How to Pass Salesforce Data Cloud Consultant Certification Exam

Last Updated on July 6, 2024 by Rakesh Gupta

As a newly minted Salesforce Certified Data Cloud Consultant, I am sharing my study experiences with you and want you to be the next one to ace it! So, get ready and dive in!

As you are here, you may want to check out the following articles:

So, Who is an Ideal Candidate for the Exam?

The Salesforce Certified Data Cloud Consultant Credential is tailored for consultants experienced in implementing and consulting on enterprise data platforms in customer-facing roles, involving design, configuration, and architecture of solutions. This exam guide is essential for those preparing for the Data Cloud Consultant Exam.

Ideal candidates for the exam typically have at least two years of experience in data strategy and data modeling, having developed multiple solutions across various Salesforce clouds. They usually come from backgrounds in development, strategy, business analysis, presales solutioning, or architecture.

Candidates should have broad knowledge of Salesforce technology, specifically Data Cloud and its capabilities. They should also have practical experience in positioning and implementing Data Cloud solutions, demonstrating their competence and contributing to customers’ long-term success.

How to Prepare for the exam?

Learning styles differ widely – so there is no magic formula that one can follow to clear an exam. The best practice is to study for a few hours daily – rain or shine! Below are some details about the exam and study materials:

60 multiple-choice/multiple-select questions – 105 mins
62% is the passing score
Exam Sections and Weighting
- Solution Overview: 18%
- Data Cloud Setup and Administration: 12%
- Data Ingestion and Modeling: 20%
- Identity Resolution: 14%
- Segmentation and Insights: 18%
- Act on Data: 18%
The exam Fee is $200 plus applicable taxes
Retake fee: $100
Schedule your certification exam here

The following list is not exhaustive; so check it out and use it as a starting point:

Salesforce Certified Data Cloud Consultant Exam Guide
Trailmix: Unlock Your Data with Data Cloud
Module: Cert Prep: Salesforce Certified Data Cloud Consultant
Trail:
1. Explore Data Cloud
2. Build a Data Strategy for Data Cloud
Data Cloud PDFs
Data Cloud Decoded: YouTube Videos
Instructor Led training by Trailhead Academy
1. Discover Salesforce Data Cloud Fundamentals (SDC101)

What you Need to Know to Smoothen your Journey

On a very high level, you have to understand the following topics to clear the exam. There is no shortcut to success. Read and practice as much as you can. All credit goes to the Salesforce Trailhead team and their respective owners.

Solution Overview

Salesforce Data Cloud, previously known as Salesforce Customer Data Platform (CDP), is a powerful platform designed to help organizations unify and manage customer data from various sources.
Data Cloud is about more than just bringing data together. It’s about bringing entire organizations together around the customer to improve experiences and drive growth.
1. Unify Your Enterprise Data – Data Cloud eliminates data silos, creating a single platform to access and leverage all your Enterprise Data. Seamlessly integrate structured and unstructured data (PDF’s, emails, call transcripts) into Salesforce with our library of connectors and leveraging zero copy integrations from Snowflake, Redshift, BigQuery, and Databricks.
2. Harness the Power of Metadata – Data Cloud is built on Salesforce’s foundational metadata layer, which provides a common language that integrates all Salesforce applications and low-code platform services including Einstein AI, Flow for automation, Lightning for UI, and Apex for deep, pro-code customization.
3. Drive AI Results from Your Data – Data Cloud unlocks the power of generative AI, grounded with your company’s data, delivering trusted, secure, and relevant outcomes without expensive model training. Plus, seamlessly integrate external predictive models with Bring Your Own Model for enhanced workflows and insights.

Data Cloud enables any team to create valuable experiences.

Sales	Every sales rep can receive real-time guidance during customer video and voice calls to adapt to the conversation and deliver personalized offers to their customers.
Service	Every service rep from the contact center to the field can provide proactive service with real-time alerts that detect challenges, enable agents to intervene, engage the customer, and resolve the issue.
Marketing	Every marketer can deliver personalized messages across channels that adapt to customer activity across various brand properties in real-time.
Commerce	Every retailer can build tailored shopper experiences that adapt to real-time customer actions, including abandoned shopping carts or actions taken on a website or mobile app.
Platform	IT teams can use low-code tools to build things like apps that leverage real-time data for example to provide fraud detection or real-time economic data to determine benefits.
MuleSoft	Every business can unlock real-time data across any modern or legacy system.
Tableau	Every business can monitor KPIs in real-time to inform action across the business, including real-time purchase data for sales, real-time case spikes for service, and real-time web traffic for marketing.
Slack	Leaders can immediately increase efficiency by enabling teams to automatically view real-time data from any channel with intelligent workflows
Healthcare & Life Sciences	Payer and provider organizations can connect clinical and non-clinical data from a variety of sources to deliver real-time intelligent insights, which can be used to build automated journeys to help patients achieve better outcomes.
Financial Services	Financial advisors and bankers can help their clients accelerate their financial goals by providing the right advice at the right time.
AppExchange	Extend the power of Data Cloud with the AppExchange Data Cloud Collection, featuring 18 Data Cloud partner apps and experts that help companies automate relevant advertising, and enrich customer profiles.

How does data cloud really work?
1. Connect all your data sources, whether batch or streaming real-time data.
2. Prepare your data through transformation and data governance features.
3. Harmonize your data to a standard data model.
4. Unify data with identity resolution rulesets.
5. Query and analyze data using insights.
6. Use AI to predict behavior.
7. Analyze, expand, and act on your data in any channel.
8. Segment audiences and create personalized experiences.
9. Output data to multiple sources to act on data based on your business needs.
10. Continue to review, measure, and optimize data.
Data Cloud allows you to create a data space to organize data to fit your business needs. You can segregate your data, metadata, and processes by categories, such as brand, region, or department. Once segregated, users can be granted access to a data space.
When you think about enterprise data management requirements, you want a solution that delivers on the five Vs: velocity, variety, veracity, volume, and value.
1. Velocity: Data moves at different speeds and different velocities. The ability to take in data that imports via batch, while also gathering data in real time as it occurs, is vital to successful marketing, people, and advertising interactions.
2. Variety: Different types of data—data schema, data formats, IDs inside of sales, service, commerce, and marketing systems—must be mapped into a common information model. Their availability to exist in a single system is an important personalization ingredient.
3. Veracity: Veracity is truth by another name. Implementing an SSOT ensures that shopper data is reconciled and transformed across multiple systems. That means you use one version of shopper data that’s been extracted from many different contacts and touch points, resulting in a rich profile that retains cross-device identity.
4. Volume: Humans create data at an incredible scale, and you want to collect lots of it. Every piece of data adds to your understanding of the shopper, but that means you need an enterprise data management system (EDMS) that handles its massive scale.
5. Value: After you’ve achieved the first four Vs, what value do you receive from the collected data? Where do you send it, how do you activate it, and how do you analyze and segment it for optimal use? The answers to these questions tell you how your data creates immediate value.
Knowledge Check: Data Cloud Core Capabilities

Data Cloud Setup and Administration

Knowledge Check: Set Up and Administer Data Cloud

Standard permission set options in Data Cloud

Permission Set	Description
Data Cloud Admin	Users with this permission set can access all functionality within Data Cloud, including mapping data to the data model and creating data streams, identity resolution rulesets, and calculated insights.
Data Cloud User	Users with this permission set can view Data Cloud features.

Data Cloud implementation steps as admin

Step	What You Do	Additional Info
1	Update your admin user.	An email is sent to admins with account login information. Once you log in for the first time you need to reset your password and add the admin permission set to your user.
2	Provision Data Cloud.	Complete the setup of your account in the Data Cloud Setup page.
3	Create profiles, users, and add permission sets.	Profiles and permission sets help you manage the access users have in Data Cloud.
4	Connect to Marketing Cloud Engagement.	Connect with a Marketing Cloud Engagement account using admin credentials.
5	Select appropriate data bundles and business units in Marketing Cloud Engagement.	Data bundles are pre-modeled standardized data sets. For Marketing Cloud Engagement these are based on email and mobile channels.
6	Connect to Sales or Service Cloud.	Connect your Data Cloud with the account provisioned or with additional Sales and Service Cloud accounts.
7	Prepare for ongoing tasks and maintenance.	Set yourself up for success by establishing auditing and troubleshooting procedures.

Data Cloud uses some terms and acronyms that are helpful to know. Here’s a rundown.
1. Data Stream: A data source ingested into Data Cloud.
2. Data Lake Object (DLO): A storage container for the data ingested into data streams.
3. Data Model Object (DMO): A Data Cloud object created from data streams, insights, and other sources.
4. Customer 360 Data Model: Data Cloud’s standard canonical data model. Data ingested into Data Cloud is mapped to DMOs found in the Customer 360 Data Model.
5. Starter Data Bundles: A Salesforce-defined data stream that includes mapping to the Data Cloud DMO structure.
Watch: Data Cloud for Admins

Data Ingestion and Modeling

Watch: Data Ingestion and Mapping
Watch: Demo: Ingestion
Watch: Demo: Mapped Data Model Object Relationships
The Customer 360 Data Model includes different data model objects (DMOs), used to describe types of information in the data model. The information used to describe those data model objects are attributes (contained in fields in the Data Cloud app).
1. The Customer 360 Data Model is the overall system that governs a set of common data model objects and determines how to describe those DMOs and how they relate to each other.
2. A subject area contains parts of the Customer 360 Data Model, which gather large chunks of data and bring them together in a general framework using data model objects. This information helps you achieve your business goals. For most marketers, that goal is to market or promote your product or service to your customers. Data model subject areas might include a grouping of unique identifiers called Party, or could be engagement data, sales orders, or product information.
3. A data model object (DMO) is a grouping of data in the Customer 360 Data Model that describes an instance of a thing or an action.
4. An attribute is a standardized piece of information about a DMO, often contained in a field as shown in Data Cloud.
5. A primary key is a value in a data set that uniquely identifies a single record. Only one instance of that value can exist in a given data set.
6. A foreign key is a common link found between data sources that builds data relationships, such as a customer ID number.
Data Cloud employs the Customer 360 Data Model to ensure standard data interoperability across cloud applications. The following subject areas, each comprising several objects, are represented in the model.
1. Party data model provides information about trading relationships, such as customer and supplier information.
2. Product data model provides information on a product available for sale or service.
3. Sales Order data model provides information on future revenue or quantity for an opportunity, including product family, territory, and other information.
4. Engagement data model provides information on interactions with a specific party, such as an email message or telephone call.
5. Case data model provides information on any recorded issue, such as a mobile phone repair problem.
6. Journey data model provides information on the complete set of experiences for a party when they interact with your company.
7. Software Application data model provides information, programs, or apps designed for an end user.
Two-phased approach to bringing in data.
1. Data ingestion: Bring in all fields from a data set exactly as they are without modification. That way, you can always revert back to the original shape of the data should you make a mistake or change business requirements during setup. You can also extend the data set by creating additional formula fields for the purpose of cleaning nomenclature or performing row-based calculations. Each data set is going to be represented by a data stream in Data Cloud.
2. Data modeling: Map the data streams to the data model in order to create a harmonized view across sources.
A streaming data transform lets you clean and enrich your data in near real-time, as it enters the system. To modify select amounts of data on a scheduled time interval, use a batch transform.
1. A streaming data transform reads records from a source data lake object (DLO) and runs a SQL query that modifies incoming data. It then maps the target data lake object to the Data Cloud data model in a Data Model Object (DMO).
In Data Cloud, data comes in through a data stream and resides in a data lake object (DLO). A DLO is the storage container for data ingested into Data Cloud. A data transform lets you access data in one or more DLOs and transform it to create your own set of data.
In contrast to a streaming data transform, which runs continually, a batch data transform runs on a scheduled basis. Batch data transforms offer more functionality than streaming data transforms, which are based on a SQL statement. Batch data transforms offer a rich visual editor. You use this editor to combine data from multiple DLOs, use functions to create calculated fields, and you can output data to multiple DLOs.

When you create a batch data transform, you can use the different node types to extract the exact data you need. Here are the different node types you can choose and what they do.

Node type	What it does
Aggregate	Rolls up data to a higher granularity using these functions: Average, Count, Maximum, Minimum, Stddevp, Stddev, Sum, Unique, Varp, and Var.
Append	Combines rows from multiple sets of data.
Filter	Removes rows that you don’t need in your target data.
Input	Contains source data in a DLO.
Join	Joins two input nodes via a lookup or join. Each input node must have a key field. For example the customer data input node and the ticket sales node each have a customer ID field.
Output	Contains the transformed data in a DLO.
Transform	Manipulates data by use of functions. With this node, you can calculate values, modify string values, format dates, edit data attributes, drop columns, and so on.
Update	Swaps column values with data from another data source when key pairs match.

Knowledge Check: Connect and Model Your Data

Identity Resolution

Identity Resolution and Unified Profiles
Data Cloud uses some terms that are helpful to know during identity resolution. Time to review.
1. Source profile: These are the records from your source data streams that Data Cloud reviews during the identity resolution process to identify matching profiles.
2. Unified profile: These are profiles that have been processed by identity resolution. Each unified profile is created from one or more source profiles.
3. Unified contact objects: Individuals and businesses may have more than one valid address, phone number, or email address. Unified contact objects store information related to unified profiles. One unified profile record can be related to many unified contact points.
4. Unified link objects: These objects provide a bridge between your source data and your unified profile data, so that you can always track each piece of a unified profile back to where it came from.
5. Ruleset: This is a set of match and reconciliation rules that tell identity resolution how to match and reconcile your source profiles.
6. Match rules: Match rules tell identity resolution what data must match for two or more source profiles to be consolidated into a single unified profile. Your ruleset can contain multiple match rules. Each match rule is an opportunity for source records to be matched differently. For example, you might create one match rule that tells identity resolution that all profiles with the same name and email address can be matched, then create a second match rule that tells identity resolution that all source profiles with the same name and phone number can be matched. That way, you need only an email address or a phone number to verify that records with matching names are really the same individual or account.
7. Match criteria: Each match rule must contain at least two match criteria. All criteria in a match rule must be met to match records. A criterion consists of a field and a match method. For example, one criteria could be that last names must match exactly. Another might allow for first names to be matched using a fuzzy match method.
8. Match method: The match method is how data is processed and reviewed during matching. When the match method is exact normalized, data must match exactly after normalization. With exact normalized matching, the name Robert and its common nickname Bob aren’t matches. When the match method is fuzzy, the name Robert and its common nickname Bob are considered matches.
9. Reconciliation rule: For certain fields, such as first name, identity resolution must select which data from multiple match sourced records is stored in the output unified profile. Data can be reconciled based on frequency of a value, recency, or based on the object it came from.
A unified profile is composed of data from multiple sources linked together using identity resolution match and reconciliation rules. If the same data exists in multiple places, profiles are linked together based on established rules.

Implementation steps to get you from raw data to a unified profile

Step	Description
Ingest raw data from data sources.	Data is added from bundles, data extensions, Amazon Simple Storage Service (S3), and other systems as is. After raw data is added into the Data Cloud as a data stream, the data needs to be mapped to the data model.
Map and model data.	Customer 360 Data Model is the behind-the-scenes tool that allows data from multiple sources to be standardized into a readable format that can be easily mapped. Data from your data stream needs to be mapped to objects, like Party Identification and Individual, in order for identity resolution rulesets to work.
Create identity resolution rulesets.	After modeling and mapping steps are complete, create identity resolution rulesets. Match and reconciliation rules are added to help look for and unify profiles across your various data streams.
Create unified profiles.	After rulesets are set up, the system creates unified profiles that can be used for segmentation and in activations.

Rulesets allow you to configure match rules and reconciliation rules about a specific object, such as individual. The system follows these rules to link together multiple sources of data into a unified profile.
A reconciliation rule helps the unified profile know which one to display. Let’s review the options for reconciliation rules.
Knowledge Check: Resolve Identity

Segmentation and Insights

Data Cloud uses some terms that are helpful to know during insights.
1. Measure: An aggregated value of attributes, for example a customer’s total amount spent or an average order amount.
2. Dimensions: A qualitative value used to categorize a measure. For example, if you want to see every customer’s total amount spent, the customer ID could be a dimension associated with the measure of the total amount spent.
3. Insight Builder: A tool that allows users to create insights with limited knowledge of SQL.
Data Cloud uses some terms that are helpful to know during segmentation.
1. Segment: Filter your data to create useful segments to understand, target, and analyze your customers.
2. Segment on: Within segmentation, segment on defines the target object used to build your segment.
3. Publish: Publish is the process of searching and building a segment based on the filter criteria. You can publish your segments on a chosen schedule or as needed.
4. Activation: Activation is the process of moving audience segments to an activation target.
5. Direct attributes: Attributes that have a one-to-one relationship with the segment target. Meaning each segmented entity has only one data point for a profile attribute. So for customer data, they would only have one entry for postal code or for first name.
6. Related attributes: Attributes that can have multiple data points.
Segmenting is the process of filtering your data to create meaningful groups, helping you better understand, target, and analyze your customers.
Publish is the process of building a segment based on the filter criteria and sending results to a destination platform. You can publish your segments on a chosen schedule or as needed.
Activation is the process of moving audience segments to an activation target. For example, during activation an audience segment is created in a shared data extension that can be used in Marketing Cloud Engagement Journey Builder.
A subject area is a business concept that helps connect data points based on a standard model. Common data model examples are Party (your customers) and Sales Orders (what they purchased).
An object in the data model created by ingested data streams and insights. Data Model Object (DMO) can be standard or custom, based on your business needs. A DMO is similar to a Marketing Cloud Engagement data extension (which is a standard database table) in that it’s an object that stores data such as leads, product info, customer info, and so on.
An attribute is a unique characteristic of a data model object—for example, a customer’s first name. This is similar to a data extension field in Marketing Cloud Engagement.

Types of Insights

Calculated insights are used to query and create complex calculations based on stored data.

Streaming insights are queries based on real-time data.

	Calculated Insights	Streaming Insights
How is data processed and collected?	Data is processed together as a unit in high-volume batches.	Data is processed from streaming data sources such as from the Web or Mobile SDKs.
What types of data are used?	Calculated insights can be created from any data.	Streaming insights can only be created from engagement data, not streaming profile data.
How can you use these insights?	Calculated insights can be used to define segment criteria and personalization attributes for activation using metrics, dimensions, and filters.	Streaming insights help build time series aggregation in near real time that can be used to drive orchestration or data actions.
How can these insights be shared?	Calculated insights are packageable and can be shared with other instances of Data Cloud.	Streaming insights can be mapped to different objects from Web and Mobile SDK and Marketing Cloud Engagement data streams.

There are four ways to build new insights in Data Cloud.

Create with Builder
Create from a Package
Create with SQL

Create Streaming Insights

Method	Description
Create with Builder	If you’re not familiar with SQL, you can build both calculated and streaming insights using the builder tool in Data Cloud. This tool allows you to drag and drop elements to build your SQL statements.
Create from a Package	If you or a colleague has created and tested a calculated insight in another org, you can create an insight from an installed Salesforce package.
Create with SQL	Write SQL expressions to create your metrics and dimensions from mapped objects and fields.
Create Streaming Insights	Using a similar interface to calculated insights, you can write SQL expressions to compute streaming metrics across dimensions from your real-time data sources.

Knowledge Check: Segment and Gain Insight

Act on Data
1. Activation and Segmentation Strategies
2. Data Actions Explained
3. Bring Your Own Lake (BYOL) Data Sharing lets you share selected data objects in Data Cloud with third-party data ecosystems such as external data warehouses and data lakes.
4. BYOL data sharing allows access to live and accurate data at scale by using a zero-ETL (extract transform load) approach.
5. Here are some of the Data Cloud objects you can share via data shares.
  1. Data lake object (DLO): Data ingested into the Data Cloud gets stored in a DLO. The data stored in a DLO is cleansed, transformed, and prepped for computation and analysis.
  2. Data model object (DMO): A DMO is a grouping of data (made up of attributes) created from data streams, insights, and DLOs. Data is harmonized from different sources into a uniform data model. Data Cloud supports standard and custom DMOs.
  3. Calculated insight object (CIO): Calculated insights help build cube-style metrics with measures and dimensions on Data Cloud data. A CIO is a DMO created after a calculated insight is processed.
6. Knowledge Check: Act on Data

Conclusion

If you have basic experience with all the mentioned topics, passing the exam should be straightforward, allowing you to earn the highly sought-after Salesforce Certified Data Cloud Consultant certification. However, if you lack sufficient experience (at least 3-5 months) with Salesforce Data Cloud and aim to become a Certified Consultant, I recommend creating a 3-4 week study plan, completing the relevant Trailhead modules to prepare.

I hope these tips and resources prove helpful. With dedication and effort, you will succeed. Happy studying and good luck!