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Salesforce Salesforce-AI-Associate Exam Syllabus Topics:

Topic	Details
Topic 1	AI Capabilities in CRM Ethical Considerations of AI AI Fundamentals
Topic 2	Identify CRM AI capabilities Differentiate between the types of AI and their capabilities
Topic 3	Apply Salesforce’s Trusted AI Principles to given scenarios Describe the benefits of AI as they apply to CRM
Topic 4	Describe the ethical challenges of AI Describe the elements components of data quality
Topic 5	Describe the importance of data quality Explain the basic principles and applications of AI within Salesforce

NO.44 What can bias in AI algorithms in CRM lead to?

Personalization and target marketing changes

Advertising cost increases

Ethical challenges in CRM systems

Explanation
“Bias in AI algorithms in CRM can lead to ethical challenges in CRM systems. Bias means that AI algorithms favor or discriminate certain groups or outcomes based on irrelevant or unfair criteria. Bias can affect the fairness and ethics of CRM systems, as they may affect how customers are perceived, treated, or represented by AI algorithms. For example, bias can lead to ethical challenges in CRM systems if AI algorithms make inaccurate or harmful predictions or recommendations based on customers’ identity or characteristics.”

NO.45 How does an organization benefit from using AI to personalize the shopping experience of online customers?

Customers are more likely to share personal information with a site that personalizes their experience.

Customers are more likely to be satisfied with their shopping experience.

Customers are more likely to visit competitor sites that personalize their 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.”

NO.46 In the context of Salesforce’s Trusted AI Principles what does the principle of Empowerment primarily aim to achieve?

Empower users to off all skill level to build AI application with clicks, not code.

Empower users to contribute to the growing body of knowledge of leading AI research.

Empower users to solve challenging technical problems using neural networks.

Explanation
“The principle of Empowerment primarily aims to achieve empowering users of all skill levels to build AI applications with clicks, not code. Empowerment is one of the Trusted AI Principles that states that AI systems should be designed and developed with respect for the empowerment and education of humans. Empowering users means enabling users to access, use, and benefit from AI systems regardless of their technical expertise or background. For example, empowering users means providing tools and platforms that allow users to build AI applications with clicks, not code, such as Einstein Prediction Builder or Einstein Discovery.”

NO.47 Which features of Einstein enhance sales efficiency and effectiveness?

Opportunity List View, Lead List View, Account List view

Opportunity Scoring, Opportunity List View, Opportunity Dashboard

Opportunity Scoring, Lead Scoring, Account Insights

Explanation
“Opportunity Scoring, Lead Scoring, Account Insights are features of Einstein that enhance sales efficiency and effectiveness. Opportunity Scoring and Lead Scoring use predictive models to assign scores to opportunities and leads based on their likelihood to close or convert. Account Insights use natural language processing (NLP) to provide relevant news and insights about accounts based on their industry, location, or events.”

NO.48 What is the rile of data quality in achieving AI business Objectives?

Data quality is unnecessary because AI can work with all data types.

Data quality is required to create accurate AI data insights.

Data quality is important for maintain Ai data storage limits

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.”

NO.49 What is a key characteristic of machine learning in the context of AI capabilities?

Uses algorithms to learn from data and make decisions

Relies on preprogrammed rules to make decisions

Can perfectly mimic human intelligence and decision-making

Explanation
“Machine learning is a key characteristic of AI capabilities that uses algorithms to learn from data and make decisions. Machine learning is a branch of AI that enables computers to learn from data without being explicitly programmed. Machine learning algorithms can analyze data, identify patterns, and make predictions or recommendations based on the data.”

NO.50 A sales manager wants to improve their processes using AI in Salesforce?
Which application of AI would be most beneficial?

Lead soring and opportunity forecasting

Sales dashboards and reporting

Data modeling and management

Explanation
“Lead scoring and opportunity forecasting are applications of AI that would be most beneficial for a sales manager who wants to improve their processes using AI in Salesforce. Lead scoring can help prioritize leads based on their likelihood to convert, while opportunity forecasting can help predict future sales or revenue based on historical data and trends. These applications of AI can help optimize sales processes by providing insights and recommendations that can increase sales efficiency and effectiveness.”

NO.51 Cloud Kicks uses Einstein to generate predictions out is not seeing accurate results?
What to a potential mason for this?

Poor data quality

The wrong product

Too much data

Explanation
“Poor data quality is a potential reason for not seeing accurate results from an AI model. 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 models, as they may not have enough or correct information to learn from or make accurate predictions.”

NO.52 A Salesforce administrator creates a new field to capture an order’s destination country.
Which field type should they use to ensure data quality?

Text

Picklist

Number

Explanation
“A picklist field type should be used to ensure data quality for capturing an order’s destination country. A picklist field type allows the user to select one or more predefined values from a list. A picklist field type can ensure data quality by enforcing consistency, accuracy, and completeness of the data values.”

NO.53 A system admin recognizes the need to put a data management strategy in place.
What is a key component of data management strategy?

Naming Convention

Data Backup

Color Coding

Explanation
Data Backup is a key component of a data management strategy. A data backup is a process of creating and storing copies of data in a separate location or device to prevent data loss or damagein case of a disaster, accident, or malicious attack. A data backup can help ensure data availability, reliability, and security by allowing data to be restored or recovered in the event of a data breach, corruption, or deletion. A data management strategy should include a data backup plan that defines the frequency, scope, method, and location of data backups, as well as the roles and responsibilities of the data backup team.

NO.54 A healthcare company implements an algorithm to analyze patient data and assist in medical diagnosis.
Which primary role does data Quality play In this AI application?

Enhanced accuracy and reliability of medical predictions and diagnoses

Ensured compatibility of AI algorithms with the system’s Infrastructure

Reduced need for healthcare expertise in interpreting AI outouts

Explanation
“Data quality plays a crucial role in enhancing the accuracy and reliability of medical predictions and diagnoses. Poor data quality can lead to inaccurate or misleading results, which can have serious consequences for patients’ health and well-being. Therefore, it is important to ensure that the data used for AI applications in healthcare is accurate, complete, consistent, and relevant.”

NO.55 Cloud Kicks wants to create a custom service analytics application to analyze cases in Salesforce. The application should rely on accurate data to ensure efficient case resolution.
Which data quality dimension Is essential for this custom application?

Consistency

Duplication

Age

Explanation
“Consistency is the data quality dimension that is essential for creating a custom service analytics application to analyze cases in Salesforce. Consistency means that the data values are uniform and follow a common standard or format across different records, fields, or sources. Consistent data can ensure that the custom application can accurately and efficiently analyze cases and provide meaningful insights.”

NO.56 How does a data quality assessment impact business outcome for companies using AI?

Improves the speed of AI recommendations

Accelerates the delivery of new AI solutions

Provides a benchmark for AI predictions

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.”

NO.57 How is natural language processing (NLP) used in the context of AI capabilities?

To cleanse and prepare data for AI implementations

To interpret and understand programming language

To understand and generate human language

Explanation
“Natural language processing (NLP) is used in the context of AI capabilities to understand and generate human language. NLP can enable AI systems to interact with humans using natural language, such as speech or text. NLP can also enable AI systems to analyze and extract information from natural language data, such as documents, emails, or social media posts.”

NO.58 Which statement exemplifies Salesforces honesty guideline when training AI models?

Minimize the AI models carbon footprint and environment impact during training.

Ensure appropriate consent and transparency when using AI-generated responses.

Control bias, toxicity, and harmful content with embedded guardrails and guidance.

Explanation
“Ensuring appropriate consent and transparency when using AI-generated responses is a statement that exemplifies Salesforce’s honesty guideline when training AI models. Salesforce’s honesty guideline is one of the Trusted AI Principles that states that AI systems should be designed and developed with respect for honesty and integrity in how they work and what they produce. Ensuring appropriate consent and transparency means respecting and honoring the choices and preferences of users regarding how their data is used or generated by AI systems. Ensuring appropriate consent and transparency also means providing clear and accurate information and documentation about the AI systems and their outputs.”

NO.59 What is an example of Salesforce’s Trusted AI Principle of Inclusivity in practice?

Testing models with diverse datasets

Striving for model explain ability

Working with human rights experts

Explanation
“An example of Salesforce’s Trusted AI Principle of Inclusivity in practice is testing models with diverse datasets. Inclusivity means that AI systems should be designed and developed with respect for diversity and inclusion of different perspectives, backgrounds, and experiences. Testing modelswith diverse datasets can help ensure that the models are fair, unbiased, and representative of the target population or domain.”

NO.60 What are the key components of the data quality standard?

Reviewing, Updating, Archiving

Naming, formatting, Monitoring

Accuracy, Completeness, Consistency

Explanation
“Accuracy, Completeness, Consistency are the key components of the data quality standard. Data quality standard is a set of criteria or measures that define and evaluate the quality of data for a specific purpose or task. Data quality standard can vary by industry, domain, or application, but some common components are accuracy, completeness, and consistency. Accuracy means that the data values are correct and valid for the data attribute. Completeness means that the data values are not missing any relevant information for the data attribute. Consistency means that the data values are uniform and follow a common standard or format across different records, fields, or sources.”

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