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| Section | Weight | Objectives |
|---|---|---|
| Campaigns & Experiences | 28% | - Implement campaign triggers and targeting rules - Set up A/B testing and multivariate testing - Use templates and creative assets - Create and manage in-app and mobile experiences - Configure personalization through decision logic - Build and configure web campaigns and experiences |
| Reporting & Analytics | 13% | - Interpret campaign performance reports - Configure conversion tracking - Analyze campaign effectiveness metrics - Use built-in dashboards and analytics tools |
| Audience Management | 20% | - Create and use filters and audiences in campaigns - Configure rules-based and predictive audiences - Use affinity and behavioral data for targeting - Implement real-time audience segmentation - Build and manage segments and audiences |
| Administration & Governance | 12% | - Implement security and data privacy controls - Manage API access and integrations - Manage user roles and permissions - Configure system settings and preferences |
| Product Overview | 7% | - Describe how Marketing Cloud Personalization integrates with other Marketing Cloud products - Explain the core concepts and value of the platform - Identify use cases for Marketing Cloud Personalization |
| Data Model & Configuration | 20% | - Configure data ingestion methods (API, SDK, integrations) - Manage dataset settings and configurations - Configure business logic elements (Objects, Attributes, Catalog) - Implement event data collection and tracking - Set up user profiles and visitor identification |
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NEW QUESTION # 90
Which three components of a recipe are optional when configuring a new algorithm?
Answer: A,D,E
Explanation:
When configuring a new recipe algorithm, the following components are optional:
* Boosters (Answer D):
* Allows prioritizing specific items based on business goals, such as promoting higher-margin products.
* Exclusions (Answer E):
* Filters out certain items or categories from being recommended.
* Variation (Answer C):
* Enables A/B testing by creating multiple variations of a recipe to optimize performance.
References:
* Salesforce Interaction Studio Documentation - Recipe Configuration
NEW QUESTION # 91
What is the standard mechanism Marketing Cloud Personalization uses to ingest data from the Marketing Cloud Personalization sFTP location?
Answer: B
Explanation:
Marketing Cloud Personalization usesETL feeds(Extract, Transform, Load) to ingest data from the sFTP location. This mechanism ensures efficient and scheduled data processing into Interaction Studio.
Reference: Salesforce Interaction Studio Data Ingestion Documentation.
NEW QUESTION # 92
What are the three types of ingredients available for a recipe?
Answer: A,C,D
Explanation:
Within the context of Salesforce Marketing Cloud ' s Interaction Studio (formerly Evergage), " recipes " are pre-built configurations for personalized recommendations. These recipes utilize different types of " ingredients " to determine which items to recommend. Let ' s break down the correct options:
A). Catalog-based and Trending
Verified: This is a type of ingredient used in Interaction Studio recipes.
Catalog-based: This ingredient leverages data from your product or content catalog. It can recommend items based on various catalog attributes like:
Category: Recommending items from the same or related categories as items the user has viewed or interacted with.
Attributes: Recommending items that share specific attributes (e.g., color, brand, size) with items the user has shown interest in.
Keywords: Recommending items whose descriptions or metadata match keywords derived from user behavior.
Trending: This ingredient considers the overall popularity or trending status of items within your catalog, often within a specific timeframe (e.g., " Trending in the last 7 days " ).
Salesforce Marketing Cloud References:
Interaction Studio Recipes: The Interaction Studio documentation describes the various recipe types and the ingredients they use.
B). Recommendations
Verified: This is a broad category encompassing ingredients that generate recommendations based on various algorithms.
Recommendation Algorithms: Interaction Studio employs different algorithms to generate recommendations, including:
Collaborative Filtering: Recommending items that similar users have liked or interacted with.
Content-Based Filtering: Recommending items that are similar in content or attributes to items the user has shown interest in.
User Affinity: Recommending items based on the user ' s overall affinity for particular categories, brands, or attributes, calculated from their historical interactions.
Note: " Recommendations " is a more general term. Specific recommendation ingredients might have names like " User-to-Item Affinity, " " Item-to-Item Similarity, " or use algorithm names directly.
C). Co-Occurrence
Verified: This is a specific type of recommendation ingredient that focuses on items frequently viewed or purchased together.
Co-occurrence Logic: This ingredient identifies items that are often viewed or purchased in the same session or within a short timeframe. It suggests that if a user is interested in item A, they are also likely to be interested in item B because other users have frequently interacted with both items together.
Examples:
" Customers who bought this item also bought... "
" Frequently viewed together "
NEW QUESTION # 93
What are the components of an Marketing Cloud Personalization web campaign?
Answer: A
NEW QUESTION # 94
How often is the Event Stream report refreshed?
Answer: C
Explanation:
* TheEvent Streamreport in Interaction Studio is refreshed every15 seconds.
* This near real-time update provides immediate visibility into user actions and behaviors.
References:
* Salesforce Interaction Studio Documentation - Event Stream
NEW QUESTION # 95
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