Salesforce Salesforce Certifications SALESFORCE-AI-ASSOCIATE Questions & Answers

• Question 31:

  A system admin recognizes the need to put a data management strategy in place.

  What is a key component of data management strategy?

  A. Naming Convention

  B. Data Backup

  C. Color Coding

  Correct Answer: B

  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 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.

• Question 32:

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

  A. Neural networks

  B. Predictive Analytics

  C. Rules- based systems

  D. Mark this item for later review,

  Correct 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 33:

  A business analyst (BA) wants to improve business by enhancing their sales processes and customer..

  Which AI application should the BA use to meet their needs?

  A. Sales data cleansing and customer support data governance

  B. Machine learning models and chatbot predictions

  C. Lead scoring, opportunity forecasting, and case classification

  Correct Answer: C

  "Lead scoring, opportunity forecasting, and case classification are AI applications that can help a business analyst improve their sales processes and customer support. Lead scoring can help prioritize leads based on their likelihood to convert, opportunity forecasting can help predict future sales or revenue based on historical data and trends, and case classification can help categorize and route cases based on their attributes."

• Question 34:

  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

  Correct 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 35:

  Which data does Salesforce automatically exclude from marketing Cloud Einstein engagement model training to mitigate bias and ethic...

  A. Geographic

  B. Geographic

  C. Cryptographic

  Correct Answer: B

  "Demographic data is the data that Salesforce automatically excludes from Marketing Cloud Einstein engagement model training to mitigate bias and ethical concerns. 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 fairness and ethics of AI systems. Salesforce excludes demographic data from Marketing Cloud Einstein engagement model training to mitigate bias and ethical concerns by ensuring that the models are based on behavioral data rather than personal data."

• Question 36:

  What is a key challenge of human AI collaboration in decision-making?

  A. Leads to move informed and balanced decision-making

  B. Creates a reliance on AI, potentially leading to less critical thinking and oversight

  C. Reduce the need for human involvement in decision-making processes

  Correct Answer: B

  "A key challenge of human-AI collaboration in decision-making is that it creates a reliance on AI, potentially leading to less critical thinking and oversight. Human-AI collaboration is a process that involves humans and AI systems working together to achieve a common goal or task. Human-AI collaboration can have many benefits, such as leveraging the strengths and complementing the weaknesses of both humans and AI systems. However, human-AI collaboration can also pose some challenges, such as creating a reliance on AI, potentially leading to less critical thinking and oversight. For example, human-AI collaboration can create a reliance on AI if humans blindly trust or follow the AI recommendations without questioning or verifying their validity or rationale."

• Question 37:

  A data quality expert at Cloud Kicks want to ensure that each new contact contains at least an email address ...

  Which feature should they use to accomplish this?

  A. Autofill

  B. Duplicate matching rule

  C. Validation rule

  Correct Answer: C

  "A validation rule should be used to ensure that each new contact contains at least an email address or phone number. A validation rule is a feature that checks the data entered by users for errors before saving it to Salesforce. A validation rule can help ensure data quality by enforcing certain criteria or conditions for the data values."

• Question 38:

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

  A. Techniques from customizing AI features in Salesforce

  B. Data's role in training and fine-tuning Salesforce AI models

  C. Integration process of AI models with Salesforce workflows

  Correct Answer: B

  "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."

• Question 39:

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

  A. Association

  B. Societal

  C. Interaction

  Correct Answer: B

  "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."

• Question 40:

  What Is a benefit of data quality and transparency as it pertains to bias in generated AI?

  A. Chances of bIas and mitigated

  B. Chances of bias are aggravated

  C. Chances of bias are remove

  Correct Answer: A

  A benefit of data quality and transparency as it pertains to bias in generated AI is that the chances of bias are mitigated. High data quality ensures that AI models are trained on accurate and representative data, reducing the risk of biased outcomes. Transparency in AI processes helps stakeholders understand how decisions are made, allowing for the identification and correction of potential biases. Together, these practices contribute to the development of fairer and more accountable AI systems. Salesforce highlights the importance of these principles in its AI practices, particularly through its ethical AI framework, which advocates for fairness and accountability. More on Salesforce's commitment to promoting unbiased AI can be found in their AI ethics guidelines at Salesforce AI Ethics.