Salesforce AI Associate Exam Practice Test Instant Access

Question 1

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

APredictive 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.

BPredictive 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

CPredictive 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.

Answer : B

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.

Question 2

How does poor data quality affect predictive and generative AI models?

ACreates inaccurate results

BIncreases raw data volume

CDecreases storage efficiency

Answer : A

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 3

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

ANeural networks

BPredictive Analytics

CRules- based systems

DMark this item for later review,

Answer : A

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.

Question 4

A sales manager is looking to enhance the quality of lead data in their CRM system.

Which process will most likely help the team accomplish this goal?

ARedesign the lead conversion process,

BReview and update missing lead information.

CPrioritize active leads quarterly.

Answer : B

To enhance the quality of lead data in their CRM system, the most effective process is to review and update missing lead information. This process involves identifying incomplete records and filling in missing details, which can significantly improve the accuracy and usefulness of lead data. Accurate and complete lead information is crucial for effective lead scoring, prioritization, and follow-up, enhancing overall sales performance. Salesforce CRM offers data quality tools and features that assist in regularly reviewing and maintaining the accuracy of lead data. Information on managing lead data quality in Salesforce can be found at Salesforce Lead Management.

Question 5

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

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

BRevenue loss, poor customer service, and reputational damage

CTechnical debt, monolithic system architecture, and slow ETL throughput

Answer : B

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 6

A consultant conducts a series of Consequence Scanning workshops to support testing diverse datasets.

Which Salesforce Trusted AI Principles is being practiced>

ATransparency

BInclusivity

CAccountability

Answer : B

''Conducting a series of Consequence Scanning workshops to support testing diverse datasets is an action that practices Salesforce's Trusted AI Principle of Inclusivity. Inclusivity is one of the Trusted AI Principles that states that AI systems should be designed and developed with respect for diversity and inclusion of different perspectives, backgrounds, and experiences. Conducting Consequence Scanning workshops means engaging with various stakeholders to identify and assess the potential impacts and implications of AI systems on different groups or domains. Conducting Consequence Scanning workshops can help practice Inclusivity by ensuring that diverse datasets are used to test and evaluate AI systems.''

Question 7

What is an example of ethical debt?

AViolating a data privacy law and falling to pay fines

BLaunching an AI feature after discovering a harmful bias

CDelaying an AI product launch to retrain an AI data model

Answer : B

''Launching an AI feature after discovering a harmful bias is an example of ethical debt. Ethical debt is a term that describes the potential harm or risk caused by unethical or irresponsible decisions or actions related to AI systems. Ethical debt can accumulate over time and have negative consequences for users, customers, partners, or society. For example, launching an AI feature after discovering a harmful bias can create ethical debt by exposing users to unfair or inaccurate results that may affect their trust, satisfaction, or well-being.''

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