Question # 1
| How does poor data quality affect predictive and generative AI models? | | A. Creates inaccurate results | | B. Increases raw data volume | | C. Decreases storage efficiency |
A. Creates inaccurate results
Explanation: Poor data quality significantly impacts the performance of predictive and generative AI models by leading to inaccurate and unreliable results. Factors such as incomplete data, incorrect data, or poorly formatted data can mislead AI models during the learning phase, causing them to make incorrect assumptions, learn inappropriate patterns, or generalize poorly to new data. This inaccuracy can be detrimental in applications where precision is critical, such as in predictive analytics for sales forecasting or customer behavior analysis. Salesforce emphasizes the importance of data quality for AI model effectiveness in their AI best practices guide, which can be reviewed on Salesforce AI Best Practices.
Question # 2
| What are the potential consequences of an organization suffering from poor data quality? | | A. Low employee morale, stock devaluation, and inability to attract top talent | | B. Revenue loss, poor customer service, and reputational damage | | C. Technical debt, monolithic system architecture, and slow ETL throughput |
B. Revenue loss, poor customer service, and reputational damage
Explanation: The potential consequences of an organization suffering from poor data quality include revenue loss, poor customer service, and reputational damage. Poor data quality can lead to inaccurate analytics and decision-making, impacting customer interactions, marketing strategies, and financial forecasting. These issues ultimately affect customer satisfaction and could lead to financial losses and a damaged brand reputation. Salesforce highlights the importance of maintaining high data quality for effective CRM and AI applications, offering various tools and best practices to enhance data integrity. For guidance on managing and improving data quality in Salesforce, see the Salesforce documentation on data quality at Salesforce Data Quality.
Question # 3
| Which Einstein capability uses emails to create content for Knowledge articles? | | A. Generate | | B. Discover | | C. Predict |
A. Generate
Explanation: “Einstein Generate uses emails to create content for Knowledge articles. Einstein Generate is a natural language generation (NLG) feature that can automatically write summaries, descriptions, or recommendations based on data or text inputs. For example, Einstein Generate can analyze email conversations between agents and customers and generate draft articles for the Knowledge base.”
Question # 4
| A service leader wants use AI to help customer resolve their issues quicker in a guided self-serve application.
Which Einstein functionality provides the best solution? | | A. Case Classification | | B. Bots | | C. Recommendation |
B. Bots
Explanation: “Bots provide the best solution for a service leader who wants to use AI to help customers resolve their issues quicker in a guided self-serve application. Bots are a feature that uses natural language processing (NLP) and natural language understanding (NLU) to create conversational interfaces that can interact with customers using text or voice. Bots can help automate and streamline customer service processes by providing answers, suggestions, or actions based on the customer’s intent and context.”
Question # 5
| How does an organization benefit from using AI to personalize the shopping experience of online customers? | | A. Customers are more likely to share personal information with a site that personalizes their experience. | | B. Customers are more likely to be satisfied with their shopping experience. | | C. Customers are more likely to visit competitor sites that personalize their experience. |
B. Customers are more likely to be satisfied with their shopping experience.
Explanation: “An organization benefits from using AI to personalize the shopping experience of online customers by increasing customer satisfaction. AI can help provide customized and relevant product recommendations, offers, or content based on the customers’ preferences, behavior, or needs. AI can also help create a more engaging and interactive shopping experience by using natural language processing (NLP) or computer vision techniques. Personalized shopping experiences can improve customer satisfaction by meeting their expectations, needs, and interests.”
Question # 6
| 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
Explanation: “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 # 7
| What is a possible outcome of poor data quality? | | A. AI models maintain accuracy but have slower response times. | | B. Biases in data can be inadvertently learned and amplified by AI systems. | | C. AI predictions become more focused and less robust. |
B. Biases in data can be inadvertently learned and amplified by AI systems.
Explanation: “A possible outcome of poor data quality is that biases in data can be inadvertently learned and amplified by AI systems. Poor data quality means that the data is inaccurate, incomplete, inconsistent, irrelevant, or outdated for the AI task. Poor data quality can affect the performance and reliability of AI systems, as they may not have enough or correct information to learn from or make accurate predictions. Poor data quality can also introduce or exacerbate biases in data, such as human bias, societal bias, or confirmation bias, which can affect the fairness and ethics of AI systems.”
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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.
Can you explain Data Preparation for Salesforce AI?
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.
Can you explain Einstein Language and Vision in Salesforce?
Einstein Language processes natural language for sentiment analysis
and intent detection, while Einstein Vision focuses on image
recognition.
What is the format of the Salesforce-AI-Associate exam?
The exam typically consists of multiple-choice questions, covering theoretical and practical aspects of Salesforce AI.
Are there prerequisites for the Salesforce-AI-Associate exam?
There are no formal prerequisites, but a basic understanding of Salesforce and AI concepts is recommended.
How long is the Salesforce-AI-Associate exam?
The exam duration is usually 90 minutes.
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