Study guide
1.3 Platform Terminology, Core Entities & Data Lifecycle Stages
Salesforce Certified Data 360 Consultant formerly Data Cloud Consultant
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1 Chapter 1: Solution Overview & Enterprise Value
1.1 Enterprise Customer 360 & Data Cloud Business Value 1.2 Architectural Foundation: Zero-Copy, Hyperforce, Lakehouse & Data Pipeline 1.3 Platform Terminology, Core Entities & Data Lifecycle Stages 1.4 Data 360's Foundational Role in Predictive & Generative AI
2 Chapter 2: Ethics, Privacy & Governance Fundamentals
2.1 Regulatory Compliance, Data Privacy & Ethics in Customer Data 2.2 Consent Management & Preference Enforcement in Data Cloud 2.3 Data Retention, Deletion, and Privacy Right-to-be-Forgotten (RTBF)
3 Chapter 3: Security, Permissions & Tenant Architecture
3.1 Standard Permission Sets, User Roles & Functional Access 3.2 Data Spaces Architecture, Resource Allocation & Tenant Isolation
4 Chapter 4: Administration, Monitoring & Lifecycle Management
4.1 Data Kits & Package Automation for Cross-Org Deployment 4.2 Auditing, Status Monitoring, Error Logs & Data Explorer 4.3 Sandboxes, Change Management & Metadata Deployment
5 Chapter 5: Ingestion Connectors & Source Integration
5.1 Standard Bundles & Salesforce CRM Connectors (Sales/Service/Marketing) 5.2 Cloud Storage (S3, GCS, Azure Blob) & Streaming Ingestion (Web/Mobile SDK, Ingestion API) 5.3 Zero-Copy Data Federation & Snowflake/BigQuery/Databricks Sharing
6 Chapter 6: Data Lake Objects (DLO) & Data Transformations
6.1 DLO Configuration, Primary Keys, Event Time & Data Stream Categories (Profile, Engagement, Other) 6.2 Streaming & Batch Data Transforms: Formula Fields, SQL Transforms & Data Prep Recipes
7 Chapter 7: Data Model Objects (DMO) & Canonical Harmonization
7.1 Customer 360 Canonical Data Model & Subject Areas 7.2 DLO-to-DMO Mapping Patterns, Cardinality & Custom DMO Design 7.3 Data Validation, Schema Maintenance & Field Mapping Best Practices
8 Chapter 8: Identity Resolution Architecture & Match Rules
8.1 Identity Resolution Concepts, Consolidation Rate & Graph Modeling 8.2 Match Rules & Match Criteria: Exact Match vs. Fuzzy Matching (Deterministic & Probabilistic) 8.3 Multi-Party Identification, Anonymous-to-Known Transitions & Contact Point Matching
9 Chapter 9: Reconciliation Rules & Unified Profile Governance
9.1 Reconciliation Rules: Most Frequent, Source Priority & Last Updated 9.2 Unified Profile Schema (Individual & Contact Points) & Resolution Troubleshooting
10 Chapter 10: Segment Architecture & Audience Design
10.1 Segmentation Canvas, Segment Types (Standard UI & Flow) & Query Processing 10.2 Direct vs. Related Attributes, Cardinality & 1:N Filtering Traps 10.3 Segment Schedule, Refresh Frequencies & Exclusion Logic 10.4 Segment Membership Analysis, Population Drift & Sizing Validation
11 Chapter 11: Calculated & Streaming Insights
11.1 Calculated Insights (CI) Architecture, SQL/Builder Syntax & Dimensions vs. Metrics 11.2 Streaming Insights (SI) Architecture, Windowing (Tumbling/Sliding) & Real-Time Signals 11.3 CI vs. SI Architectural Decision Matrix & Performance Optimization
12 Chapter 12: Data Enhancements, Sharing & Analysis
12.1 Referencing Data 360 Data from Other Systems: Enrichments, Related Lists, Data Graphs & APIs 12.2 Reports, Dashboards & Tableau Analysis on Data 360 Data 12.3 Applying Predictive & Generative AI Tooling to Customer Scenarios
13 Chapter 13: Activation Targets & Outbound Activation
13.1 Activation Targets: Marketing Cloud, External Platforms, Cloud Storage & Data Spaces 13.2 Activation Configuration: Audience Mapping, Related Attributes & Payload Optimization 13.3 Activation Schedules, Publishing Cadence & Incremental vs. Full Refresh 13.4 Activation Troubleshooting: Error Logs, Rejection Reasons & Delivery Failures
14 Chapter 14: Real-Time Data Actions & Flow Automation
14.1 Data Action Targets & Webhook/Platform Event Architecture 14.2 Data Cloud-Triggered Flows & Real-Time Business Orchestration 14.3 End-to-End Enterprise Scenario Walkthrough: Ingestion to Closed-Loop Action
1.3 Platform Terminology, Core Entities & Data Lifecycle Stages
Key Takeaways Explain This Topic AI
Salesforce Data Cloud is now Salesforce Data 360, and the certification was renamed Salesforce Certified Data 360 Consultant while keeping the Data-Con-101 exam code.
A Data Stream represents the configured ingestion pipeline from an external connection, creating a Data Source Object (DSO) metadata contract and populating a raw Data Lake Object (DLO).
Data Lake Objects (DLOs) maintain source schema fidelity and cannot be directly segmented or activated; they must be harmonized and mapped to canonical Customer 360 Data Model Objects (DMOs).
DLOs and DMOs are classified into three strict categories: Profile (individual entity records), Engagement (immutable time-series events with mandatory Event Time), and Other (reference and dimensional data).
Identity Resolution consumes mapped Profile DMOs to create Unified Profile DMOs and Identity Link tables, establishing the trusted foundation for Calculated Insights and audience segmentation.
Last updated: September 2026
1.3 Platform Terminology, Core Entities & Data Lifecycle Stages
Quick Answer: Mastery of Data Cloud requires understanding the strict entity lifecycle: external data enters through a Data Stream , which defines a Data Source Object (DSO) metadata contract and writes raw data into a Data Lake Object (DLO) in the source schema. To make data usable, the DLO is harmonized and mapped to standard or custom Data Model Objects (DMOs) in the canonical Customer 360 model. Identity Resolution processes Profile DMOs into a Unified Profile . Calculated Insights (CI) aggregate metrics across DMOs, and audiences are built on the Segmentation Canvas and dispatched via Activation Targets or real-time Data Actions .
Naming: Data Cloud Is Now Data 360
Before any of the entity vocabulary makes sense, settle the product name. Salesforce rebranded Data Cloud to Salesforce Data 360 , and the rename reached the certification itself: the credential is now the Salesforce Certified Data 360 Consultant , renamed from Salesforce Certified Data Cloud Consultant, while the exam short code stayed Data-Con-101 . Official Salesforce Help articles now carry titles such as Data 360 Standard Permission Sets and Data 360 License, Credits, and Permission Set Changes , and Trailhead badges read Customer 360 Data Model for Data 360 .
What this means for you in practice:
Nothing about the platform architecture changed with the rename. DLOs, DMOs, identity resolution, segments, and activations behave exactly as before.
Both names appear in the wild. Setup labels, permission set API names (for example sfdc_c360a_sfdctrust_permSet ), and many object names still carry "Data Cloud" or the older c360a / a360 prefixes. Salesforce renamed the product faster than it renamed every internal identifier, so "Data Cloud" inside an API name is not evidence that something is out of date.
The official exam outline is written in Data 360 language. Its six sections are Solution Positioning (14%), Data 360 Setup and Administration (13%), Data Source Connection and Ingestion (18%), Harmonization and Unification (17%), Data Enhancements, Sharing, and Analysis (18%), and Data Activations and Utilization (20%). Prep material still organised around the retired "Data Cloud Overview / Identity Resolution / Segmentation and Insights / Act on Data" outline is describing a superseded blueprint.
Exam logistics as published: 60 multiple-choice questions plus up to five unscored questions, 105 minutes, a 70% passing score , US$200 registration and US$100 retake, no prerequisite, and no reference materials permitted.
This guide uses "Data 360" when discussing the product and the credential, and keeps "Data Cloud" where it is the literal label of an object, permission set, or menu item you will see on screen.
Essential Platform Vocabulary & Core Entity Definitions
To pass the certification exam, you must distinguish between similarly named platform entities and understand their exact operational responsibilities: +-----------------------------------------------------------------------------------------+
- | DATA CLOUD CORE ENTITY ARCHITECTURE |
- +-----------------------------------------------------------------------------------------+
- [ External Connection ] (Salesforce CRM / S3 / Web SDK / Ingestion API / Zero-Copy)
- │
- ▼
- [ Data Stream ] ─────────► Defines Ingestion Schedule, Source Connection & Entity Category
- │
- ├──────────────► [ Data Source Object (DSO) ]: Metadata schema contract
- ▼
- [ Data Lake Object (DLO) ] ────► Physical storage container in Lakehouse (Source Schema)
- │
- │ (Harmonization / Field-Level Mapping)
- ▼
- [ Data Model Object (DMO) ] ───► Canonical Customer 360 Object (Standard or Custom)
- │
- ├──────────────────────────────┬──────────────────────────────┐
- ▼ ▼ ▼
- [ Identity Resolution ] [ Calculated Insights ] [ Segmentation Canvas ]
- (Match & Reconciliation Rules) (Multi-Dimensional SQL) (Filters DMOs & Insights)
- │ │ │
- ▼ │ ▼
- [ Unified Profile DMOs ] ────────────────┘ [ Activation Target ]
- (Unified Individual & Links) (Marketing Cloud / S3)
- 1. Data Stream
Definition: The configured pipeline and data ingestion schedule linking a specific external data source object to Data Cloud.
Configuration: When creating a Data Stream, the administrator specifies the connection source, object name, sync frequency (for batch), primary key, and Data Stream Category .
Crucial Rule: The Category assigned to a Data Stream ( Profile , Engagement , or Other ) is immutable . Once created, it cannot be changed. If configured incorrectly, the stream must be deleted and recreated.
2. Data Source Object (DSO)
Definition: The metadata definition and schema description of the incoming external data payload.
Role: It acts as the contract defining the fields, data types, and properties expected from the source connector.
3. Data Lake Object (DLO)
Definition: The physical data container in Data Cloud's lakehouse storage where ingested data actually lands.
Characteristics: The DLO strictly preserves the raw source schema , including source field names, casing, and structures (e.g., cust_first_nm , zip_cd_5 ).
Critical Constraint: You CANNOT build audience segments or configure identity resolution directly on a Data Lake Object. DLOs are purely staging storage layers.
4. Data Model Object (DMO)
Definition: A standardized business object belonging to the Customer 360 Data Model (or a custom schema extension).
Role: DMOs represent the harmonized semantic layer. For example, the Individual DMO represents a human being, while Contact Point Email represents an email address.
Capabilities: Only DMOs (and Calculated Insights built on DMOs) can be queried on the Segmentation Canvas , evaluated by Identity Resolution , or surfaced on Service Cloud Lightning Pages .
5. Entity Categories: Profile, Engagement, and Other
Every DLO and DMO in Data Cloud must belong to one of three specific categories:
Category Type Description Mandatory Requirements Common Examples
Profile Represents entities with identity attributes (people, business accounts, contacts). Records describe who someone or something is. Must have a defined Primary Key uniquely identifying the entity record. Individual , Account , Contact Point Email , Contact Point Phone
Engagement Represents timestamped behavioral interactions. Records describe what an entity did at a specific moment. Engagement data is append-only. Must have a defined Primary Key AND a mandatory Event Time DateTime field. Sales Order , Device Application Event , Email Engagement Event , Web Click
Other Represents reference, dimensional, or catalog data that is neither a person profile nor a behavioral interaction. Must have a defined Primary Key. Product , Store Location , Postal Code Reference , Campaign Details
6. Unified Profile & Link Tables
Unified Profile: The consolidated "golden record" generated by the Identity Resolution engine after evaluating Match and Reconciliation rules across Profile DMOs. Represented by objects like Unified Individual and Unified Contact Point Email .
Key Resolution Link Tables: System-generated bridge tables (such as Individual Identity Link ) that map every source-specific record ID (e.g., Lead ID, Contact ID, SAP Customer Number, Web Cookie UUID) back to the single Unified Individual Id .
7. Calculated Insights (CI) vs. Streaming Insights (SI)
Calculated Insight (CI): Multidimensional SQL aggregations computed over historical batch data in DMOs. Outputs metrics grouped by dimensions (e.g., Total_Spend_LTV grouped by Unified_Individual_Id ). CIs can be directly used as segmentation criteria.
Streaming Insight (SI): Real-time metric calculations evaluated over sliding or tumbling time windows (e.g., counting failed login attempts within a 5-minute rolling window). SIs trigger real-time Data Actions but are not directly queried on the batch segmentation canvas.
8. Activation Targets, Activations & Data Actions
Activation Target: The authenticated destination platform configured to receive audiences or data (e.g., Marketing Cloud, Amazon S3, Google Ads, Meta Ads).
Activation: The specific configuration that defines an audience segment, selects which member attributes (e.g., Email, First Name, LTV) to include in the payload, and establishes a publishing schedule.
Data Action & Data Action Target: An event-driven mechanism that pushes streaming alerts out of Data Cloud to a webhook, Salesforce Platform Event, or Flow when an insight condition is satisfied.
Step-by-Step Enterprise Lifecycle Walkthrough: Aura Retail Example
To solidify how these entities interact in a real enterprise implementation, trace the end-to-end journey of customer data for Aura Retail , a global omnichannel brand: +-----------------------------------------------------------------------------------------+
- | AURA RETAIL END-TO-END DATA LIFECYCLE |
- +-----------------------------------------------------------------------------------------+
[ STEP 1: INGESTION ]
- Source A: POS retail store transactions uploaded hourly to Amazon S3 as CSV.
- Source B: E-Commerce web clicks streamed live via the Salesforce Web SDK.
- Generates DLOs:
- POS_Orders_DLO (Engagement; PK: Order_UUID; Event Time: Purchase_Timestamp)
- POS_Customers_DLO (Profile; PK: Loyalty_ID)
- Web_Clickstream_DLO (Engagement; PK: Click_UUID; Event Time: Click_Timestamp)
- │
- ▼
- [ STEP 2: HARMONIZATION / DMO MAPPING ]
- POS_Customers_DLO fields mapped to canonical C360 DMOs:
- Mapped to Individual DMO (First Name, Last Name, Birthdate)
- Mapped to Contact Point Email DMO (Email Address, Individual Id FK)
- POS_Orders_DLO mapped to:
- Sales Order DMO (Order Date, Total Amount, Grand Total Amount)
- Sales Order Product DMO (Product SKU, Quantity, Unit Price)
- Web_Clickstream_DLO mapped to:
• Device Application Event DMO (Page URL, Device Type, User Agent)
- │
- ▼
- [ STEP 3: IDENTITY RESOLUTION / UNIFICATION ]
- Match Rules configured:
• Rule 1: Exact Match on Normalized Email Address.
• Rule 2: Exact Match on Party Identifier (Loyalty Number).
- Reconciliation Rules configured:
• Mailing Address: Source Priority (Service Cloud CRM > In-Store POS > Web Form).
• Phone Number: Most Frequently Occurring across sources.
- Output: Unified Individual DMO and Individual Identity Link table linking the
anonymous web cookie to the known retail loyalty customer.
- │
- ▼
- [ STEP 4: CALCULATION & SEGMENTATION ]
- Calculated Insight Customer_LTV_CI calculates 12-month net spend per Unified Individual.
- Marketer creates Audience Segment on Segmentation Canvas:
- Criteria: Unified Individual where Customer_LTV_CI.Lifetime_Spend > $1,000
- AND Sales Order.Order_Date < LAST_N_DAYS:60
AND Device Application Event.Page_URL contains '/vip-sale'.
- │
- ▼
- [ STEP 5: ACTIVATION & CLOSED-LOOP ACTION ]
- Segment activated to Marketing Cloud Activation Target with personalized attributes.
- A Streaming Insight simultaneously detects a real-time high-ticket cart abandonment,
firing a Data Action to Service Cloud to create a high-priority outreach task.
- +-----------------------------------------------------------------------------------------+
- Architectural Dependencies & Structural Prerequisites
The Data Cloud Consultant certification heavily tests architectural prerequisites. Memorize these fundamental platform constraints:
Rule 1: The DLO Segmentation Prohibition
You cannot build segments or write activation queries directly against a Data Lake Object (DLO). If an exam question describes a consultant who has ingested data into a DLO and is unable to find the fields in the Segmentation Canvas, the missing step is always: The DLO has not been mapped to a Data Model Object (DMO) .
Rule 2: Identity Resolution Prerequisites
Identity Resolution rulesets cannot run directly on raw DLOs or unharmonized data. To configure Identity Resolution:
The source DLO must be assigned the Profile category.
The DLO must be mapped to the standard Individual DMO (or Account DMO for B2B).
Contact points (email, phone, address) must be mapped to their corresponding Contact Point DMOs ( Contact Point Email , Contact Point Phone , Contact Point Address ).
The Individual Id foreign key on the Contact Point DMO must be populated with the primary identifier of the Individual DMO. If this foreign key relationship is broken, identity resolution cannot link contact points to individual profiles.
Rule 3: Category Immutability and Event Time Obligations
When creating a Data Stream, selecting Profile , Engagement , or Other is permanent. If you accidentally categorize an Engagement stream as Profile, you cannot change the dropdown setting later; you must delete the Data Stream and recreate it.
Any Data Stream or DLO categorized as Engagement strictly requires a designated Event Time DateTime field. Ingestion will fail or throw validation errors if an Engagement object does not have an assigned timestamp indicating when the interaction occurred.
Rule 4: Primary Key Integrity
Every DLO and DMO requires a designated Primary Key. If the source external system has composite keys (e.g., an order record uniquely identified by Store_Number + Register_ID + Transaction_Number ), the consultant must configure a formula field during ingestion to concatenate these values into a single unique primary key string before mapping to the DMO.
Entity Taxonomy & Comparison Matrix
Entity Name Layer / Location Schema Origin Direct Segmentation? Direct Identity Resolution? Primary Architectural Purpose
Data Source Object (DSO) Ingestion (Metadata) External Source No No Metadata contract describing incoming source fields and types
Data Lake Object (DLO) Storage (Lakehouse) External Source No No Raw physical storage preserving source schema and field naming
Data Model Object (DMO) Harmonization (C360) Canonical / Standard Yes Yes (Profile DMOs) Standardized business entity used for querying, insights, and modeling
Unified Profile DMO Unification (Identity) System Standard Yes N/A (Result of IR) Authoritative golden customer record resolving duplicate identities
Calculated Insight (CI) Calculation (Batch SQL) User SQL Definition Yes (as Segment Criteria) No Pre-computed multi-dimensional metrics (LTV, RFM, Churn Score)
Streaming Insight (SI) Calculation (Streaming) User SQL Definition No (Data Action Only) No Real-time threshold detection across sliding/tumbling event windows
Activation Target Outbound Infrastructure External Destination No No Authenticated endpoint destination for publishing audience payloads
Exam Traps & Common Consultant Pitfalls
Exam Trap: Inventing Custom DMOs When Standard DMOs Exist. Salesforce Data Cloud includes dozens of out-of-the-box Customer 360 DMOs (e.g., Individual , Contact Point Email , Sales Order , Device Application Event ). The exam heavily penalizes creating custom DMOs when standard DMOs can satisfy the business requirement. Standard DMOs are required to leverage out-of-the-box Identity Resolution rules, standard Calculated Insights, and native activation targets.
Exam Trap: Confusing DLO with DMO in Scenario Questions. Exam questions frequently describe an administrator looking for data in the Segment Canvas or Identity Resolution ruleset. Distractors will mention "Verify the DLO has refreshed" or "Map the DLO to the Activation Target." Remember: DLOs are raw storage; DMOs are the harmonized semantic layer required for segmentation, identity resolution, and activation.
Exam Trap: Ingestion Category Mismatches. If a client needs to track customer product return events or customer loyalty point redemption transactions, categorizing the stream as Profile is fatal. Returns and redemptions are time-series events that belong in the Engagement category with an assigned Event Time field. Categorizing them as Profile overwrites previous transactions rather than appending an interaction history.