Salesforce-AI-Associate Dumps 2024 - New Salesforce Salesforce-AI-Associate Exam Questions
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Salesforce Salesforce-AI-Associate Exam Syllabus Topics:
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NEW QUESTION # 29
Cloud Kicks implements a new product recommendation feature for its shoppers that recommends shoes of a given color to display to customers based on the color of the products from their purchase history.
Which type of bias is most likely to be encountered in this scenario?
- A. Confirmation
- B. Societal
- C. Survivorship
Answer: A
Explanation:
Explanation
"Confirmation bias is most likely to be encountered in this scenario. Confirmation bias is a type of bias that occurs when data or information confirms or supports one's existing beliefs or expectations. For example, confirmation bias can occur when a product recommendation feature only recommends shoes of a given color based on the customer's purchase history, without considering other factors or preferences that may influence their choice."
NEW QUESTION # 30
Cloud Kicks relies on data analysis to optimize its product recommendation; however, CK encounters a recurring Issue of Incomplete customer records, withmissing contact Information and incomplete purchase histories.
How will this incomplete data quality impact the company's operations?
- A. The response time for product recommendations is stalled.
- B. The diversity of product recommendations Is Improved.
- C. The accuracy of product recommendations is hindered.
Answer: C
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 # 31
A consultant conducts a series of Consequence Scanning workshops to support testing diverse datasets.
Which Salesforce Trusted AI Principles is being practiced>
- A. Inclusivity
- B. Accountability
- C. Transparency
Answer: A
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 AIPrinciples 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 toidentify 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 # 32
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 # 33
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. Number
- B. Picklist
- C. Text
Answer: B
Explanation:
"A picklist field type should be used to ensure data quality for capturing an order's destinationcountry. 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 # 34
What are predictive analytics, machine learning, natural language processing (NLP), and computer vision?
- A. Different types of automation tools used in Salesforce
- B. Different types of data models used in Salesforce
- C. Different types of AI that can be applied in Salesforce
Answer: C
Explanation:
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.
NEW QUESTION # 35
What are some of the ethical challenges associated with AI development?
- A. Inherent neutrality of AI systems, which eliminates any potential for human bias in decision-making
- B. Potential for human bias in machine learning algorithms and the lack of transparency in AI decision-making processes
- C. Implicit transparency of AI systems, which makes It easy for users to understand and trust their decisions
Answer: B
Explanation:
"Some of the ethical challenges associated with AI development are the potential for human bias in machine learning algorithms and the lack of transparency in AI decision-making processes. Human bias can arise from the data used to train themodels, the design choices made by the developers, or the interpretation of the results by the users. Lack of transparency can make it difficult to understand how and why AI systems make certain decisions, which can affect trust, accountability, and fairness."
NEW QUESTION # 36
What is a benefit of a diverse, balanced, and large dataset?
- A. Model accuracy
- B. Data privacy
- C. Training time
Answer: A
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 orunderfitting 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 # 37
Cloud Kicks wants to use Einstein Prediction Builder to determine a customer's likelihood of buying specific products; however, data quality is a...
How can data quality be assessed quality?
- A. Leverage data quality apps from AppExchange
- B. Build a Data Management Strategy.
- C. Build reports to expire the data quality.
Answer: A
Explanation:
Explanation
"Leveraging data quality apps from AppExchange is how data quality can be assessed. 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. Leveraging data quality apps from AppExchange means using third-party applications or solutions that can help measure, monitor, or improve data quality in Salesforce."
NEW QUESTION # 38
A financial institution plans a campaign for preapproved credit cards?
How should they implement Salesforce's Trusted AI Principle of Transparency?
- A. Communicate how risk factors such as credit score can impact customer eligibility.
- B. Flagsensitive variables and their proxies to prevent discriminatory lending practices.
- C. Incorporate customer feedback into the model's continuous training.
Answer: B
Explanation:
"Flagging sensitive variables and their proxies to prevent discriminatory lending practicesis how they should implement Salesforce's Trusted AI Principle of Transparency. Transparency is one of the Trusted AI Principles that states that AI systems should be designed and developed with respect for clarity and openness in how they work and why they make certain decisions. Transparency also means that AI users should be able to access relevant information and documentation about the AI systems they interact with. Flagging sensitive variables and their proxies means identifying and marking variablesthat can potentially cause discrimination or unfair treatment based on a person's identity or characteristics, such as age, gender, race, income, or credit score. Flagging sensitive variables and their proxies can help implement Transparency by allowing users to understand and evaluate the data used or generated by AI systems."
NEW QUESTION # 39
What is the key difference between generative and predictive AI?
- A. Generative AI analyzes existing data and predictive AI creates new content based on existing data.
- B. Generative AI finds content similar to existing data and predictive AI analyzes existing data.
- C. Generative AI creates new content based on existing data and predictive AI analyzes existing data.
Answer: C
Explanation:
Explanation
"The key difference between generative and predictive AI is that generative AI creates new content based on existing data and predictive AI analyzes existing data. Generative AI is a type of AI that can generate novel content such as images, text, music, or video based on existing data or inputs. Predictive AI is a type of AI that can analyze existing data or inputs and make predictions or recommendations based on patterns or trends."
NEW QUESTION # 40
Cloud Kicks learns of complaints from customers who are receiving too many sales calls and emails.
Which data quality dimension should be assessed to reduce these communication Inefficiencies?
- A. Consent
- B. Usage
- C. Duplication
Answer: C
Explanation:
"Duplication is the data quality dimension that should be assessed to reduce communication inefficiencies.
Duplication means that the data contains multiple copies or instances of the same record or value. Duplication can cause confusion, errors,or waste in data analysis and processing. For example, duplication can lead to communication inefficiencies if customers receive multiple calls or emails from different sources for the same purpose."
NEW QUESTION # 41
Which best describes the different between predictive AI and generative AI?
- A. 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
- B. Predictive AI and generative have the same capabilities differ in the type of input theyreceive: predictive AI receives raw data whereas generation AI receives natural language.
- C. Predictive new and original output for a given input.
Answer: C
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 to generate 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 # 42
Cloud Kicks wants to evaluate its data quality to ensure accurate and up-to-date records.
Which type of records negatively impact data quality?
- A. Complete
- B. Duplicate
- C. Structured
Answer: B
Explanation:
Duplicate records negatively impact data quality by creating inconsistencies and confusion in database management, leading to potential errors in customer relationship management (CRM) systems like Salesforce. Duplicates can skew analytics results, lead to inefficiencies in customer service, and result in redundant marketing efforts. Salesforce offers various tools to identify and merge duplicate records, thereby maintaining high data integrity. More about managing duplicate records in Salesforce and ensuring data quality can be found in Salesforce's documentation on duplicate management at Salesforce Duplicate Management.
NEW QUESTION # 43
What is the significance of explainability of trusted AI systems?
- A. Enhances the security and accuracy of AI models
- B. Increases the complexity of AI models
- C. Describes how Al models make decisions
Answer: C
Explanation:
The significance of the explainability of trusted AI systems is that it describes how AI models make decisions.
Explainability is crucial for building trust and accountability in AI systems, ensuring that users and stakeholders understand the decision-making processes and outcomes generated by AI. This is particularly important in scenarios where AI decisions impact personal or financial status, such as in credit scoring or healthcare diagnostics. Salesforce emphasizes the importance of explainable AI through its ethical AI practices, aiming to make AI systems more transparent and understandable. More details about Salesforce's approach to ethical and explainable AI can be found in Salesforce AI ethics resources at Salesforce AI Ethics.
NEW QUESTION # 44
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:
"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 thefeasibility 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 # 45
What is a sensitive variable that car esc to bias?
- A. Education level
- B. Gender
- C. Country
Answer: B
Explanation:
Explanation
"Gender is a sensitive variable that can lead to bias. A sensitive variable is a variable that can potentially cause discrimination or unfair treatment based on a person's identity or characteristics. For example, gender is a sensitive variable because it can affect how people are perceived, treated, or represented by AI systems."
NEW QUESTION # 46
To avoid introducing unintended bias to an AI model, which type of data should be omitted?
- A. Engagement
- B. Demographic
- C. Transactional
Answer: B
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
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?
- A. Age
- B. Duplication
- C. Consistency
Answer: C
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."
NEW QUESTION # 48
What is the rile of data quality in achieving AI business Objectives?
- A. Data quality is required to create accurate AI data insights.
- B. Data quality is important for maintain Ai data storage limits
- C. Data quality is unnecessary because AI can work with all data types.
Answer: A
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."
NEW QUESTION # 49
How does data quality impact the trustworthiness of Al-driven decisions?
- A. High-quality data improves the reliability and credibility of Al-driven decisions, fostering trust among users.
- B. Low-quality data reduces the risk of overfitting the model, improving the trustworthiness of the predictions.
- C. The use of both low-quality and high-quality data can improve the accuracy and reliability of AI-driven decisions.
Answer: A
Explanation:
"High-quality dataimproves the reliability and credibility of AI-driven decisions, fostering trust among users.
High-quality data means that the data is accurate, complete, consistent, relevant, and timely for the AI task.
High-quality data can improve the performance and reliability of AI systems, as they have enough and correct information to learn from and make accurate predictions. High-quality data can also improve the trustworthiness of AI-driven decisions, as users can have more confidence and satisfaction in using AIsystems."
NEW QUESTION # 50
A system admin recognizes the need to put a data management strategy in place.
What is a key component of data management strategy?
- A. Color Coding
- B. Naming Convention
- C. Data Backup
Answer: C
Explanation:
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 # 51
Which type of AI can enhance customer service agents' email responses by analyzing the written content of previous emails?
- A. Deep learning
- B. Machine learning
- C. Natural language processing
Answer: C
Explanation:
Natural language processing (NLP) is the type of AI that can enhance customer service agents' email responses by analyzing the written content of previous emails. NLP technologies interpret and generate human language, allowing AI systems to understand, respond to, and even anticipate customer needs based on email interactions. This capability helps in crafting more relevant, accurate, and personalized email responses, improving customer service quality. Salesforce utilizes NLP in its Einstein AI platform to augment various customer service functions. More about Salesforce Einstein's NLP capabilities can be found on the Salesforce Einstein page at Salesforce Einstein NLP.
NEW QUESTION # 52
Cloud Kicks wants to use AI to enhance its sales processes and customer support.
Which capacity should they use?
- A. Dashboard of Current Leads and Cases
- B. Sales path and Automaton Case Escalations
- C. Einstein Lead Scoring and Case Classification
Answer: C
Explanation:
"Einstein Lead Scoring and Case Classification are the capabilities that Cloud Kicks should use to enhance its sales processes and customer support. Einstein Lead Scoring and Case Classification are features that use AI to optimize sales and service processes by providing insights and recommendations based on data.
Einstein Lead Scoring can help prioritize leads based on their likelihood to convert, while Einstein Case Classification can help categorize and route cases based on their attributes."
NEW QUESTION # 53
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