Latest Salesforce Salesforce-AI-Associate First Attempt, Exam real Dumps Updated [Dec-2023]
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NEW QUESTION # 29
An administrator at Cloud Kicks wants to ensure that a field is set up on the customer record so their preferred name can be captured.
Which Salesforce field type should the administrator use to accomplish this?
- A. Text
- B. Rich Text Area
- C. Multi-Select Picklist
Answer: A
Explanation:
Explanation
"A text field type should be used to capture the customer's preferred name. A text field type allows the user to enter any combination of letters, numbers, or symbols. A text field type can be used to store names, addresses, phone numbers, or other personal information."
NEW QUESTION # 30
A developer is tasked with selecting a suitable dataset for training an AI model in Salesforce to accurately predict current customer behavior.
What Is a crucial factor that the developer should consider during selection?
- A. Size of the dataset
- B. Number of variables ipn the dataset
- C. Age of the dataset
Answer: A
Explanation:
Explanation
"The size of the dataset is a crucial factor that the developer should consider during selection. The size of the dataset refers to the amount or volume of data available for training an AI model. The size of the dataset can affect the feasibility and quality of the AI model, as well as the choice of AI techniques and tools. The size of the dataset should be large enough to provide sufficient information for the AI model to learn from and generalize well to new data."
NEW QUESTION # 31
What is a possible outcome of poor data quality?
- A. AI predictions become more focused and less robust.
- B. AI models maintain accuracy but have slower response times.
- C. Biases in data can be inadvertently learned and amplified by AI systems.
Answer: C
Explanation:
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 Poor data quality means that the data is inaccurate, incomplete, inconsistent, irrelevant, or outdated for the AI task.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."
NEW QUESTION # 32
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. Lead scoring, opportunity forecasting, and case classification
- B. Machine learning models and chatbot predictions
- C. Sales data cleansing and customer support data governance
Answer: A
Explanation:
Explanation
"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."
NEW QUESTION # 33
Cloud Kicks relies on data analysis to optimize its product recommendation; however, CK encounters a recurring Issue of Incomplete customer records, with missing contact Information and incomplete purchase histories.
How will this incomplete data quality impact the company's operations?
- A. The accuracy of product recommendations is hindered.
- B. The diversity of product recommendations Is Improved.
- C. The response time for product recommendations is stalled.
Answer: A
Explanation:
Explanation
"The incomplete data quality will impact the company's operations by hindering the accuracy of product recommendations. Incomplete data means that the data is missing some values or attributes that are relevant for the AI task. Incomplete data can affect the performance and reliability of AI models, as they may not have enough information to learn from or make accurate predictions. For example, incomplete customer records can affect the quality of product recommendations, as the AI model may not be able to capture the customers' preferences, behavior, or needs."
NEW QUESTION # 34
How does an organization benefit from using AI to personalize the shopping experience of online customers?
- A. Customers are more likely to visit competitor sites that personalize their experience.
- B. Customers are more likely to share personal information with a site that personalizes their experience.
- C. Customers are more likely to be satisfied with their shopping experience.
Answer: C
Explanation:
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."
NEW QUESTION # 35
What is a key challenge of human AI collaboration in decision-making?
- A. Creates a reliance on AI, potentially leading to less critical thinking and oversight
- B. Reduce the need for human involvement in decision-making processes
- C. Leads to move informed and balanced decision-making
Answer: A
Explanation:
Explanation
"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."
NEW QUESTION # 36
What are the key components of the data quality standard?
- A. Accuracy, Completeness, Consistency
- B. Reviewing, Updating, Archiving
- C. Naming, formatting, Monitoring
Answer: A
Explanation:
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."
NEW QUESTION # 37
What is a key benefit of effective interaction between humans and AI systems?
- A. Alerts humans to the presence of biased data
- B. Reduces the need for human involvement
- C. Leads to more informed and balanced decision making
Answer: C
Explanation:
Explanation
"A key benefit of effective interaction between humans and AI systems is that it leads to more informed and balanced decision making. Effective interaction means that humans and AI systems can communicate and collaborate with each other in a clear, natural, and respectful way. Effective interaction can help leverage the strengths and complement the weaknesses of both humans and AI systems. Effective interaction can also help increase trust, confidence, and satisfaction in using AI systems."
NEW QUESTION # 38
A consultant conducts a series of Consequence Scanning workshops to support testing diverse datasets.
Which Salesforce Trusted AI Principles is being practiced>
- A. Accountability
- B. Inclusivity
- C. Transparency
Answer: B
Explanation:
Explanation
"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."
NEW QUESTION # 39
Which best describes the different between predictive AI and generative AI?
- A. Predictive AI and generative have the same capabilities differ in the type of input they receive:
predictive AI receives raw data whereas generation AI receives natural language. - B. Predictive AI uses machine learning to classes or predict output from its input data whereas generative AI does not use machine learning to generate its output
- C. Predictive new and original output for a given input.
Answer: C
Explanation:
Explanation
"The difference between predictive AI and generative AI is that predictive AI analyzes existing data to make predictions or recommendations based on patterns or trends, while generative AI creates new content based on existing data or inputs. Predictive AI is a type of AI that uses machine learning techniques to learn from existing data and make predictions or recommendations based on the data. For example, predictive AI can be used to forecast sales, revenue, or demand based on historical data and trends. Generative AI is a type of AI that uses machine learning techniques togenerate novel content such as images, text, music, or video based on existing data or inputs. For example, generative AI can be used to create realistic faces, write summaries, compose songs, or produce videos."
NEW QUESTION # 40
Which data does Salesforce automatically exclude from marketing Cloud Einstein engagement model training to mitigate bias and ethic...
- A. Geographic
- B. Geographic
- C. Cryptographic
Answer: B
Explanation:
Explanation
"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."
NEW QUESTION # 41
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. Validation rule
- C. Duplicate matching rule
Answer: B
Explanation:
Explanation
"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."
NEW QUESTION # 42
Why is it critical to consider privacy concerns when dealing with AI and CRM data?
- A. Confirms the data is accessible to all users
- B. Ensures compliance with laws and regulations
- C. Increases the volume of data collected
Answer: B
Explanation:
Explanation
"It is critical to consider privacy concerns when dealing with AI and CRM data because it ensures compliance with laws and regulations. Data privacy is the right of individuals to control how their personal data is collected, used, shared, or stored by others. Data privacy laws and regulations are legal frameworks that define and enforce the rights and obligations of data subjects, data controllers, and data processors regarding personal data. Data privacy laws and regulations vary by country, region, or industry, and may impose different requirements or restrictions on how AI and CRM data can be handled."
NEW QUESTION # 43
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?
- A. Consistency
- B. Accuracy
- C. Completeness
Answer: A
Explanation:
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 # 44
Cloud Kicks is testing a new AI model.
Which approach aligns with Salesforce's Trusted AI Principle of Incluslvity?
- A. Test with diverse and representative datasets appropriate for how the model will be used.
- B. Test only with data from a specific region or demographic to limit the risk of data leaks.
- C. Rely on a development team with uniform backgrounds to assess the potential societal implications of the model.
Answer: A
Explanation:
Explanation
"Testing with diverse and representative datasets appropriate for how the model will be used aligns with Salesforce's Trusted AI Principle of Inclusivity. Inclusivity means that AI systems should be designed and developed with respect for diversity and inclusion of different perspectives, backgrounds, and experiences.
Testing with diverse and representative datasets can help ensure that the models are fair, unbiased, and representative of the target population or domain."
NEW QUESTION # 45
What are some key benefits of AI in improving customer experiences in CRM?
- A. Improves CRM security protocols, safeguarding sensitive customer data from potential breaches and threats
- B. Fully automates the customer service experience, ensuring seamless automated interactions with customers
- C. Streamlines case management by categorizing and tracking customer support cases, identifying topics, and summarizing case resolutions
Answer: C
Explanation:
Explanation
"Streamlining case management by categorizing and tracking customer support cases, identifying topics, and summarizing case resolutions are some key benefits of AI in improving customer experiences in CRM. AI can help automate and optimize various aspects of customer service, such as routing cases to the right agents, providing relevant information or suggestions, and generating reports or insights. AI can also help enhance customer satisfaction and loyalty by reducing wait times, improving response quality, and providing personalized solutions."
NEW QUESTION # 46
To avoid introducing unintended bias to an AI model, which type of data should be omitted?
- A. Transactional
- B. Engagement
- C. Demographic
Answer: C
Explanation:
Explanation
"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 fairness and ethics of AI systems."
NEW QUESTION # 47
Which action should be taken to develop and implement trusted generated AI with Salesforce's safety guideline in mind?
- A. Create guardrails that mitigates toxicity and protect PII
- B. Develop right-sized models to reduce our carbon footprint.
- C. Be transparent when AI has created and automatically delivered content.
Answer: A
Explanation:
Explanation
"Creating guardrails that mitigate toxicity and protect PII is an action that should be taken to develop and implement trusted generative AI with Salesforce's safety guideline in mind. Salesforce's safety guideline is one of the Trusted AI Principles that states that AI systems should be designed and developed with respect for the safety and well-being of humans and the environment. Creating guardrails means implementing measures or mechanisms that can prevent or limit the potential harm or risk caused by AI systems. For example, creating guardrails can help mitigate toxicity by filtering out inappropriate or offensive content generated by AI systems. Creating guardrails can also help protect PII by masking or anonymizing personal or sensitive information generated by AI systems."
NEW QUESTION # 48
A Salesforce administrator creates a new field to capture an order's destination country.
Which field type should they use to ensure data quality?
- A. Text
- B. Picklist
- C. Number
Answer: B
Explanation:
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."
NEW QUESTION # 49
How does a data quality assessment impact business outcome for companies using AI?
- A. Improves the speed of AI recommendations
- B. Accelerates the delivery of new AI solutions
- C. Provides a benchmark for AI predictions
Answer: C
Explanation:
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."
NEW QUESTION # 50
What is a benefit of a diverse, balanced, and large dataset?
- A. Model accuracy
- B. Data privacy
- C. Training time
Answer: A
Explanation:
Explanation
"Model accuracy is a benefit of a diverse, balanced, and large dataset. A diverse dataset can capture a variety of features and patterns that are relevant for the AI task. A balanced dataset can avoid overfitting or underfitting the model to a specific subset of data. A large dataset can provide enough information for the model to learn from and generalize well to new data."
NEW QUESTION # 51
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