Data Cloud Consultant
The Salesforce Certified Data Cloud Consultant credential is one of the more technically demanding consultant exams on the platform. It goes well beyond knowing what Data Cloud does -- you need to understand the full pipeline from raw ingestion through semantic mapping, identity resolution, segmentation, and activation. If you have been working hands-on with Data Cloud for a few months, you are in good shape to start studying. If you are coming from a Marketing Cloud or CRM Analytics background, expect to spend extra time on the data model and identity resolution topics, which are unlike anything else in the Salesforce ecosystem.
3-Step Path to Passing
- 1
Complete the Unlock Your Data with Data Cloud Trailmix
The official Trailmix covers every exam topic from setup through activation. Work through it in order -- the modules build on each other, and skipping ahead on identity resolution or segmentation without understanding the data model first will hurt you on the exam. - 2
Attempt Practice Exams
I recommend my own practice exams, but I have linked other options in the resources below. Data Cloud questions are scenario-heavy and test judgment calls between real alternatives -- things like batch vs. streaming segments, or activations vs. data actions. Practice under time pressure. - 3Schedule with Kryterion when you are consistently scoring above 75% on practice exams. This exam has a high density of scenario questions, so do not rush it. Give yourself at least a week after finishing your study materials.
Core Resources
Exam Overview
Questions
65
60 scored + 5 unscored
Duration
105 min
1 hour 45 minutes
To Pass
65%
Minimum Score
Question Format
Scored
92%60questions
Unscored
8%5questions
Exam Details
Pricing
$200 registration · $100 retake
Delivery
Online proctored or at a testing center
Experience
Prerequisites
No prerequisites required
Exam Topics
Each topic section shows the topic weight, learning objectives, and links to study resources.
Data Cloud Overview18%
Data Cloud platform architecture and terminology, core data flow stages, typical use cases across Sales, Service, and Marketing, dependencies on connected orgs, and data ethics principles including consent, suppression, and right-to-be-forgotten.
- Describe Data Cloud's function, key terminology, and business value.
- Identify typical use cases for Data Cloud.
- Articulate how Data Cloud works and its dependencies.
- Describe and apply the principles of data ethics.
Resources
Data Cloud Setup and Administration12%
Permission sets specific to Data Cloud, data spaces for multi-brand or multi-region isolation, data bundles and kits for packaged configuration, Data Explorer and Profile Explorer for diagnostics, reports and dashboards for monitoring, and Salesforce Flow integration.
- Apply Data Cloud permissions, permission sets, and org-wide settings.
- Describe and configure the available data stream types and data bundles.
- Identify use cases for data spaces and create data spaces based on requirements.
- Manage and administer Data Cloud using reports, dashboards, flows, packaging, and data kits.
- Diagnose and explore data using Data Explorer, Profile Explorer, and APIs.
Resources
Data Ingestion and Modeling20%
Connector types and refresh modes, Data Streams vs. Data Lake Objects vs. Data Model Objects, streaming and batch data transforms, formula fields in DMOs, mapping source fields to canonical model entities (Individual, Contact Point Email, Engagement), and tools for inspecting and validating ingested data.
- Identify the different transformation capabilities within Data Cloud.
- Describe processes and considerations for data ingestion from different sources into Data Cloud.
- Define, map, and model data using best practices and aligning to requirements for identity resolution.
- Use available tools to inspect and validate ingested and modeled data.
Resources
Identity Resolution14%
Fuzzy vs. exact matching rules, reconciliation rule types and priority order (last updated, most frequent, source priority), Unified Individual and Unified Contact Point output objects, multi-source merge scenarios, and validating unified profile output.
- Describe matching and how its rule sets are applied.
- Reconcile data and describe how its rule sets are applied.
- Describe the results of identity resolution and use cases.
Resources
Segmentation and Insights18%
Segment creation and filtering, batch vs. streaming segments, related attributes and nested segments, segment exclusions, calculated insights using SAQL-like expressions, streaming insights for near-real-time event processing, data graphs for query optimization, and SQL-based insights.
- Define basic concepts of segmentation and use cases.
- Identify scenarios for analyzing segment membership.
- Configure, refine, and maintain segments within Data Cloud.
- Identify and differentiate between calculated and streaming insights.
Resources
Act on Data18%
Activation targets and publishing to external destinations (Marketing Cloud, advertising platforms, CRM), data actions via Salesforce Flow and webhooks, timing dependencies and refresh cadence, troubleshooting accepted vs. rejected activation records, CRM enrichments, and Data Cloud-powered Agentforce.
- Define activations and their basic use cases.
- Use attributes and related attributes.
- Identify and analyze timing dependencies affecting the Data Cloud lifecycle.
- Troubleshoot common problems with activations including accepted/rejected counts, errors, and blank field values.
- Configure and troubleshoot data actions.
- Describe and configure activation targets and associated use cases.
Resources
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