Question # 1
| What should an organization do to enforce consistency across accounts for newly entered records? | | A. Merge all duplicate accounts into a single record when duplicate entries are detected. | | B. Input the data exactly as it appears from the source, such as the company’s website or social media, | | C. Implement naming conventions or a predefined list of user-selectable values for organization-wide records. |
C. Implement naming conventions or a predefined list of user-selectable values for organization-wide records.
Explanation: To ensure consistency across accounts for newly entered records, organizations should implement naming conventions or a predefined list of user-selectable values. This approach standardizes data entry, reducing variations and errors. It also helps in maintaining clean data which is essential for accurate reporting and analytics. Using standardized naming conventions ensures that all users adhere to a consistent format, making it easier to manage and analyze data across the organization. For more information on best practices for data management in Salesforce, refer to Salesforce's documentation on Data Management Best Practices.
Question # 2
| 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 # 3
| Which type of bias imposes a system ‘s values on others? | | A. Societal | | B. Automation | | C. Association |
A. Societal
Explanation: “Societal bias is the type of bias that imposes a system’s values on others. Societal bias is a type of bias that reflects the assumptions, norms, or values of a specific society or culture. Societal bias can affect the fairness and ethics of AI systems, as they may affect how different groups or domains are perceived, treated, or represented by AI systems. For example, societal bias can occur when AI systems impose a system’s values on others, such as using Western standards of beauty or success to judge or rank people from other cultures.”
Question # 4
| What is the rile of data quality in achieving AI business Objectives? | | A. Data quality is unnecessary because AI can work with all data types. | | B. Data quality is required to create accurate AI data insights. | | C. Data quality is important for maintain Ai data storage limits |
B. Data quality is required to create accurate AI data insights.
Explanation: “Data quality is required to create accurate AI data insights. 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 quality can also affect the accuracy and validity of AI data insights, as they reflect the quality of the data used or generated by AI systems.”
Question # 5
| Cloud Kicks plans to use automated chat as its primary support channel.
Which Einstein feature should they use? | | A. Discovery | | B. Bots | | C. Next Best Action |
B. Bots
Explanation: For Cloud Kicks, using automated chat as the primary support channel, the recommended Einstein feature is Bots. Einstein Bots are designed to automate customer interactions on common issues through chat and messaging platforms. They can handle routine requests, provide quick answers to frequently asked questions, and escalate more complex issues to human agents. Using Einstein Bots helps improve customer service
efficiency and speed, leading to enhanced customer satisfaction. To learn more about setting up and optimizing Einstein Bots for a business, you can visit the Salesforce documentation on Einstein Bots at Salesforce Einstein Bots.
Question # 6
| Cloud Kicks wants to implement AI features on its Salesforce Platform but has concerns about potential ethical and privacy challenges. What should they consider doing to minimize potential AI bias? | | A. Use demographic data to identify minority groups. | | B. Integrate AI models that auto-correct biased data. | | C. Implement Salesforce's Trusted AI Principles. |
C. Implement Salesforce's Trusted AI Principles.
Question # 7
| What is an example of ethical debt? | | A. Violating a data privacy law and failing to pay fines | | B. Delaying an AI product launch to retrain an AI data model | | C. Launching an AI feature after discovering a harmful bias |
C. Launching an AI feature after discovering a harmful bias
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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.
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It enables creating custom predictive models that help in making informed business decisions based on data.
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Einstein Language processes natural language for sentiment analysis
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The exam typically consists of multiple-choice questions, covering theoretical and practical aspects of Salesforce AI.
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There are no formal prerequisites, but a basic understanding of Salesforce and AI concepts is recommended.
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