Salesforce Certified AI Associate Exam Practice Test

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Total 102 questions
Question 1

What is one technique to mitigate bias and ensure fairness in AI applications?



Answer : A

A technique to mitigate bias and ensure fairness in AI applications is ongoing auditing and monitoring of the data used in AI applications. Regular audits help identify and address any biases that may exist in the data, ensuring that AI models function fairly and without prejudice. Monitoring involves continuously checking the performance of AI systems to safeguard against discriminatory outcomes. Salesforce emphasizes the importance of ethical AI practices, including transparency and fairness, which can be further explored through Salesforce's AI ethics guidelines at Salesforce AI Ethics.


Question 2

Cloud Kicks wants to evaluate its data quality to ensure accurate and up-to-date records.

Which type of records negatively impact data quality?



Answer : C

Duplicate records negatively impact data quality by creating inconsistencies and confusion in database management, leading to potential errors in customer relationship management (CRM) systems like Salesforce. Duplicates can skew analytics results, lead to inefficiencies in customer service, and result in redundant marketing efforts. Salesforce offers various tools to identify and merge duplicate records, thereby maintaining high data integrity. More about managing duplicate records in Salesforce and ensuring data quality can be found in Salesforce's documentation on duplicate management at Salesforce Duplicate Management.


Question 3

A business analyst (BA) is preparing a new use case for Al. They run a report to check for null values in the attributes they plan to use.

Which data quality component Is the BA verifying by checking for null values?



Answer : C

By checking for null values, a business analyst (BA) is verifying the data quality component of completeness. Completeness refers to the absence of missing values or gaps in the data, which is essential for the accuracy and reliability of reports and analytics used in AI models. Null values can indicate incomplete data, which may adversely affect the performance of AI applications by leading to incorrect predictions or insights. Salesforce emphasizes the importance of data completeness for effective data analysis and provides tools for data quality assessment and improvement. Details on handling data completeness in Salesforce can be explored at Salesforce Help Data Management.


Question 4

A developer has a large amount of data, but it is scattered across different systems and is not standardized.

Which key data quality element should they focus on to ensure the effectiveness of the AI models?



Answer : B

When data is scattered and not standardized, the key data quality element a developer should focus on is consistency. Consistency refers to the uniformity and standardization of data across different systems, which is crucial for integrating and analyzing data effectively, especially when developing AI models. Inconsistent data can lead to errors in analysis, poor AI model performance, and misleading insights. Salesforce provides tools and practices for ensuring data consistency, such as data integration and management solutions that help standardize and synchronize data across platforms. For more information on Salesforce data management, refer to the Salesforce data management tools at Salesforce Data Management.


Question 5

What is the significance of explainability of trusted AI systems?



Answer : C

The significance of the explainability of trusted AI systems is that it describes how AI models make decisions. Explainability is crucial for building trust and accountability in AI systems, ensuring that users and stakeholders understand the decision-making processes and outcomes generated by AI. This is particularly important in scenarios where AI decisions impact personal or financial status, such as in credit scoring or healthcare diagnostics. Salesforce emphasizes the importance of explainable AI through its ethical AI practices, aiming to make AI systems more transparent and understandable. More details about Salesforce's approach to ethical and explainable AI can be found in Salesforce AI ethics resources at Salesforce AI Ethics.


Question 6

What should be done to prevent bias from entering an AI system when training it?



Answer : B

''Using diverse training data is what should be done to prevent bias from entering an AI system when training it. Diverse training data means that the data covers a wide range of features and patterns that are relevant for the AI task. Diverse training data can help prevent bias by ensuring that the AI system learns from a balanced and representative sample of the target population or domain. Diverse training data can also help improve the accuracy and generalization of the AI system by capturing more variations and scenarios in the data.''


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?



Answer : C

''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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Total 102 questions