SALESFORCE-AI-ASSOCIATE EXAM DUMPS DEMO - SALESFORCE-AI-ASSOCIATE REGUALER UPDATE

Salesforce-AI-Associate Exam Dumps Demo - Salesforce-AI-Associate Regualer Update

Salesforce-AI-Associate Exam Dumps Demo - Salesforce-AI-Associate Regualer Update

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Salesforce Certified AI Associate Exam Sample Questions (Q73-Q78):

NEW QUESTION # 73
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?

  • A. Completeness
  • B. Duplication
  • C. Usage

Answer: A

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.


NEW QUESTION # 74
What is the rile of data quality in achieving AI business Objectives?

  • A. Data quality is required to create accurate AI data insights.
  • B. Data quality is unnecessary because AI can work with all data types.
  • C. Data quality is important for maintain Ai data storage limits

Answer: A

Explanation:
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."


NEW QUESTION # 75
Which data does Salesforce automatically exclude from marketing Cloud Einstein engagement model training to mitigate bias and ethic...

  • A. Geographic
  • B. Geographic
  • C. Cryptographic

Answer: A

Explanation:
"Demographic data is thedata 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."


NEW QUESTION # 76
What Is a benefit of data quality and transparency as it pertains to bias in generated AI?

  • A. Chances of bias are remove
  • B. Chances of bias are aggravated
  • C. Chances of bIas and mitigated

Answer: C

Explanation:
"Data quality and transparency can help mitigate the chances of bias in generative AI. Data quality means that the data is accurate, complete, consistent, relevant, and timely for the AI task. Data quality can help mitigate bias by ensuring that the generative AI model learns from a balanced and representative sample of the target population or domain. Data transparency means that the data sources, methods, and processes are clear and open to inspection and verification. Data transparency can help mitigate bias by allowing users to understand and evaluate the dataused or generated by the generative AI model."


NEW QUESTION # 77
Cloud Kicks discovered multiple variations of state and country values in contact records.
Which data quality dimension is affected by this issue?

  • A. Consistency
  • B. Accuracy
  • C. Usage

Answer: A

Explanation:
Explanation
"Consistency is the data quality dimension that is affected by multiple variations of state and country values in contact records. 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."


NEW QUESTION # 78
......

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