Salesforce AI Associate Exam Practice Test Instant Access

Question 1

What is one technique to mitigate bias and ensure fairness in AI applications?

AOngoing auditing and monitoring of data that is used in AI applications

BExcluding data features from the Al application to benefit a population

CUsing data that contains more examples of minority groups than majority groups

Answer : A

A technique to mitigate bias and ensure fairness in AI applications is ongoing auditing and monitoring of the data used in AI applications. Regular audits help identify and address any biases that may exist in the data, ensuring that AI models function fairly and without prejudice. Monitoring involves continuously checking the performance of AI systems to safeguard against discriminatory outcomes. Salesforce emphasizes the importance of ethical AI practices, including transparency and fairness, which can be further explored through Salesforce's AI ethics guidelines at Salesforce AI Ethics.

Question 2

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 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 a potential outcome of using poor-quality data in AI application?

AAI model training becomes slower and less efficient

BAI models may produce biased or erroneous results.

CAI models become more interpretable

Answer : B

''A potential outcome of using poor-quality data in AI applications is that AI models may produce biased or erroneous results. Poor-quality data means that the data is inaccurate, incomplete, inconsistent, irrelevant, or outdated for the AI task. Poor-quality data can affect the performance and reliability of AI models, as they may not have enough or correct information to learn from or make accurate predictions. Poor-quality data can also introduce or exacerbate biases or errors in AI models, such as human bias, societal bias, confirmation bias, or overfitting or underfitting.''

Question 5

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 6

Which type of bias imposes a system 's values on others?

ASocietal

BAutomation

CAssociation

Answer : A

''Societal bias is the type of bias that imposes a system's values on others. Societal bias is a type of bias that reflects the assumptions, norms, or values of a specific society or culture. Societal bias can affect the fairness and ethics of AI systems, as they may affect how different groups or domains are perceived, treated, or represented by AI systems. For example, societal bias can occur when AI systems impose a system's values on others, such as using Western standards of beauty or success to judge or rank people from other cultures.''

Question 7

Which best describes the different between predictive AI and generative AI?

APredictive new and original output for a given input.

BPredictive 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.

CPredictive 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

Answer : A

''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.''

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