitions, and you pick the initial use case with the best mix of value and feasibility.
- Common confusion
- Choosing the most ambitious enterprise-wide project first instead of a scoped, measurable one.
1.4 Describe the principles of data ethics and governance.Consent, minimization, transparency and access control.
- What it means
- Responsible use of customer data means collecting only what a purpose requires, honoring consent and preferences, being transparent about how data is used, protecting sensitive attributes and limiting who can see what. Salesforce frames this through its ethical data use and trusted AI principles.
- What to understand
- In Data 360 these principles show up as consent and contact point data mapped into the data model, data spaces and permissions that restrict access, data classification and policies on sensitive fields, and processes for data subject requests such as deletion. Segments and AI features should not use attributes like health status or inferred ethnicity to target or exclude people unless there is a lawful, consented and fair reason.
- How it shows up
- A marketing or AI request touches sensitive or unconsented data, and you choose the response that follows ethical principles.
- Common confusion
- Believing that data legally collected can be used for any purpose; purpose limitation and consent still apply.
High-yield concepts
The terms, rules and formulas this domain turns into questions most often.
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Data 360 (formerly Data Cloud)
Salesforce's data platform for ingesting, harmonizing, unifying and acting on customer data.
- Remember
- Renamed from Data Cloud in October 2025; earlier names include Salesforce CDP and Genie.
- Tested as
- Current guide and question wording use Data 360.
- Trap
- Thinking Data 360 and Data Cloud are separate products.
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Data stream
A configured feed that brings data from a source connector into Data 360.
- Remember
- Each stream has a category such as Profile or Engagement and a refresh mode.
- Tested as
- Terminology and setup questions.
- Trap
- Calling the stream itself the stored data object.
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Data lake object (DLO)
Storage object holding ingested or transformed data before harmonization.
- Remember
- DLOs keep source structure; mapping happens after.
- Tested as
- Questions that ask where raw data lives.
- Trap
- Segmenting directly on DLOs.
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Data model object (DMO)
Harmonized object based on the Customer 360 Data Model, such as Individual or Sales Order.
- Remember
- Segments, insights and activations run on DMOs.
- Tested as
- Most downstream scenarios reference DMOs.
- Trap
- Confusing DMOs with standard CRM objects.
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Unified individual
The profile created when identity resolution links source records for one person.
- Remember
- It is produced by a ruleset, not loaded from a source.
- Tested as
- Business value and AI grounding questions.
- Trap
- Expecting it to exist before identity resolution runs.
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Consumption credits
Data 360 usage is metered by credits for actions such as ingestion, unification, queries and activation.
- Remember
- Frequent refreshes and large queries consume more credits.
- Tested as
- Positioning and cost-aware design questions.
- Trap
- Ignoring cost when choosing streaming over batch.
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Grounding
Supplying an AI prompt with relevant, current data from Data 360 at request time.
- Remember
- Grounding reduces made-up answers and keeps data inside the trust boundary.
- Tested as
- Generative AI positioning questions.
- Trap
- Thinking fine-tuning the model is required instead.
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Einstein Studio
The Data 360 workspace for building predictive models and connecting external or bring-your-own models.
- Remember
- Models use Data 360 data without exporting it.
- Tested as
- Predictive AI positioning.
- Trap
- Assuming external models need data copied out.
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Zero Copy
Access to data in external warehouses or lakes without physically moving it, and sharing Data 360 data outward the same way.
- Remember
- It reduces duplication and pipeline maintenance.
- Tested as
- Value questions about existing data warehouses.
- Trap
- Assuming all data must be ingested first.
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Purpose limitation
Customer data should be used only for purposes the person agreed to or would reasonably expect.
- Remember
- Consent and stated purpose limit segmentation and AI use.
- Tested as
- Ethics and governance scenarios.
- Trap
- Using data for a new purpose because it is already stored.
Side by side
Concepts candidates mix up, compared on the point that decides the answer.
Data 360 object layers
| Concept | Meaning | Key difference | Common trap |
|---|---|---|---|
| Data source object | Data as read from the source by the stream | Temporary staging view of the source | Treating it as long-term storage |
| Data lake object | Persisted ingested or transformed data | Keeps the source shape | Building segments on it |
| Data model object | Harmonized data mapped to the standard model | Shared semantic layer for apps | Expecting raw source field names |
| Unified individual | Merged profile from identity resolution | Created by rules, not ingestion | Mapping a source directly into it |
How Data 360 supports AI
| Concept | Meaning | Key difference | Common trap |
|---|---|---|---|
| Grounding for generative AI | Adds Data 360 context to prompts and agents | Used at request time | Thinking the model is retrained |
| Vector search on unstructured data | Indexes documents and transcripts as chunks and embeddings | Finds semantically similar content | Using it for exact field lookups |
| Einstein Studio predictive models | Scores records such as churn or propensity | Outputs predictions to Data 360 | Confusing it with prompt templates |
Exam traps
Each trap is an answer that looks right. Learn the clue that should change your mind.
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Report solves it
- What candidates pick
- Recommend a CRM report when data sits in several external systems with different IDs.
- Why it looks right
- Reports are familiar and cheap.
- Why it's wrong
- Reports cannot join and resolve identities across external sources; Data 360 harmonizes and unifies them first.
Clue to watch for The scenario names multiple systems and inconsistent identifiers.
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Retrain the model
- What candidates pick
- Fine-tune a language model on customer records so an agent can answer account questions.
- Why it looks right
- Training sounds like how AI learns facts.
- Why it's wrong
- Grounding with current Data 360 data at request time keeps answers fresh and data governed without retraining.
Clue to watch for The requirement is up-to-date answers about specific customers.
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Biggest project first
- What candidates pick
- Start with a company-wide golden record across every system.
- Why it looks right
- It promises the most value.
- Why it's wrong
- Large first phases delay results; a scoped use case with available data proves value and builds momentum.
Clue to watch for The customer is new to Data 360 and wants quick results.
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Stored means usable
- What candidates pick
- Use inferred health interests in an advertising segment because the data is already in Data 360.
- Why it looks right
- The data is available and might improve targeting.
- Why it's wrong
- Purpose limitation, consent and fairness principles restrict sensitive attributes regardless of storage.
Clue to watch for The attribute is sensitive or the person did not consent to that use.
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Copy everything in
- What candidates pick
- Ingest an entire Snowflake warehouse before any use case is defined.
- Why it looks right
- Having all data seems safer.
- Why it's wrong
- Zero Copy federation can query warehouse data in place, and ingesting unused data adds cost and governance risk.
Clue to watch for The data already lives in a supported warehouse.
Answer framework
Use these steps when a scenario asks you to position Data 360 or choose a first project.
- Restate the customer's business goal in one line, such as faster service, better targeting or grounded AI answers.
- List the data sources the goal needs and note whether they connect natively, through Zero Copy or need custom ingestion through the Ingestion API or MuleSoft.
- Confirm that unification and harmonization are actually required, since a single-source problem may not need Data 360, and a report in the source system may be enough.
- Choose the smallest use case that delivers a measurable result and name the metric the customer will track.
- Check consent, sensitive attributes and access needs before recommending how the data will be used or activated, especially for marketing channels.
Practice question
Want more like this? The Data-Con-101 practice test has questions for every domain.
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Study strategy
Start with the vocabulary, because every other section assumes it. Draw the Data 360 flow on one page: connector, data stream, data lake object, mapping, data model object, identity resolution, unified individual, insights, segments, activations and data actions. Add one line under each box that says what it stores or produces. Then complete the Solution Positioning modules in the official prep trail, including the credit consumption and real-time use case quick looks.
Next, study the AI objective through examples. Write out how a service agent, a prompt template and a churn model would each use Data 360 data, and note whether the data arrives through grounding, a retriever or a model output.
For use cases, practice turning a vague goal into a scoped first phase with named sources and a metric. For ethics, review Salesforce's ethical data use guidance and practice spotting sensitive attributes and missing consent in scenarios. Because older blogs still say Data Cloud, read them with the rename in mind and check terms against current Salesforce Help.
FAQ
How much of the exam is Solution Positioning?
Solution Positioning is 14% of the Data 360 Consultant exam, about eight of the 60 scored questions as a planning estimate.
Is Data 360 the same product as Data Cloud?
Yes. Salesforce renamed Data Cloud to Data 360 in October 2025. The certification was renamed from Data Cloud Consultant to Data 360 Consultant, and the current guide uses Data 360 terminology.
Do I need deep AI knowledge for this section?
No. You need to explain how Data 360 feeds AI: grounding prompts and agents, indexing unstructured content for search, and supplying data to Einstein Studio models.
What makes a good first Data 360 use case?
One with a clear business owner, accessible data sources, a measurable outcome and a quick path to activation, such as a unified service view or a cross-channel marketing segment.
Are pricing details tested?
Specific prices are not part of the guide. You should understand that usage consumes credits and that refresh frequency, data volume and query patterns affect consumption.
How are data ethics tested?
Through scenarios where a team wants to use sensitive, unconsented or repurposed data. Choose the answer that respects consent, minimization, purpose and fair treatment.
Is the exam code still the same after the rename?
Third-party and Trailhead listings use Data-Con-101. The official guide text does not print the code, so confirm it when you register.
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Test your Domain 1 knowledge
You have covered Data 360 terminology, its role in AI, first use cases and data ethics. Check how well you position the platform in timed scenario questions.
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