The ANC-301 exam validates your ability to implement and manage CRM Analytics within Salesforce environments. This certification is designed for professionals pursuing the Salesforce Consultant, CRM Analytics and Einstein Discovery Consultant credential who need to demonstrate hands-on expertise in analytics deployment, configuration, and governance. This page provides a structured study guide covering the exam syllabus, question formats, and practical preparation strategies to help you build confidence and achieve a passing score.
Use this topic map to guide your study for Salesforce ANC-301 (Implement and Manage CRM Analytics) within the Salesforce Consultant, CRM Analytics and Einstein Discovery Consultant path.
The ANC-301 exam uses multiple question types to assess both theoretical knowledge and practical decision-making in CRM Analytics implementation scenarios. Questions progress in difficulty and require you to apply concepts to realistic business situations.
Questions emphasize practical application, requiring you to connect discovery activities to implementation decisions and link dashboard design to underlying data architecture.
An effective study plan maps exam topics to weekly milestones and incorporates both conceptual review and hands-on practice. Allocate time proportionally to each domain, with extra focus on areas where you have less production experience. Use practice questions to identify weak spots and reinforce understanding through detailed explanations.
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Data connectivity, security implementation, and dashboard design typically account for a larger portion of the exam. However, all eight domains are tested, so balanced preparation across all topics is essential. Focus extra effort on areas where you lack hands-on production experience to close knowledge gaps.
Discovery and Planning establishes the foundation by defining user personas, data requirements, and security policies. These decisions directly inform your choices during Set Up Users and Apps, data connection strategy, and security configuration. Understanding this workflow helps you see why certain setup decisions are made and prepares you for scenario-based questions that test this end-to-end thinking.
Direct experience building connectors, creating datasets with extended metadata, and configuring dashboard bindings is highly valuable. If you lack production access, prioritize Salesforce sandbox labs and practice environments that let you perform these tasks. Familiarity with SAQL query syntax and dashboard filter logic will significantly boost your confidence on the exam.
Many candidates overlook the importance of row-level security and data governance in implementation planning, treating security as an afterthought rather than a core requirement. Others struggle with query optimization questions because they lack hands-on experience tuning SAQL. Additionally, misunderstanding the relationship between bindings and dashboard interactivity leads to incorrect scenario answers. Review security policies and query performance concepts thoroughly to avoid these pitfalls.
In your final week, take a full-length timed practice test to identify remaining weak areas and build pacing skills. Review explanations for any questions you missed, focusing on the reasoning behind correct answers rather than memorizing facts. Spend your last few days reviewing high-weight topics like data security and dashboard design, and ensure you understand how different concepts connect in real-world implementation workflows.
A company wants to create a timeline chart to visualize the evolution of its Closed Won opportunities.
What are the required parameters to build a lens that displays output similar to the image shown?
To create a timeline chart similar to the one shown, the following parameters are typically required:
1 Measure: This could be the count of Closed Won opportunities or any other relevant metric that needs to be tracked over time.
1 Grouping by a Date Field: This is essential to plot the timeline effectively. The date field would typically be the close date of the opportunities.
Additional Groupings: Depending on the complexity and the detail needed, additional groupings can be added. For example, grouping by region or product line can provide more insights into the timeline. If trellis is used, it allows for the creation of multiple smaller charts within the main chart, each representing a slice of data based on the additional groupings.
This setup helps visualize the evolution of Closed Won opportunities over time, making it easy to spot trends, seasonal patterns, or other relevant insights.
CRM Analytics users at Cloud Kicks are granted access to an app with specific dashboards. When trying to download a specific widget, they are unable to do so.
In CRM Analytics, even if users are granted access to view an app and its dashboards, their ability to download data is controlled by permissions assigned via permission sets. Specifically, users need the 'Download Data' permission to download data from widgets or dashboards. If this permission is missing from their permission set, they will be unable to download the specific widget, even though they can view the data.
Universal Containers' CRM Analytics team is building a dashboard with two widgets.
1. List widget associated to the query "Type_2" and grouped by the dimension "Type" (multi-selection)
2. Donut chart widget associated to the query "Step_pie_3" and grouped by the dimension "Type"
The team wants to use bindings/interactions so any selection in the List widget will filter the Donut chart. The queries use different datasets,
and users should be able to choose more than one Type (multi-selection).
What is the right syntax for the binding/interaction?
A)
B)
C)
For the given requirement where a selection in a list widget needs to filter data displayed on a donut chart, and considering the list allows for multi-selection of the 'Type', the correct binding/interaction would be to use a syntax that captures the multi-select aspect and passes it appropriately. The right syntax, as indicated in Option A, looks like this:
'{{column(Type_2.selection, [\'Type\']).asObject()}}'
This syntax ensures:
Multi-selection: The column() function in combination with .asObject() ensures that multiple selected values from the 'Type_2' query can be passed as an object, which the donut chart can utilize to filter its content.
Correct Data Type Handling: By using .asObject(), the binding ensures the data passed between widgets maintains the correct structure expected by the CRM Analytics dashboard, thereby ensuring accurate filtering.
A consultant is tasked with creating a dataset and a dashboard for a sales team. During the requirements gathering, it was highlighted that security of the data is important.
It was noted that the Opportunity object has organization-wide defaults set to Private with access via the role hierarchy. Sales wants to keep this security in place for the dashboard. Looking at the Opportunity data, the consultant sees that the VP of sales can have access to up to 20,000 records and is unsure if sharing inheritance can be used.
Which approach ensures data security for the new Opportunity dataset?
For ensuring that the security settings on the Opportunity object are appropriately replicated in the CRM Analytics environment, running the Sharing Inheritance Coverage Assessment is an effective strategy. Here's why:
Assessment of Inheritance Feasibility: This tool assesses whether sharing rules on the Opportunity object can be effectively inherited in the analytics environment, ensuring that the organization-wide defaults and role hierarchy are maintained.
Identification of Limitations: The assessment will identify any potential issues or limitations with inheriting sharing settings due to the large number of records (up to 20,000 for the VP of Sales), providing clear insights on how to proceed.
Guided Decision Making: Based on the outcome of the assessment, the consultant can make informed decisions about whether to directly inherit sharing or consider alternative strategies like flattening the role hierarchy or using security predicates.
consultant is tasked with creating an opportunity dataset for a new analytics app. One requirement is to make sure users only see the opportunities they have access to in Salesforce. Opportunity records are private but shared using the role hierarchy. The consultant runs
the sharing inheritance coverage assessment and finds that the VP of sales is not covered by the sharing inheritance. The consultant decides to proceed with using sharing inheritance for the dataset.
What else does the consultant need to do?
