Month: August 2024

Table of ContentsNo header found About the Exam DescriptionAnswerNumber of Questions60Time Limit105 MinutesAverage Minutes per Question1.75 Minutes (105 minutes / 60 questions)Passing Score62livery Options- Online- Onsite- At DreamforceCost$200PrerequisitesNoneData Cloud Consultant Certification information Certification Introduction If you have been to any kind of Salesforce event in the last year, you will have seen a couple of things plastered on every wall. One of them being Salesforce Data Cloud. Salesforce Data Cloud empowers businesses to unify, govern, and activate their data across the entire customer journey. Data Cloud is a new sort after skill amongst companies in the Salesforce space. Anybody who possesses the Data Cloud certification will be well in demand from recruiters looking for knowledgable professionals. The Salesforce Data Cloud Consultant certification will test and validate your expertise in designing, implementing, and managing data solutions on the Salesforce Data Cloud. Below we will talk about a high level over view of topics and content you should be revising and aware of for the exam. Let this blog post serve as your guide to passing the Salesforce Data Cloud Certification. Topics & Weightings Find below an outline of the Data Cloud Consultant certification Data Cloud Overview (18%) Grasp the core functionalities of the Data Cloud. Understand how it unifies data from various sources, establishes data governance frameworks, and empowers businesses to activate their data for actionable insights. Master key terms like data lineage, data quality, data lakes, and data activation to navigate technical discussions and configure the platform effectively. Articulate how Data Cloud fosters data-driven decision-making, streamlines workflows, and drives business growth. Explore real-world scenarios that involve consolidating customer data from disparate systems for a 360-degree customer view, ensuring regulatory compliance through robust data governance practices, or activating customer data for targeted marketing campaigns. Map the complete lifecycle of data within the Data Cloud environment. This includes data ingestion, transformation, cleansing, and activation for analysis and business use. Comprehend the ethical principles governing data management within the Data Cloud platform. This includes principles like data privacy, transparency, and accountability. Data Cloud Setup & Administration (12%) Manage Data Cloud permissions and permission sets. Understand how to grant granular access to users and ensure data security. Configure organization-wide settings within Data Cloud. This could involve setting data retention policies, defining data access restrictions, or customizing user interfaces for optimal efficiency. Grasp the different data stream types available in Data Cloud, such as streaming data or batch data. Learn to configure these streams to efficiently ingest data from various sources. Understand when to utilize data bundles for pre-packaged industry-specific datasets and how to configure them for seamless data integration. Identify the ideal use cases for creating data spaces in Data Cloud. This could involve establishing secure data environments for specific departments or collaborating with external partners while maintaining data governance. Monitor data quality, track data lineage, and identify potential issues with Data Cloud reports. Explore how Data Cloud flows can be leveraged to automate data management tasks, streamlining your workflows and enhancing efficiency. Concepts of packaging and data kits within Data Cloud. Learn how to package your data configurations for reuse or distribution within your organization. Functionalities of Data Explorer to diagnose data quality issues, investigate data lineage, and troubleshoot data management challenges. Data Cloud APIs and their role in advanced data management tasks. Explore how APIs can be utilized for custom integrations and data manipulation. Data Ingestion & Modeling (20%) Data transformation capabilities offered by Data Cloud. This could involve data cleansing, filtering, aggregation, or data type conversion to ensure your data is optimized for analysis and business use. Processes involved in ingesting data from various sources into Data Cloud. This may encompass real-time data streams, batch data files, or data extracted from external applications. Considerations for successful data ingestion. This could involve defining data formats, establishing data quality checks, and ensuring data security throughout the process. Core principles of data modeling within Data Cloud. Learn how to define data entities, map data attributes across different datasets, and establish relationships between them to create a unified data model. The concept of identity resolution and its importance within data governance. Explore best practices for identifying and linking records that represent the same entity across disparate data sources, ensuring data accuracy and consistency. Tools within Data Cloud to inspect and validate ingested and modeled data. This may involve data profiling tools to identify inconsistencies, data lineage exploration to track data origin and transformations, and data quality checks to ensure data accuracy and completeness. Identity Resolution (14%) The concept of data matching within Data Cloud. Understand how matching rules are applied to identify records representing the same real-world entity across different datasets, even when they have variations in field values or formatting. Understand how data reconciliation rules are applied to identify and resolve inconsistencies between records identified as duplicates or potential matches through the matching process. Positive outcomes of successful identity resolution within Data Cloud. This can encompass a 360-degree customer view for improved customer service, enhanced data quality for analytics, and ultimately, more reliable insights for data-driven decision-making. Practical use cases for identity resolution across various business scenarios. This could involve consolidating customer data from multiple sources for a unified marketing campaign, reconciling financial records for accurate reporting, or streamlining lead management by eliminating duplicate entries. Segmentation & Insights (18%) Classify customers into distinct groups based on shared characteristics or behaviors, enabling targeted marketing campaigns and personalized customer experiences. Identify practical scenarios where customer segmentation within the Data Cloud can be highly beneficial. This could involve segmenting customers by purchase history for targeted promotions, grouping leads by interests for personalized nurturing campaigns, or tailoring website content based on user demographics. Explore different scenarios for analyzing segment membership within Data Cloud. Learn how to identify trends within segments, measure the effectiveness of segmentation strategies, and refine your segments for optimal targeting and personalization. Configure segments within Data Cloud. Learn how to define segment criteria using various data points, establish segment filters, and leverage Data Cloud tools to build dynamic segments that adapt to changing customer behavior. Explore techniques for monitoring segment performance, identifying potential data drift, and making adjustments to ensure your segments remain accurate and effective over time. Identify the key differences between calculated insights and streaming insights within Data Cloud. Calculated insights provide a historical view of data while streaming insights offer real-time visibility into customer behavior and trends. Act on Data (18%) Understand how activations bridge the gap between data and downstream systems, allowing you to trigger actions based on specific data events or conditions. Explore real-world use cases for data activations. This could involve sending real-time alerts to sales reps upon lead creation, automatically syncing customer data with marketing automation platforms for personalized campaigns, or triggering loyalty program updates based on customer purchase history. Learn how to leverage attributes and related attributes to define activation criteria and personalize downstream actions. For example, you could trigger an email campaign based on a customer’s purchase history (attribute) and personalize the email content with the customer’s name (related attribute) pulled from another data source. Identify and analyze potential timing dependencies that can affect the Data Cloud lifecycle, specifically impacting activations. This could involve understanding how data ingestion schedules, data processing times, and activation triggers interact and influence the overall flow of data within the platform. Explore common problems that can arise with Data Cloud activations, such as discrepancies in accepted/rejected activation counts, encountering errors during activation, or missing related attributes in downstream systems. Explore practical use cases for data actions within activations. This could involve automatically creating new customer accounts in your CRM system upon lead qualification, updating product inventory levels in your ERP based on real-time sales data, or triggering a personalized welcome email campaign upon customer onboarding. Important Topics for the Exam Use the below information as recommendations for what you should be revising and have a strong knowledge of before entering the exam. Take this as a high level "cheat sheet" of information. Its important to note - Data Cloud used to be called Customer Data Platform (CDP). A customer data platform (CDP) is a place where a company collects and stores data about its customers. With Customer Data Platform you can: Create unified customer profiles across all touch points by connecting identities, engagement data,customer orders, loyalty, and marketing journeys. Build smarter audience segments using insights and filtering capabilities. Activate data from anywhere across your organization. Capture and unify data from anywhere with a high-scale data ingestion service. Analye your data using tools like Tableau or Marketing Cloud Intelligence. Customer 360 Data Model is Customer Data Platform's standard data model Data Cloud features cover functionality for enterprise customer data in five key categories: Connect - Data Cloud offers multiple methods that allow users to connect their data: Connectors APIs MuleSoft Harmonise - Map data between your connected data sources and the Customer 360 Data Model. Unify - Link all the different data records that refer to the same real-world entity or object, such as a person. Analyse and Predict - Aggregate and analyse data across multiple channels and touch points,businesses optimize marketing strategies, improve customer experiences, and drive businessperformance. Act - Create smart segments and activate them anywhere. Data Cloud Capabilities Ingestion - Consume or activate data to any cloud and any application Identity - Connect, match and resolve customer data Segmentation and Activation - Create smart segments and activate anywhere Insights - Embed your data with intelligence and make it available to analytics systems 5 stages of a Data Cloud implementation Plan Architect Construct Validate Deploy Data Cloud Implementation preparation steps Identify project stakeholders. Identify data sources and integrations. Understand system requirements. Identify Customer Data Platform users. Document all business requirements. Data Cloud Permission Sets 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, andcalculated insights. Data Cloud User - Users with this permission set can view Data Cloud features. Segmentation and Activation Add-On License Permission Sets Data Cloud for Marketing Admin - Users with this permission set can manage day-to-dayconfiguration needs, support, maintenance, and improvement and perform regular internal systemaudits. Data Cloud for Marketing Data Aware Specialist - Users with this permission set can map data to thedata model and create data streams, identity resolution rulesets, and calculated insights. Data Cloud for Marketing Manager - Users with this permission set can manage an overallsegmentation strategy, including creating activation targets and activations. Data Cloud for Marketing Specialist - Users with this permission set can create segments. From feedback we get from our students - there typically aren't too many questions on permission set/permissions Starter data bundles are pre-modeled standardized data sets and are available for Marketing Cloud EmailStudio, MobileConnect, and MobilePush. Sharing rules can be used on Salesforce Data Cloud to control access Sharing rules can be applied to the following Data Cloud objects: Data Stream Calculated Insights Segment Activation Target Activation Data Cloud setup process steps order Configure Admin user Provision Data Cloud Create profiles and configure additional users Connect to relevant Salesforce Clouds Salesforce Data Cloud home page Data Streams and Data Model provide insight into your selected data model and connected datasources that are created by the data aware specialist user. Calculated Insights are predefined and calculated metrics that can help you build segments. Identity Resolutions is where your team creates match and reconciliation rules to unify individualrecords. Data Explorer and Profile Explorer are data-viewing tools, allowing you to view your ingested andunified profile data, respectively. Segments tab is where you create your filtered audience segments. Activation Targets and Activations are used to manage where segments get exported, for exampleMarketing Cloud. Data Ingestion & Modeling (20%) Marketing Cloud Connect user requirements Must have only two roles assigned: Administrator and Marketing Cloud Administrator. Must have the Enterprise business unit (top level) assigned as default. Must have access assigned to any other child business unit from which data needs to be ingested. MC Connect connection is established at the account level (Enterprise Business Unit) Marketing Cloud supports one-to-one (1:1) and one-to-many (1:M) connections with Data Cloud. Connections from multiple Marketing Cloud accounts to a single Data Cloud instance (M:1) are notsupported. CRM orgs connectable by the Salesforce CRM connector Home Org - Org where Data Cloud is installed External Org - Orgs that are external to the org where Data Cloud is installed Sandbox Org - Sandbox CRM orgs that are external to the org where Data Cloud is installed Data Cloud and Loyalty Management must be installed within the same org to support Activation toLoyalty Management. Salesforce CRM supports one:one (1:1), one:many (1:M), and many:one (M:1) connections with Data Cloud. You can connect a total of 5 CRM orgs to Customer Data Platform. Prerequisites for the B2C Commerce Cloud Connector configuration The B2C Commerce instance should be implemented and owned by a customer. Commerce Einstein must be activated in order for data to flow from B2C Commerce to Data Cloud. The user configuring the connection needs to have access to B2C Commerce Business Manager. B2C Commerce supports one:one (1:1), one:many (1:M), and many:one (M:1) connections with Data Cloud. Non-production B2C Commerce instances (dev, testing, sandbox) are not currently supported. Marketing Cloud Personalization Connector prerequisites The user configuring the connection needs to have Admin permission in Marketing CloudPersonalization. Data Cloud Gear has to be enabled in the Marketing Cloud Personalization datasets) that are to beconnected. All user attributes should be defined in your Marketing Cloud Personalization dataset. Marketing Cloud Personalization supports one:one (1:1) and many:one (M:1) dataset connections with Data Cloud. Steps to configure the data stream Select data source Select source object (dataset) Define data stream properties Confirm data source object schema Apply necessary row-level transforms Configure updates to data source object Apply necessary row level transforms Configure updates to data source objects The data stream with the Engagement Category has a limit of 2000 unique dates at the date granularityper single ingestion. The data stream with the Engagement Category has a limit of 2000 unique dates at the date granularityper single ingestion. If the Refresh Mode is set to Full Refresh, Marketing Cloud only exports the data set on a daily (24 hours)cadence. Marketing Cloud starter data bundles - Provides access to the messaging and engagement data, includingcampaign and journey details, and message template metadata. Email Studio - Includes engagement events, including opens, clicks, bounces, complaints,unsubscribes, Einstein Engagement Scores, campaigns, journeys, and enterprise profile attributes MobilePush - Includes engagement events, such as sends, opens, displays, undelivers, geofenceentries and exits, Einstein Engagement Scores, campaigns, journeys, push message templatemetadata, and contact point (app). MobileConnect - Includes engagement events such as sends, delivers, undelivers, opt-ins, and opt-outs, campaigns, journeys, SMS message template metadata, and the contact point (phone). The Full Refresh option can only be used for a data set with 50 million records or less. For larger groups,use one of the delta extract options. There are two options for Refresh Mode: Upsert and Full Refresh. Last 90 days of the data will be exported and ingested into Data Cloud for the initial historical load. The initial extraction of historical data from Marketing Cloud may take up to 24 hours. If the wrong Data Extract option is selected during initial setup, it can't be changed post creation. The starter data bundle cannot be ingested multiple times. Access to the objects and fields, must be explicitly granted via the Salesforce Data Cloud SalesforceConnector Integration permission set. Failing to do this results in an Insufficient Permissions errormessage. During data stream configuration, Field Name and Field API Name of the CRM object field can be updatedat the schema review step. Customer 360 Data Model Components Subject Area (A Business Goal) Data Model Object, also called DMOs (Groups of Data) Attributes (Data About Your Contacts) Customer Data Platform, the system can only unify profiles if they are mapped correctly to the individualobject and one other element: a contact point object or a party identifier object. Individual Object - All the personal information you know about your customer. Contact Point Objects - Contact points (things like email, phone, address, device, and social) all haveassociated objects that can be used for identity resolution. Party Identification Object - Party identifier matching allows you to use your own customer-suppliedidentifiers. Identity Resolution Rulesets - Allow you to configure match rules and reconciliation rules about a specificobject, such as individual. Two types of rules that make up a ruleset: Match Rules - Used to link together multiple records in a unified customer profile. Reconciliation Rules - Determine the logic for data selection Match Rules match methods Exact Fuzzy Normalized While setting up new data stream Field Label and Field API Name can be edited Once an attribute path has been selected in a container, it can't be changed. Data Cloud segments can be used in Journey Builder. You need to go to Activations to activate a segment in Data Cloud Activities possible on the Data Explorer: Preview data Inspect multiple object types Validate formula fields The Customer 360 Data Model is organized into subject areas that represent a major business activity,such as customer information, product, or engagement data. Subject areas consist of data model objects (DMO). A DMO is a grouping of data created from data streams, insights, and other data sources. The DMOincludes attributes (also called fields). An attribute is a specific piece of data stored in Customer Data Platform (Data Cloud), for example, a customer's firstname. Data mapping requirements When you're mapping a data model object (DMO) in the Profile or Other category, you map the primarykey field. You can save the data mapping only after mapping the primary key. When you're mapping a DMO in the Engagement category, you map the primary key field and the EventdateTime field. You can save the mapping only after mapping both fields. To use identity resolution, segmentation, and activation, map the required fields and relationships forthe party area data. You must also map the Individual object and either a Contact Point or the PartyIdentification object must be mapped in data streams. The values for the individual.ld field must be unique across all data sources that map to the IndividualDMO Contact Point and Customer Profile must be mapped to the Individual DMO to enable the unification andactivation process to work. In a data model the Individual Object referenced in other DMOs via Party Attribute Customer 360 Data Model Subject Areas Case Data Model - Groups and defines recorded issues, such as laptop connectivity problems orsupport tickets. Engagement Data Model - Defines interactions with a party. Example interactions could be speakingto a customer over the phone or receiving a customer email. Party Data Model - Groups data model objects (DMO) that reflect contact information (personal orbusiness) for a specific customer or account. Privacy Data Model - Tracks and stores certain data privacy preferences. Keep in mind that decidinghow to honor customer privacy preferences is up to you. Product Subject Area - Defines anything you plan to sell or any part of a product to track for servicepurposes. Sales Order Data Object - Defines the future revenue or quantity for an opportunity by product familyand rolls it up by territory, management (role), or hierarchy. Additional Standard Data Model Objects - Includes other standard data model objects that can helporganize data in Customer Data Platform. Data Cloud Definitions Please find below a list of definitions you will need to understand before taking the exam. it's important you also know the abbreviations of words that have them (e.g. DMO) as the exam will mention them. Subject Area - A term used to group similar data objects to aid in data modelling Data Stream - A data source brought into Customer Data Platform Data Source Object (DSO) - The object that underpins the data object. Data Model Object (DMO) - A grouping of data (made up of attributes) that are created from datastreams, insights, and other sources Data Model Object (DMO) - The entity within the Data Cloud data model the consolidates data of the samenature from numerous data sources through the data lake objects. Customer 360 Data Model - Used to be known as Cloud Information Model (CIM), refers to the foundationof the Marketing Cloud Data Cloud standard data model. Attribute - A specific piece of data found in a DMO Foreign Key - A common link found between data sources that builds data relationships Segment - Filter your data to create useful segments to understand, target, and analyze your customers. Publish - Process of searching and building a segment based on the filter criteria. You can publish yoursegments on a chosen schedule or as needed. Activation - Process of moving audience segments to an activation target. Individuals - A specific person or customer from a specific data source, like Marketing Cloud. Unified individual - A customer profile whose data has been merged based on multiple sources usingIdentity Resolution rules. Harmonization: The process of mapping the ingested data in alignment with the Customer 360 DataModel. Data Explorer: Used to inspect the data in data source objects, data model objects, and calculatedinsights objects. Our Thoughts on the Exam Need to have a strong understanding of Data Ingestion & Modelling, as well as Identity Resolution. Need to understand on right to be forgotten. Trailhead course is commonly enough revision to sit exam Need to understand questions on activations Need to understand segmenting questions, including nested segments Most of the time they don’t refer to the data cloud terms in their full name, they will abbreviate (e.g. DLO, DMO) etc. So make sure you know the terms Understand the relationship between Marketing Cloud and Data Cloud, and connecting the two. E.g. what happens when you request to delete somebody - where it should be requested from? Need to understand Identity Resolutions - get up to speed on matching rules and reconciliation rules You can request a dev org for Data Cloud either from a project in Trailhead or from the Partner Learning Camp. A lot of people suggest using the Partner Learning Camp data cloud course there, but we personally think it goes beyond the knowledge you are required to know. We commonly only see students focus on Trailhead and hands on practice - knowing how each feature connects to the other, and the order it connects. Need to understand the order of things. E.g the order of implementing data cloud, the order or getting data digested, mapped, consolidated, etc. So make sure you are familiar with the order of Data Cloud, from Ingestion to Actions and Analysing Need to be familiar with DMOs, specifically Individual, and the different Contact Point objects. Be familiar with the contact point objects and parties. Need to know the subject areas (party, engagement, etc.). Need to understand the basics of what Amazon S3 is. Data Cloud Consultant Certification Revision Materials DescriptionLinkSalesforce Trailhead should be where you always start when undertaking a new exam. Each certification page in Trailhead will have a suggested Trailmix - complete this. (Free)Trailhead Data Cloud Consultant Certification TrailmixSalesforce Trailhead also has Exam Guides for each of its certifications, here it will outline what's expected of people sitting the exams, and what you should focus your revision on. (Free)Trailhead Data Cloud Consultant Certification Exam GuideBlack Cloud Data Cloud Consultant Practice Exams - sit free practice exam quesions that closely mirror that of the actual exam (Free)Black Cloud Data

Month: August 2024

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