
Part 7 - Salesforce AI Associate
Authored by quizizz psocial
Information Technology (IT)
KG
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9 questions
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1.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
A developer is tasked with selecting a suitable dataset for training an AI model in Salesforce to accurately predict current customer behavior. What is a crucial factor that the developer should consider during selection?
Size of dataset
Number of variables in the dataset
Age of dataset
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is an implication of user consent in regard to AI data privacy?
AI ensures complete data privacy by automatically obtaining user consent.
AI operates independently of user privacy and consent.
AI infringes on privacy when user consent is not obtained.
3.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Cloud Kicks learns of complaints from customers who are receiving too many sales calls and emails. Which data quality dimension should be assessed to reduce these communication inefficiencies?
Usage
Duplication
Consent
4.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Cloud Kicks wants to ensure that multiple records for the same customer are removed from Salesforce. Which feature should be used to accomplish this?
Duplicate management
Trigger deletion of old records
Standardized field names
5.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Cloud Kicks wants to develop a solution to predict customers' product interests based on historical data. The company found that employees from one region use a text field to capture the product category, while employees from other locations use a picklist. Which data quality dimension is affected in this scenario?
Completeness
Consistency
Accuracy
6.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is a key benefit of effective interaction between humans and AI systems?
Leads to more informed and balanced decision-making
Reduces the need for human involvement
Alerts humans to the presence of biased data
7.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
How does data quality impact the trustworthiness of AI-driven decisions?
High-quality data improves the reliability and credibility of AI-driven decisions, fostering trust among users.
The use of both low-quality and high-quality data can improve the accuracy and reliability of AI-driven decisions.
Low-quality data reduces the risk of overfitting the model, improving the trustworthiness of the predictions.
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