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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Reporting & Analytics | 13% | - Configure conversion tracking - Analyze campaign effectiveness metrics - Interpret campaign performance reports - Use built-in dashboards and analytics tools |
| Topic 2: Campaigns & Experiences | 28% | - Create and manage in-app and mobile experiences - Configure personalization through decision logic - Use templates and creative assets - Implement campaign triggers and targeting rules - Build and configure web campaigns and experiences - Set up A/B testing and multivariate testing |
| Topic 3: Administration & Governance | 12% | - Manage API access and integrations - Manage user roles and permissions - Implement security and data privacy controls - Configure system settings and preferences |
| Topic 4: Product Overview | 7% | - Identify use cases for Marketing Cloud Personalization - Describe how Marketing Cloud Personalization integrates with other Marketing Cloud products - Explain the core concepts and value of the platform |
| Topic 5: Data Model & Configuration | 20% | - Set up user profiles and visitor identification - Manage dataset settings and configurations - Configure data ingestion methods (API, SDK, integrations) - Implement event data collection and tracking - Configure business logic elements (Objects, Attributes, Catalog) |
| Topic 6: Audience Management | 20% | - Configure rules-based and predictive audiences - Create and use filters and audiences in campaigns - Build and manage segments and audiences - Implement real-time audience segmentation - Use affinity and behavioral data for targeting |
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NEW QUESTION # 103
A marketer would like to display the most common products purchased by previous buyers along with the main item on a product page, which ingredient would they need to use in the recipe?
Answer: D
Explanation:
* TheCo-Buyingredient in a recipe identifies and displays products commonly purchased together.
* On a product page, it can recommend complementary items based on the main product being viewed.
References:
* Salesforce Interaction Studio Documentation - Recipes and Ingredients
NEW QUESTION # 104
How many times can a visitor accomplish a goal in interaction studio?
Answer: D
NEW QUESTION # 105
How does interaction studio deliver technical value to marketers?
Answer: D
Explanation:
Interaction Studio delivers technical value by enabling marketers to:
Listen to customer interactions across channels.
Understand intent using AI and machine learning.
Act in real time to deliver personalized experiences.
Reference: Salesforce Interaction Studio Technical Benefits Guide.
NEW QUESTION # 106
What are the three types of ingredients available for a recipe?
Answer: B,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 # 107
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 # 108
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