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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implementation and Configuration | 16% | - Configure user and visitor authentication - Set up and configure the Web Media Manager - Configure campaign areas and zones - Set up data collection and event processing - Implement JavaScript beacon and tag management |
| Topic 2: Audience Management | 16% | - Use profile attributes and engagement scoring - Create and manage segments - Configure dynamic audience membership - Implement catalog-based recommendations - Build audience criteria and filters |
| Topic 3: Campaigns and Experiences | 24% | - Create and manage campaigns - Implement decision logic and rules - Set up triggers and response handling - Configure audience targeting and segmentation - Create and test campaign variations - Build experiences using drag-and-drop editor |
| Topic 4: Overview and Key Concepts | 12% | - Identify use cases for Marketing Cloud Personalization - Define Marketing Cloud Personalization - Describe the platform architecture and components - Explain the difference between Real-Time and Batch modes |
| Topic 5: Administration and Governance | 20% | - Manage user roles and permissions - Monitor system health and troubleshooting - Manage integrations with Marketing Cloud - Configure system settings and preferences - Understand compliance and security considerations - Implement data governance policies |
| Topic 6: Reporting and Analytics | 12% | - Analyze campaign performance metrics - Create and customize reports - Navigate and use the Reports dashboard - Interpret engagement and conversion data |
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NEW QUESTION # 118
What are Marketing Cloud Personalization's machine learning powered algorithms called?
Answer: B
Explanation:
Marketing Cloud Personalization'smachine learning-powered algorithmsare referred to asEinstein Recipes.
* They enable personalized recommendations and decisions by combining machine learning models with business logic.
* Marketers can customize these recipes to optimize recommendations based on specific use cases.
References:
* Salesforce Interaction Studio Documentation - Einstein Recipes
NEW QUESTION # 119
Which ingredient shows a visitor products or content based on a 'people like me' algorithm?
Answer: C
Explanation:
TheCollaborative Filteringingredient uses a "people like me" algorithm to recommend products or content based on the behavior of similar users.
Reference: Salesforce Interaction Studio Recipe Ingredients Documentation.
NEW QUESTION # 120
How quickly does Marketing Cloud Personalization synthesize and activate data to gauge and respond to an individual's in-the-moment intent, across channels?
Answer: C
Explanation:
Marketing Cloud Personalization synthesizes and activates data within30 milliseconds, enabling real-time personalization and decision-making across channels.
Reference: Salesforce Interaction Studio Real-Time Engagement Documentation.
NEW QUESTION # 121
In the user interface, what is the visual representation of the data about a single visitor including preferences and affinities?
Answer: C
Explanation:
TheUnified Customer Profilevisually represents data about a single visitor, including preferences, affinities, and behaviors. It provides a comprehensive view of the customer's interactions across channels.
Reference: Salesforce Interaction Studio User Profile Documentation.
NEW QUESTION # 122
Which two successs metrics can a company achieve with IS their web channel?
Answer: A,D
Explanation:
* Interaction Studio (IS) helps businesses achieve significant success metrics through web channel personalization:
* Increase in Conversion Rate: By delivering tailored experiences and recommendations, IS drives more users to take desired actions (e.g., purchases, sign-ups).
* Increase in Revenue: Enhanced user engagement and conversions contribute directly to revenue growth.
NEW QUESTION # 123
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