Salesforce AI Associate Exam Practice Test Instant Access

Question 1

A financial institution plans a campaign for preapproved credit cards?

How should they implement Salesforce's Trusted AI Principle of Transparency?

ACommunicate how risk factors such as credit score can impact customer eligibility.

BFlag sensitive variables and their proxies to prevent discriminatory lending practices.

CIncorporate customer feedback into the model's continuous training.

Answer : B

''Flagging sensitive variables and their proxies to prevent discriminatory lending practices is 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 variables that 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.''

Question 2

How does the "right of least privilege" reduce the risk of handling sensitive personal data?

ABy limiting how many people have access to data

BBy reducing how many attributes are collected

CBy applying data retention policies

Answer : A

''The ''right of least privilege'' reduces the risk of handling sensitive personal data by limiting how many people have access to data. The ''right of least privilege'' is a security principle that states that each user or system should have the minimum level of access or privilege necessary to perform their tasks or functions. The ''right of least privilege'' can help protect sensitive personal data from unauthorized access, misuse, or leakage.''

Question 3

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 4

What is the rile of data quality in achieving AI business Objectives?

AData quality is unnecessary because AI can work with all data types.

BData quality is required to create accurate AI data insights.

CData quality is important for maintain Ai data storage limits

Answer : B

''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.''

Question 5

What is a key benefit of effective interaction between humans and AI systems?

ALeads to more informed and balanced decision making

BAlerts humans to the presence of biased data

CReduces the need for human involvement

Answer : A

''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.''

Question 6

How is natural language processing (NLP) used in the context of AI capabilities?

ATo cleanse and prepare data for AI implementations

BTo interpret and understand programming language

CTo understand and generate human language

Answer : C

''Natural language processing (NLP) is used in the context of AI capabilities to understand and generate human language. NLP can enable AI systems to interact with humans using natural language, such as speech or text. NLP can also enable AI systems to analyze and extract information from natural language data, such as documents, emails, or social media posts.''

Question 7

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

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

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

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

Answer : B

''High-quality data improves 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 AI systems.''

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