Salesforce SALESFORCE-AI-ASSOCIATE Online Practice Questions and Exam Preparation

Salesforce SALESFORCE-AI-ASSOCIATE Online Questions & Answers

• Question 1:

  Cloud Kicks wants to improve the quality of its AI model's predictions with the use of a large amount of data.

  Which data quality element should the company focus on?

  A. Accuracy
  B. Location
  C. Volume
  A. Accuracy

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  explanation:

  Explanation/Reference:

  To improve the quality of AI model predictions, Cloud Kicks should focus on the accuracy of the data. Accurate data ensures that the insights and predictions generated by AI models are reliable and valid. Data accuracy involves correcting errors, filling missing values, and verifying data sources to enhance the quality of information fed into the AI systems. Focusing on data accuracy helps in minimizing prediction errors and enhances the decision-making process based on AI insights. For more details on the importance of data quality in AI models, Salesforce provides extensive guidance in their documentation, which can be found at Data Quality and AI.

• Question 2:

  Cloud Kicks implements a new product recommendation feature for its shoppers that recommends shoes of a given color to display to customers based on the color of the products from their purchase history.

  Which type of bias is most likely to be encountered in this scenario?

  A. Confirmation
  B. Survivorship
  C. Societal
  A. Confirmation

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  explanation:

  Explanation/Reference:

  "Confirmation bias is most likely to be encountered in this scenario. Confirmation bias is a type of bias that occurs when data or information confirms or supports one's existing beliefs or expectations. For example, confirmation bias can occur when a product recommendation feature only recommends shoes of a given color based on the customer's purchase history, without considering other factors or preferences that may influence their choice."

• Question 3:

  What is a potential source of bias in training data for AI models?

  A. The data is collected in area time from sources systems.
  B. The data is skewed toward is particular demographic or source.
  C. The data is collected from a diverse range of sources and demographics.
  B. The data is skewed toward is particular demographic or source.

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  explanation:

  Explanation/Reference:

  "A potential source of bias in training data for AI models is that the data is skewed toward a particular demographic or source. Skewed data means that the data is not balanced or representative of the target population or domain. Skewed data can introduce or exacerbate bias in AI models, as they may overfit or underfit the model to a specific subset of data. For example, skewed data can lead to bias if the data is collected from a limited or biased demographic or source, such as a certain age group, gender, race, location, or platform."

• Question 4:

  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?

  A. An explanation of how Prediction Builder works and a link to Salesforce's Trusted AI Principles
  B. An explanation of the prediction's rationale and a model card that describes how the model was created
  C. A marketing article of the product that clearly outlines the oroduct's capabilities and features
  B. An explanation of the prediction's rationale and a model card that describes how the model was created

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  explanation:

  Explanation/Reference:

  "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 5:

  A developer is tasked with selecting a suitable dataset for training an AI model in Salesforce to accurately predict current customer behavior.

  What Is a crucial factor that the developer should consider during selection?

  A. Number of variables ipn the dataset
  B. Size of the dataset
  C. Age of the dataset
  B. Size of the dataset

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  explanation:

  Explanation/Reference:

  "The size of the dataset is a crucial factor that the developer should consider during selection. The size of the dataset refers to the amount or volume of data available for training an AI model. The size of the dataset can affect the feasibility and quality of the AI model, as well as the choice of AI techniques and tools. The size of the dataset should be large enough to provide sufficient information for the AI model to learn from and generalize well to new data."

• Question 6:

  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?

  A. Ethical AI Prediction Maturity Model
  B. Ethical AI Process Maturity Model
  C. Ethical AI practice Maturity Model
  B. Ethical AI Process Maturity Model

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  explanation:

  Explanation/Reference:

  "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."

• Question 7:

  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.

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  explanation:

  Explanation/Reference:

  "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 8:

  What role does data quality play in the ethical us of AI applications?

  A. High-quality data is essential for ensuring unbased and for fair AI decisions, promoting ethical use, and preventing discrimi...
  B. High-quality data ensures the process of demographic attributes requires for personalized campaigns.
  C. Low-quality data reduces the risk of unintended bias as the data is not overfitted to demographic groups.
  A. High-quality data is essential for ensuring unbased and for fair AI decisions, promoting ethical use, and preventing discrimi...

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  explanation:

  Explanation/Reference:

  "High-quality data is essential for ensuring unbiased and fair AI decisions, promoting ethical use, and preventing discrimination. High-quality data means that the data is accurate, complete, consistent, relevant, and timely for the AI task. High-quality data can help ensure unbiased and fair AI decisions by providing a balanced and representative sample of the target population or domain. High-quality data can also help promote ethical use and prevent discrimination by respecting the rights and preferences of users regarding their personal data."

• Question 9:

  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.

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  explanation:

  Explanation/Reference:

  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 10:

  What is a societal implication of excluding ethics in AI development?

  A. Faster and cheaper development
  B. More innovation and creativity
  C. Harm to marginalized communities
  C. Harm to marginalized communities

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  explanation:

  Explanation/Reference:

  Excluding ethics in AI development can lead to societal implications such as harm to marginalized communities. When ethical considerations are not integrated into AI development, the resulting technologies may perpetuate or amplify biases, leading to unfair treatment or discrimination against certain groups. This can reinforce existing social inequalities and prevent these communities from benefiting equally from the advancements in AI technology. Salesforce is committed to responsible AI development and emphasizes the importance of ethical considerations in their development practices to prevent such outcomes. Details on Salesforce's approach to ethical AI and its importance can be found at Salesforce Ethical AI.