Salesforce Certified AI Associate Practice Test - Questions Answers, Page 4
List of questions
Question 31
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A Salesforce administrator creates a new field to capture an order's destination country.
Which field type should they use to ensure data quality?
Explanation:
''A picklist field type should be used to ensure data quality for capturing an order's destination country. A picklist field type allows the user to select one or more predefined values from a list. A picklist field type can ensure data quality by enforcing consistency, accuracy, and completeness of the data values.''
Question 32
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A customer using Einstein Prediction Builder is confused about why a certain prediction was made.
Following Salesforce's Trusted AI Principle of Transparency, which customer information should be accessible on the Salesforce Platform?
Explanation:
''An explanation of the prediction's rationale and a model card that describes how the model was created should be accessible on the Salesforce Platform following Salesforce's Trusted AI Principle of Transparency. Transparency means that AI systems should be designed and developed with respect for clarity and openness in how they work and why they make certain decisions. Transparency also means that AI users should be able to access relevant information and documentation about the AI systems they interact with.''
Question 33
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What are the potential consequences of an organization suffering from poor data quality?
Explanation:
The potential consequences of an organization suffering from poor data quality include revenue loss, poor customer service, and reputational damage. Poor data quality can lead to inaccurate analytics and decision-making, impacting customer interactions, marketing strategies, and financial forecasting. These issues ultimately affect customer satisfaction and could lead to financial losses and a damaged brand reputation. Salesforce highlights the importance of maintaining high data quality for effective CRM and AI applications, offering various tools and best practices to enhance data integrity. For guidance on managing and improving data quality in Salesforce, see the Salesforce documentation on data quality at Salesforce Data Quality.
Question 34
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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?
Explanation:
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 35
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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?
Explanation:
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 36
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Cloud Kicks wants to evaluate its data quality to ensure accurate and up-to-date records.
Which type of records negatively impact data quality?
Explanation:
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 37
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A sales manager is looking to enhance the quality of lead data in their CRM system.
Which process will most likely help the team accomplish this goal?
Explanation:
To enhance the quality of lead data in their CRM system, the most effective process is to review and update missing lead information. This process involves identifying incomplete records and filling in missing details, which can significantly improve the accuracy and usefulness of lead data. Accurate and complete lead information is crucial for effective lead scoring, prioritization, and follow-up, enhancing overall sales performance. Salesforce CRM offers data quality tools and features that assist in regularly reviewing and maintaining the accuracy of lead data. Information on managing lead data quality in Salesforce can be found at Salesforce Lead Management.
Question 38
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What is one technique to mitigate bias and ensure fairness in AI applications?
Explanation:
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 39
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Which type of AI can enhance customer service agents' email responses by analyzing the written content of previous emails?
Explanation:
Natural language processing (NLP) is the type of AI that can enhance customer service agents' email responses by analyzing the written content of previous emails. NLP technologies interpret and generate human language, allowing AI systems to understand, respond to, and even anticipate customer needs based on email interactions. This capability helps in crafting more relevant, accurate, and personalized email responses, improving customer service quality. Salesforce utilizes NLP in its Einstein AI platform to augment various customer service functions. More about Salesforce Einstein's NLP capabilities can be found on the Salesforce Einstein page at Salesforce Einstein NLP.
Question 40
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How does the 'right of least privilege' reduce the risk of handling sensitive personal data?
Explanation:
''The ''right of least privilege'' reduces the risk of handling sensitive personal data by limiting how many people have access to data. The ''right of least privilege'' is a security principle that states that each user or system should have the minimum level of access or privilege necessary to perform their tasks or functions. The ''right of least privilege'' can help protect sensitive personal data from unauthorized access, misuse, or leakage.''
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