Question # 1
| How does AI which CRM help sales representatives better understand previous customer interactions? | | A. Creates, localizes, and translates product descriptions | | B. Triggers personalized service replies | | C. Provides call summaries |
C. Provides call summaries
Explanation: “Providing call summaries is how AI with CRM helps sales representatives better understand previous customer interactions. Call summaries are a feature that uses natural language processing (NLP) to analyze voice conversations between sales representatives and customers and generate summaries or transcripts of the calls. Call summaries can help sales representatives better understand previous customer interactions by providing key information, insights, or action items from the calls.”
Question # 2
| In the context of Salesforce’s Trusted AI Principles what does the principle of Empowerment primarily aim to achieve? | | A. Empower users to off all skill level to build AI application with clicks, not code. | | B. Empower users to contribute to the growing body of knowledge of leading AI research. | | C. Empower users to solve challenging technical problems using neural networks. |
A. Empower users to off all skill level to build AI application with clicks, not code.
Explanation: “The principle of Empowerment primarily aims to achieve empowering users of all skill levels to build AI applications with clicks, not code. Empowerment is one of the Trusted AI Principles that states that AI systems should be designed and developed with respect for the empowerment and education of humans. Empowering users means enabling users to access, use, and benefit from AI systems regardless of their technical expertise or background. For example, empowering users means providing tools and platforms that allow users to build AI applications with clicks, not code, such as Einstein Prediction Builder or Einstein Discovery.”
Question # 3
| Which data does Salesforce automatically exclude from marketing Cloud Einstein engagement model training to mitigate bias and ethic… | | A. Geographic | | B. Geographic | | C. Cryptographic |
B. Geographic
Explanation: “Demographic data is the data that Salesforce automatically excludes from Marketing Cloud Einstein engagement model training to mitigate bias and ethical concerns. Demographic data is data that describes the characteristics of a population or a group of people, such as age, gender, race, ethnicity, income, education, or occupation. Demographic data can lead to bias if it is used to discriminate or treat people differently based on their identity or attributes. Demographic data can also reflect existing biases or stereotypes in society or culture, which can affect the fairness and ethics of AI systems. Salesforce excludes demographic data from Marketing Cloud Einstein engagement model training to mitigate bias and ethical concerns by ensuring that the models are based on behavioral data rather than personal data.”
Question # 4
| Cloud kicks wants to develop a solution to predict customers’ interest based on historical data. The company found that employee region uses a text field to capture the product category while employee from all other locations use a picklist.
Which dimension of data quality is affected in this scenario? | | A. Accuracy | | B. Consistency | | C. Completeness |
B. Consistency
Explanation: “Consistency is the dimension of data quality that is affected in this scenario. Consistency means that the data values are uniform and follow a common standard or format across different records, fields, or sources. Inconsistent data can cause confusion, errors, or duplication in data analysis and processing. For example, using different field types for the same attribute can affect the consistency of the data.”
Question # 5
| What is a Key consideration regarding data quality in AI implementation? | | A. Techniques from customizing AI features in Salesforce | | B. Data’s role in training and fine-tuning Salesforce AI models | | C. Integration process of AI models with Salesforce workflows |
B. Data’s role in training and fine-tuning Salesforce AI models
Explanation: “Data’s role in training and fine-tuning Salesforce AI models is a key consideration regarding data quality in AI implementation. Data quality is the degree to which data is accurate, complete, consistent, relevant, and timely for the AI task. Data quality can affect the performance and reliability of AI systems, as they depend on the quality of the data they use to learn from and make predictions. Data’s role in training and fine-tuning Salesforce AI models means understanding how data is used to build, train, test, and improve AI models in Salesforce, such as Einstein Prediction Builder or Einstein Discovery.”
Question # 6
| To avoid introducing unintended bias to an AI model, which type of data should be omitted? | | A. Transactional | | B. Engagement | | C. Demographic |
C. Demographic
Explanation: “Demographic data should be omitted to avoid introducing unintended bias to an AI model. Demographic data is data that describes the characteristics of a population or a group of people, such as age, gender, race, ethnicity, income, education, or occupation. Demographic data can lead to bias if it is used to discriminate or treat people differently based on their identity or attributes. Demographic data can also reflect existing biases or stereotypes in society or culture, which can affect the fairness and ethics of AI systems.”
Question # 7
Cloud Kicks wants to use Einstein Prediction Builder to determine a customer’s likelihood of buying specific products; however, data quality is a…
How can data quality be assessed quality?
| | A. Build a Data Management Strategy. | | B. Build reports to expire the data quality. | | C. Leverage data quality apps from AppExchange |
C. Leverage data quality apps from AppExchange
Explanation:
“Leveraging data quality apps from AppExchange is how data quality can be assessed. Data quality is the degree to which data is accurate, complete, consistent, relevant, and timely for the AI task. Data quality can affect the performance and reliability of AI systems, as they depend on the quality of the data they use to learn from and make predictions. Leveraging data quality apps from AppExchange means using third-party applications or solutions that can help measure, monitor, or improve data quality in Salesforce.”
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FAQs of Salesforce-AI-Associate Exam
What is covered under Einstein AI Basics in the Salesforce-AI-Associate exam?
It includes fundamental concepts of Salesforce Einstein AI, like predictive analytics and machine learning principles.
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Data Preparation involves cleaning and organizing data to make it suitable for processing by Salesforce AI models.
How is Einstein Discovery used in Salesforce?
Einstein Discovery is used for data analysis, uncovering patterns, and making predictive recommendations based on data.
What is the role of Einstein Prediction Builder in Salesforce?
It enables creating custom predictive models that help in making informed business decisions based on data.
Can you explain Einstein Language and Vision in Salesforce?
Einstein Language processes natural language for sentiment analysis
and intent detection, while Einstein Vision focuses on image
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The exam typically consists of multiple-choice questions, covering theoretical and practical aspects of Salesforce AI.
Are there prerequisites for the Salesforce-AI-Associate exam?
There are no formal prerequisites, but a basic understanding of Salesforce and AI concepts is recommended.
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The exam duration is usually 90 minutes.
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