[Q42-Q62] Updated Jul-2024 Test Engine to Practice Test for Salesforce-AI-Associate Exam Questions and Answers!

Salesforce-AI-Associate Salesforce

admin -

July 8, 2024

4.5/5 - (4 votes)

Updated Jul-2024 Test Engine to Practice Test for Salesforce-AI-Associate Exam Questions and Answers!

Salesforce Certified AI Associate Exam Certification Sample Questions and Practice Exam

NEW QUESTION 42
To avoid introducing unintended bias to an AI model, which type of data should be omitted?

Transactional

Engagement

Demographic

“Demographic data should be omitted to avoid introducing unintended bias to an AI model. Demographic data is data that describes the characteristics of a population or a group of people, such as age, gender, race, ethnicity, income, education, or occupation. Demographic data can lead to bias if it is used to discriminate or treat people differently based on their identity or attributes. Demographic data can also reflect existing biases or stereotypes in society or culture, which can affect the fairnessand ethics of AI systems.”

NEW QUESTION 43
Which AI tool is a web of connections, guided by weights and biases?

Neural networks

Predictive Analytics

Rules- based systems

Mark this item for later review,

Neural networks are a key AI tool designed as a web of interconnected nodes, similar to the human brain’s structure. Each connection, or synapse, in a neural network is guided by weights and biases that are adjusted during the learning process. These weights and biases determine the strength and influence of one node over another, facilitating complex pattern recognition and decision-making processes. Neural networks are extensively used in machine learning for tasks like image and speech recognition, among others. For more on neural networks in the context of Salesforce AI, see the Salesforce AI documentation on Neural Networks.

NEW QUESTION 44
A developer has a large amount of data, but it is scattered across different systems and is not standardized.
Which key data quality element should they focus on to ensure the effectiveness of the AI models?

Performance

Consistency

Volume

When data is scattered and not standardized, the key data quality element a developer should focus on is consistency. Consistency refers to the uniformity and standardization of data across different systems, which is crucial for integrating and analyzing data effectively, especially when developing AI models. Inconsistent data can lead to errors in analysis, poor AI model performance, and misleading insights. Salesforce provides tools and practices for ensuring data consistency, such as data integration and management solutions that help standardize and synchronize data across platforms. For more information on Salesforce data management, refer to the Salesforce data management tools at Salesforce Data Management.

NEW QUESTION 45
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 anddecision-making

“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.”

NEW QUESTION 46
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 withembedded guardrails and guidance.

“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 andpreferences 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.”

NEW QUESTION 47
What are the potential consequences of an organization suffering from poor data quality?

Low employee morale, stock devaluation, and inability to attract top talent

Revenue loss, poor customer service, and reputational damage

Technical debt, monolithic system architecture, and slow ETL throughput

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.

NEW QUESTION 48
Which best describes the difference between predictive AI and generative Al?

Predictive AT uses machine learning to classify or predict outputs from its input data whereas generative Al does not use machine learning to generate its output.

Predictive Al uses machine learning to classify or predict outputs from its input data whereas generative Al uses machine learning to generate new and original output for 4 given input

Predictive Al and generative Al have the same capabilities but differ in the type of input they receive; predictive AT receives raw data whereas generative AT receives natural language.

Predictive AI and generative AI represent two different applications of machine learning technologies.
Predictive AI focuses on making predictions based on historical data. It analyzes past data to forecast future outcomes, such as customer churn or sales trends. On the other hand, generative AI is designed to generate new and original outputs based on the learned data patterns. This includes tasks like creating new images, text, or music that resemble the training data but do not duplicate it. Both types of AI use machine learning, but their objectives and outputs are distinct. For detailed differences and applications in a Salesforce context, Salesforce’s guide on AI technologies is a helpful resource, accessible at Salesforce AI Technologies.

NEW QUESTION 49
What is machine learning?

AI that can grow its intelligence

AI that creates new content

A data model used in Salesforce

“A data model is a machine learning feature used in Salesforce. A data model is a representation or abstraction of a real-world phenomenon or process using data structures and algorithms. A data model can be used to describe, analyze, or predict various aspects of the phenomenon or process using machine learning techniques.”

NEW QUESTION 50
A marketing manager wants to use AI to better engage their customers.
Which functionality provides the best solution?

Journey Optimization

Bring Your Own Model

Einstein Engagement

“EinsteinEngagement provides the best solution for a marketing manager who wants to use AI to better engage their customers. Einstein Engagement is a feature that uses AI to optimize email marketing campaigns by providing insights and recommendations on the best time, frequency, content, and subject lines to send emails to each customer. Einstein Engagement can help increase customer engagement, retention, and loyalty by delivering personalized and relevant messages.”

NEW QUESTION 51
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.
Whichdata quality dimension Is essential for this custom application?

Consistency

Duplication

Age

“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.”

NEW QUESTION 52
Cloud kicks wants to develop a solution to predict customers’ interest based on historical data. The company found that employee region uses a text field to capture the product category while employee from all other locations use a picklist.
Which dimension of data quality is affected in this scenario?

Accuracy

Consistency

Completeness

Explanation
“Consistency is the dimension of data quality that is affected in this scenario. Consistency means that the data values are uniform and follow a common standard or format across different records, fields, or sources.
Inconsistent data can cause confusion, errors, or duplication in data analysis andprocessing. For example, using different field types for the same attribute can affect the consistency of the data.”

NEW QUESTION 53
How does data quality impact the trustworthiness of Al-driven decisions?

Low-quality data reduces the risk of overfitting the model, improving the trustworthiness of the predictions.

High-quality data improves the reliability and credibility of Al-driven decisions, fostering trust among users.

The use of both low-quality and high-quality data can improve the accuracy and reliability of AI-driven decisions.

NEW QUESTION 54
What is the role of Salesforce Trust AI principles in the context of CRM system?

Guiding ethical and responsible use of AI

Providing a framework for AI data model accuracy

Outlining the technical specifications for AI integration

“The role of Salesforce Trust AI principles in the context of CRM systems is guiding ethical and responsible use of AI. Salesforce Trust AI principles are a set of guidelines and best practicesfor developing and using AI systems in a responsible and ethical way. The principles include Accountability, Fairness & Equality, Transparency & Explainability, Privacy & Security, Reliability & Safety, Inclusivity & Diversity, Empowerment & Education. The principles aim to ensure that AI systems are aligned with the values and interests of customers, partners, and society.”

NEW QUESTION 55
Cloud Kicks wants to optimize its business operations by incorporating AI into its CRM.
What should the company do first to prepare its data for use with AI?

Remove biased data.

Determine data availability.

Determine data outcomes.

Before using AI to optimize business operations, the company should first assess the availability and quality of its data. Data is thefuel for AI, and without sufficient and relevant data, AI cannot produce accurate and reliable results. Therefore, the company should identify what data it has, where it is stored, how it is accessed, and how it is maintained. This will help the company understand the feasibility and scope of its AI projects.

NEW QUESTION 56
What is the main focus of the Accountability principle in Salesforce’s Trusted AI Principles?

Safeguarding fundamental human rights and protecting sensitive data

Taking responsibility for one’s actions toward customers, partners, and society

Ensuring transparency In Al-driven recommendations and predictions

“The main focus of the Accountability principle in Salesforce’s Trusted AI Principles is taking responsibility for one’s actions toward customers,partners, and society. Accountability means that AI systems should be designed and developed with respect for the impact and consequences of their actions on others.
Accountability also means that AI developers and users should be aware of and adhere to the ethical, legal, and regulatory standards and expectations of their industry and domain.”

NEW QUESTION 57
Cloud Kicks wants to use an AI mode to predict the demand for shoes using historical data on sales and regional characteristics.
What is an essential data quality dimension to achieve this goal?

Reliability

Volume

Age

“Reliability is an essential data quality dimension to achieve the goal of predicting the demand for shoes using historical data on sales and regional characteristics. Reliability means that the data values are trustworthy, credible, and authoritativefor the AI task. Reliable data can improve the accuracy and confidence of AI predictions, as they reflect the true state or condition of the target population or domain. For example, reliable data can help predict the demand for shoes by using verified andvalidated sales and regional data.”

NEW QUESTION 58
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

Data Backup is a key component of a datamanagement 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 damage in 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.

NEW QUESTION 59
What is a Key consideration regarding data quality in AI implementation?

Techniques from customizing AI features in Salesforce

Data’s role in training and fine-tuning Salesforce AI models

Integration process of AI models with Salesforce workflows

Explanation
“Data’s role in training and fine-tuning Salesforce AI models is a key consideration regarding data quality in AI implementation. 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’s role in training and fine-tuning Salesforce AI models means understanding how data is used to build, train, test, and improve AI models in Salesforce, such as Einstein Prediction Builder or Einstein Discovery.”

NEW QUESTION 60
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.

NEW QUESTION 61
Which type of bias results from data being labeled according to stereotypes?

Association

Societal

Interaction

Explanation
“Societal bias results from data being labeled according to stereotypes. Societal bias is a type of bias that reflects the assumptions, norms, or values of a specific society or culture. For example, societal bias can occur when data is labeled based on gender, race, ethnicity, or religion stereotypes.”

NEW QUESTION 62
What are predictive analytics, machine learning, natural language processing (NLP), and computer vision?

Different types of data models used in Salesforce

Different types of automation tools used in Salesforce

Different types of AI that can be applied in Salesforce

Predictive analytics, machine learning, natural language processing (NLP), and computer vision are all types of artificial intelligence technologies that can be applied in Salesforce to enhance various aspects of business operations and customer interactions. Predictive analytics uses historical data to make predictions about future events. Machine learning involves algorithms that can learn from and make decisions based on data.
NLP is concerned with the interactions between computers and humans using natural language, and computer vision interprets and processes visual information from the world to make sense of it in the way humans do.
Salesforce harnesses these AI technologies, particularly through its Einstein platform, to provide powerful tools that help businesses automate tasks, make better decisions, and offer more personalized services. For more on how Salesforce utilizes these AI technologies, you can explore the Einstein AI services documentation at Salesforce Einstein.

Loading …