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Free Salesforce Salesforce-AI-Associate Exam Questions (page: 2)

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PDF with 106 Questions

What is a benefit of a diverse, balanced, and large dataset?

  1. Training time
  2. Data privacy
  3. Model accuracy

Answer(s): C

Explanation:

"Model accuracy is a benefit of a diverse, balanced, and large dataset. A diverse dataset can capture a variety of features and patterns that are relevant for the AI task. A balanced dataset can avoid overfitting or underfitting the model to a specific subset of data. A large dataset can provide enough information for the model to learn from and generalize well to new data."



How does a data quality assessment impact business outcome for companies using AI?

  1. Improves the speed of AI recommendations
  2. Accelerates the delivery of new AI solutions
  3. Provides a benchmark for AI predictions

Answer(s): C

Explanation:

"A data quality assessment impacts business outcomes for companies using AI by providing a benchmark for AI predictions. A data quality assessment is a process that measures and evaluates the quality of data for a specific purpose or task. A data quality assessment can help identify and address any issues or gaps in the data quality dimensions, such as accuracy, completeness, consistency, relevance, and timeliness. A data quality assessment can impact business outcomes for companies using AI by providing a benchmark for AI predictions, as it can help ensure that the predictions are based on high-quality data that reflects the true state or condition of the target population or domain."



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

  1. Geographic
  2. Geographic
  3. Cryptographic

Answer(s): B

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."



The Cloud technical team is assessing the effectiveness of their AI development processes? Which established Salesforce Ethical Maturity Model should the team use to guide the development of trusted AI solution?

  1. Ethical AI Prediction Maturity Model
  2. Ethical AI Process Maturity Model
  3. Ethical AI practice Maturity Model

Answer(s): B

Explanation:

"The Ethical AI Process Maturity Model is the established Salesforce Ethical Maturity Model that the Cloud technical team should use to guide the development of trusted AI solutions. The Ethical AI Process Maturity Model is a framework that helps assess and improve the ethical and responsible practices and processes involved in developing and deploying AI systems. The Ethical AI Process Maturity Model consists of five levels of maturity: Ad Hoc, Aware, Defined, Managed, and Optimized. The Ethical AI Process Maturity Model can help guide the development of trusted AI solutions by providing a roadmap and best practices for achieving higher levels of ethical maturity."



What is a potential outcome of using poor-quality data in AI application?

  1. AI model training becomes slower and less efficient
  2. AI models may produce biased or erroneous results.
  3. AI models become more interpretable

Answer(s): B

Explanation:

"A potential outcome of using poor-quality data in AI applications is that AI models may produce biased or erroneous results. Poor-quality data means that the data is inaccurate, incomplete, inconsistent, irrelevant, or outdated for the AI task. Poor-quality data can affect the performance and reliability of AI models, as they may not have enough or correct information to learn from or make accurate predictions. Poor-quality data can also introduce or exacerbate biases or errors in AI models, such as human bias, societal bias, confirmation bias, or overfitting or underfitting."



What is the rile of data quality in achieving AI business Objectives?

  1. Data quality is unnecessary because AI can work with all data types.
  2. Data quality is required to create accurate AI data insights.
  3. Data quality is important for maintain Ai data storage limits

Answer(s): B

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."



Which type of bias imposes a system `s values on others?

  1. Societal
  2. Automation
  3. Association

Answer(s): A

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."



Which best describes the different between predictive AI and generative AI?

  1. Predictive new and original output for a given input.
  2. Predictive AI and generative have the same capabilities differ in the type of input they receive:
    predictive AI receives raw data whereas generation AI receives natural language.
  3. Predictive AI uses machine learning to classes or predict output from its input data whereas generative AI does not use machine learning to generate its output

Answer(s): A

Explanation:

"The difference between predictive AI and generative AI is that predictive AI analyzes existing data to make predictions or recommendations based on patterns or trends, while generative AI creates new content based on existing data or inputs. Predictive AI is a type of AI that uses machine learning techniques to learn from existing data and make predictions or recommendations based on the data. For example, predictive AI can be used to forecast sales, revenue, or demand based on historical data and trends. Generative AI is a type of AI that uses machine learning techniques to generate novel content such as images, text, music, or video based on existing data or inputs. For example, generative AI can be used to create realistic faces, write summaries, compose songs, or produce videos."



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