Question # 1
What is a potential outcome of using poor-quality data in AI application? | A. AI model training becomes slower and less efficient | B. AI models may produce biased or erroneous results. | C. AI models become more interpretable |
B. AI models may produce biased or erroneous results.
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 Poor-quality data means that the data is inaccurate, incomplete, inconsistent, irrelevant, or outdated for the AI task.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.”
Question # 2
How does AI assist in lead qualification? | A. Scores leads based on customer data | B. Creates personalized SMS campaigns | C. Automatically interacts with prospects |
A. Scores leads based on customer data
Explanation: AI assists in lead qualification primarily by scoring leads based on customer data. This process, known as lead scoring, uses machine learning algorithms to evaluate leads against a set of predefined criteria that reflect potential interest and sales readiness. The scores assigned help sales teams prioritize their efforts toward leads most likely to convert, thus improving efficiency and success rates in sales activities. Salesforce AI enhances this process through features like Einstein Lead Scoring, which automatically calculates scores based on both historical conversion data and behavioral data from prospects. For further insights, Salesforce provides detailed documentation on lead scoring with AI at Salesforce Einstein Lead Scoring.
Question # 3
How does AI which CRM help sales representatives better understand previous customer interactions? | A. Creates, localizes, and translates product descriptions | B. Triggers personalized service replies | C. Provides call summaries |
C. Provides call summaries
Explanation: “Providing call summaries is how AI with CRM helps sales representatives better understand previous customer interactions. Call summaries are a feature that uses natural language processing (NLP) to analyze voice conversations between sales representatives and customers and generate summaries or transcripts of the calls. Call summaries can help sales representatives better understand previous customer interactions by providing key information, insights, or action items from the calls.”
Question # 4
Cloud Kicks wants to evaluate the quality of its sales data.
Which first step should they take for the data quality assessment? | A. Plan and align territories, | B. Run a new report or dashboard. | C. Identify business objectives. |
C. Identify business objectives.
Explanation: The first step Cloud Kicks should take for data quality assessment is to identify business objectives. This is crucial because understanding how the company uses customer data to support its business objectives will guide the assessment process1. By identifying the business objectives, Cloud Kicks can determine what customer data is required to support those objectives and how that data is being used. This foundational step is essential before moving on to other aspects of data quality assessment, such as running reports or planning territories. It aligns the data quality initiatives with the company’s goals and ensures that the assessment is focused on areas that will drive business value.
Question # 5
What are the three commonly used examples of AI in CRM? | A. Predictive scoring, reporting, Image classification | B. Predictive scoring, forecasting, recommendations | C. Einstein Bots, face recognition, recommendations |
B. Predictive scoring, forecasting, recommendations
Explanation: “Predictive scoring, forecasting, and recommendations are three commonly used examples of AI in CRM. Predictive scoring can help prioritize leads, opportunities, and customers based on their likelihood to convert, churn, or buy. Forecasting can help predict future sales, revenue, or demand based on historical data and trends. Recommendations can help suggest the best products, services, or actions for each customer based on their preferences, behavior, and needs.”
Question # 6
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.
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.”
Question # 7
What is the key difference between generative and predictive AI?
| A. Generative AI creates new content based on existing data and predictive AI analyzes existing data.
| B. Generative AI finds content similar to existing data and predictive AI analyzes existing data.
| C. Generative AI analyzes existing data and predictive AI creates new content based on existing data.
|
A. Generative AI creates new content based on existing data and predictive AI analyzes existing data.
Explanation:
“The key difference between generative and predictive AI is that generative AI creates new content based on existing data and predictive AI analyzes existing data. Generative AI is a type of AI that can generate novel content such as images, text, music, or video based on existing data or inputs. Predictive AI is a type of AI that can analyze existing data or inputs and make predictions or recommendations based on patterns or trends.”
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FAQs of Salesforce-AI-Associate Exam
What is covered under Einstein AI Basics in the Salesforce-AI-Associate exam?
It includes fundamental concepts of Salesforce Einstein AI, like predictive analytics and machine learning principles.
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Data Preparation involves cleaning and organizing data to make it suitable for processing by Salesforce AI models.
How is Einstein Discovery used in Salesforce?
Einstein Discovery is used for data analysis, uncovering patterns, and making predictive recommendations based on data.
What is the role of Einstein Prediction Builder in Salesforce?
It enables creating custom predictive models that help in making informed business decisions based on data.
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Einstein Language processes natural language for sentiment analysis
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The exam typically consists of multiple-choice questions, covering theoretical and practical aspects of Salesforce AI.
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There are no formal prerequisites, but a basic understanding of Salesforce and AI concepts is recommended.
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