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Data Quality and AI Applications Quiz
37 Questions
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Data Quality and AI Applications Quiz

Created by
@SportyCouplet

Questions and Answers

Which data quality dimension is crucial for reducing communication inefficiencies?

  • Format
  • Accessibility
  • Timeliness
  • Content (correct)

    • What AI applications should a business analyst use to improve sales processes and customer support?

  • Lead scoring, opportunity forecasting, and case classification (correct)
  • Customer segmentation and case resolution
  • Sales tracking and marketing automation
  • Case classification and opportunity forecasting

    • What could be a reason for inaccurate predictions generated by Einstein at Cloud Kicks?

  • Insufficient historical data
  • Poor data quality (correct)
  • Improper model selection
  • Inadequate regional characteristics

    • Which feature should a data quality expert use to ensure that every new contact at Cloud Kicks has at least an email address or phone number?

    <p>Validation rule</p> Signup and view all the answers

    What dimension of data quality is affected when employees from one region use a text field while others use a picklist to describe product categories?

    <p>Consistency</p> Signup and view all the answers

    What essential data quality dimension is needed for an AI model to predict demand for shoes using historical data?

    <p>Reliability</p> Signup and view all the answers

    How does AI within CRM assist sales representatives in understanding previous customer interactions?

    <p>By generating comprehensive call summaries</p> Signup and view all the answers

    What is the primary impact of high-quality data on customer relationships?

    <p>Improved customer trust and satisfaction</p> Signup and view all the answers

    Which data quality dimension is critical for the successful operation of Cloud Kicks' custom analytics application in Salesforce?

    <p>Consistency</p> Signup and view all the answers

    What action should organizations take to enhance data quality for their AI initiatives?

    <p>Collect and curate high-quality data from reliable sources</p> Signup and view all the answers

    Which Einstein capability utilizes emails to generate content for Knowledge articles?

    <p>Generate</p> Signup and view all the answers

    What type of bias arises from labeling data based on stereotypes?

    <p>Associations</p> Signup and view all the answers

    Which is an example of an immutable trait that could be considered sensitive?

    <p>Financial status</p> Signup and view all the answers

    How does incomplete data quality affect Cloud Kicks' operations regarding product recommendations?

    <p>It hinders the accuracy of product recommendations.</p> Signup and view all the answers

    What is a primary advantage of using AI to improve customer experience in CRM?

    <p>It fully automates customer service experiences.</p> Signup and view all the answers

    Which approach aligns with the Trusted AI Principles of Inclusivity when testing a new AI model?

    <p>Test with diverse and representative datasets appropriate for how the model will be used</p> Signup and view all the answers

    What is the primary function of Einstein Discovery?

    <p>To provide insights and recommendations based on data</p> Signup and view all the answers

    Which field of AI is most suitable for implementing a chatbot that answers frequently asked questions?

    <p>Natural language processing</p> Signup and view all the answers

    What contributes to inaccurate predictions when using Einstein in Salesforce?

    <p>Poor data quality</p> Signup and view all the answers

    What does Einstein Prediction focus on within Salesforce AI?

    <p>Forecasting business outcomes</p> Signup and view all the answers

    Why is it important to consider privacy concerns when using AI and CRM data?

    <p>To ensure compliance with laws and regulations</p> Signup and view all the answers

    Which principle in Salesforce's Trusted AI Principles is primarily aimed at providing a framework for model accuracy?

    <p>Transparency</p> Signup and view all the answers

    What is a sensitive variable that can lead to bias in AI-generated predictions?

    <p>Gender</p> Signup and view all the answers

    What should Cloud Kicks determine first to prepare its data for AI integration?

    <p>Data availability</p> Signup and view all the answers

    What is a potential consequence of biased training data in AI models?

    <p>Skewed predictions</p> Signup and view all the answers

    What benefit does data quality and transparency provide in relation to bias in AI?

    <p>It mitigates the chances of bias</p> Signup and view all the answers

    Which dimension of data quality is highlighted by variations in state and country values in Cloud Kicks' contact records?

    <p>Consistency</p> Signup and view all the answers

    What is a significant benefit of using a diverse and large dataset for AI models?

    <p>Improved model accuracy</p> Signup and view all the answers

    What ethical challenge is associated with the transparency of AI decision-making processes?

    <p>Human bias in algorithms</p> Signup and view all the answers

    What is a consequence of bias in AI algorithms within CRM systems?

    <p>Ethical challenges in CRM systems</p> Signup and view all the answers

    What defines ethical debt in the context of AI implementation?

    <p>Launching an AI feature after discovering harmful bias</p> Signup and view all the answers

    Which Salesforce trusted AI principle is being practiced when a consultant conducts workshops to test diverse datasets?

    <p>Inclusivity</p> Signup and view all the answers

    How can a financial institution implement the principle of Transparency in their preapproved credit card campaign?

    <p>Flagging sensitive variables and their proxies</p> Signup and view all the answers

    Which field of AI is most suitable for implementing a chatbot that answers frequently asked questions on a website?

    <p>Natural language processing</p> Signup and view all the answers

    What are the key components of data quality standards?

    <p>Accuracy, completeness, consistency</p> Signup and view all the answers

    What best differentiates predictive AI from generative AI?

    <p>Predictive AI classifies data, while generative AI generates new outputs</p> Signup and view all the answers

    What is one potential outcome of using poor-quality data in AI applications?

    <p>AI models may produce biased or erroneous results</p> Signup and view all the answers

    Study Notes

    Data Quality Dimensions

    • Content quality helps reduce communication inefficiencies.
    • Consistency is affected when different regions use varying formats for product categorization.
    • Reliability is crucial for AI models predicting demand using historical data.
    • Accuracy, completeness, and consistency are key components of data quality standards.

    AI Applications for Business Improvement

    • Lead scoring, opportunity forecasting, and case classification enhance the sales process and customer support.
    • Natural language processing is suitable for chatbots that handle FAQs, easing customer agents' workload.

    Data Quality and AI

    • Poor data quality results in inaccurate AI predictions.
    • High-quality data is essential for unbiased AI decisions, promoting ethical usage and preventing discrimination.
    • Incomplete customer records hinder product recommendation accuracy.

    Ethical Considerations in AI

    • Salesforce’s trusted AI principles ensure the ethical and responsible use of AI in CRM systems.
    • Ethical debt occurs when an AI feature is launched after uncovering harmful biases.
    • Transparency requires flagging sensitive variables to prevent discriminatory practices.

    AI in Customer Experience

    • AI can automate customer service experiences, enabling seamless interactions.
    • Personalization increases customer willingness to share personal information.

    Understanding Salesforce AI Features

    • Einstein Discovery provides insights and recommendations, while Einstein Analytics focuses on interactive dashboards.
    • Einstein Vision handles image recognition, whereas Einstein Prediction focuses on business outcome forecasting.

    Using AI for Fraud Detection

    • Einstein Anomaly Detection identifies unusual data patterns for fraud and security threat detection.

    Preparing Data for AI Implementation

    • Prioritizing data availability is critical for successfully integrating AI into CRM operations.

    Ethical Challenges and Bias in AI

    • Societal bias imposes a system's values on others, potentially leading to ethical challenges in CRM systems.
    • Associations create biases through stereotypes in data labeling, while immutable traits like financial status can lead to discrimination.

    Diversity and Inclusivity in AI Models

    • Testing AI with diverse datasets is essential for inclusive AI development and effective model performance.

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    Quiz Team

    Description

    Test your knowledge on data quality dimensions and AI applications that enhance business processes. This quiz includes questions related to sales, customer support, and data accuracy in predictions. See how well you understand the importance of data in optimizing business strategies.

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