AI ASSOCIATE FINAL CURRENT EXAM
Question: 1.
What are the advantages of a diversified, balanced, and huge dataset?
A. Training time
B. Data Privacy
C. Model accuracy: ANSWER C
"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 to 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."
Question: 2.
What are the three most widely utilized AI examples in CRM?
A: Predictive scoring, reporting, image classification
B. Predictive scoring, predicting, and recommendations
C. Einstein Bots, facial recognition, and recommendations - ANSWER B
"Predictive scoring, forecasting, and recommendations are three commonly used
examples of AI in CRM. Predictive scoring can help prioritize leads, opportunities,
and customers based on their likelihood to convert, churn, or buy. Forecasting can
help predict future sales, revenue, or demand based on historical data and trends.
Recommendations can help suggest the best products, services, or actions for each
customer based on their preferences, behavior, and needs."
Question 3
Cloud Kicks aims to improve its business processes by implementing AI into its
CRM. What should the organization do initially to prepare its data for use in AI?
,A. Eliminate skewed data.
B. Determine data availability.
C. Determine the data outputs. ANSWER B
Before implementing AI to optimize business processes, the organization should
evaluate the availability and quality of its data. Data is the fuel for AI, and without
enough and relevant data, it cannot deliver accurate and dependable results. As a
result, the organization should determine what data it has, where it is stored, how it
is accessible, and how it is managed. This will allow the corporation to better
assess the feasibility and breadth of its AI programs.
Question: 4.
A healthcare company uses an algorithm to examine patient data and assist with
medical diagnosis.
What is the key purpose of data quality in this artificial intelligence application?
A. improved the accuracy and reliability of medical predictions and diagnoses.
B. Ensured that AI algorithms are compatible with the system's infrastructure.
C. Reduced requirement for healthcare knowledge in evaluating AI outputs
(ANSWER A)
"Data quality plays a crucial role in enhancing the accuracy and reliability of
medical predictions and diagnoses. Poor data quality can lead to inaccurate or
misleading results, which can have serious consequences for patients' health and
well-being. Therefore, it is important to ensure that the data used for AI
applications in healthcare is accurate, complete, consistent, and relevant."
Question 5
What are some of the ethical issues related to AI development?
,A. Potential for human bias in machine learning algorithms and lack of openness in
AI decision-making processes.
B. The implicit transparency of AI systems allows consumers to easily comprehend
and trust their decisions.
C. AI systems' inherent impartiality eliminates any chance for human bias in
decision-making. - ANSWER A
"One of the ethical challenges linked with AI research is the possibility of human
prejudice.
Machine learning algorithms and the lack of transparency in AI decision-making
processes. Human bias can arise from the data used to train the models, the design
choices made by the developers, or the interpretation of the results by the users. A
lack of transparency can make it difficult to understand how and why AI systems
make certain decisions, affecting trust, accountability, and fairness.
Question: 6.
Cloud Kicks observed several variants of state and country values in contact
records. Which data quality dimension is impacted by this issue?
A. Usage
B. Accuracy.
C. Consistency: ANSWER C
"Consistency is the data quality dimension that is affected by multiple variations of
state and country values in contact records. 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 and processing."
Question 7
, How does natural language processing (NLP) fit into the context of AI capabilities?
A. To clean and prepare data for AI deployment.
B. To interpret and understand programming languages.
C. To comprehend and develop 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: 8.
What is an example of Salesforce's Trusted AI Principle of Inclusivity in action?
A. Testing models using various datasets.
B. Striving for model explainability.
C. Working with human rights specialists.
"An example of Salesforce's Trusted AI Principle of Inclusivity in practice is
testing models with diverse datasets. Inclusivity means that AI systems should be
designed and developed with respect for diversity and inclusion of different
perspectives, backgrounds, and experiences. Testing models with diverse datasets
can help ensure that the models are fair, unbiased, and representative of the target
population or domain." - ANSWER:
Question: 9.
Cloud Kicks wishes to develop a custom service analytics tool for analyzing cases
in Salesforce. To guarantee effective case resolution, the application should use
accurate data.
Question: 1.
What are the advantages of a diversified, balanced, and huge dataset?
A. Training time
B. Data Privacy
C. Model accuracy: ANSWER C
"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 to 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."
Question: 2.
What are the three most widely utilized AI examples in CRM?
A: Predictive scoring, reporting, image classification
B. Predictive scoring, predicting, and recommendations
C. Einstein Bots, facial recognition, and recommendations - ANSWER B
"Predictive scoring, forecasting, and recommendations are three commonly used
examples of AI in CRM. Predictive scoring can help prioritize leads, opportunities,
and customers based on their likelihood to convert, churn, or buy. Forecasting can
help predict future sales, revenue, or demand based on historical data and trends.
Recommendations can help suggest the best products, services, or actions for each
customer based on their preferences, behavior, and needs."
Question 3
Cloud Kicks aims to improve its business processes by implementing AI into its
CRM. What should the organization do initially to prepare its data for use in AI?
,A. Eliminate skewed data.
B. Determine data availability.
C. Determine the data outputs. ANSWER B
Before implementing AI to optimize business processes, the organization should
evaluate the availability and quality of its data. Data is the fuel for AI, and without
enough and relevant data, it cannot deliver accurate and dependable results. As a
result, the organization should determine what data it has, where it is stored, how it
is accessible, and how it is managed. This will allow the corporation to better
assess the feasibility and breadth of its AI programs.
Question: 4.
A healthcare company uses an algorithm to examine patient data and assist with
medical diagnosis.
What is the key purpose of data quality in this artificial intelligence application?
A. improved the accuracy and reliability of medical predictions and diagnoses.
B. Ensured that AI algorithms are compatible with the system's infrastructure.
C. Reduced requirement for healthcare knowledge in evaluating AI outputs
(ANSWER A)
"Data quality plays a crucial role in enhancing the accuracy and reliability of
medical predictions and diagnoses. Poor data quality can lead to inaccurate or
misleading results, which can have serious consequences for patients' health and
well-being. Therefore, it is important to ensure that the data used for AI
applications in healthcare is accurate, complete, consistent, and relevant."
Question 5
What are some of the ethical issues related to AI development?
,A. Potential for human bias in machine learning algorithms and lack of openness in
AI decision-making processes.
B. The implicit transparency of AI systems allows consumers to easily comprehend
and trust their decisions.
C. AI systems' inherent impartiality eliminates any chance for human bias in
decision-making. - ANSWER A
"One of the ethical challenges linked with AI research is the possibility of human
prejudice.
Machine learning algorithms and the lack of transparency in AI decision-making
processes. Human bias can arise from the data used to train the models, the design
choices made by the developers, or the interpretation of the results by the users. A
lack of transparency can make it difficult to understand how and why AI systems
make certain decisions, affecting trust, accountability, and fairness.
Question: 6.
Cloud Kicks observed several variants of state and country values in contact
records. Which data quality dimension is impacted by this issue?
A. Usage
B. Accuracy.
C. Consistency: ANSWER C
"Consistency is the data quality dimension that is affected by multiple variations of
state and country values in contact records. 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 and processing."
Question 7
, How does natural language processing (NLP) fit into the context of AI capabilities?
A. To clean and prepare data for AI deployment.
B. To interpret and understand programming languages.
C. To comprehend and develop 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: 8.
What is an example of Salesforce's Trusted AI Principle of Inclusivity in action?
A. Testing models using various datasets.
B. Striving for model explainability.
C. Working with human rights specialists.
"An example of Salesforce's Trusted AI Principle of Inclusivity in practice is
testing models with diverse datasets. Inclusivity means that AI systems should be
designed and developed with respect for diversity and inclusion of different
perspectives, backgrounds, and experiences. Testing models with diverse datasets
can help ensure that the models are fair, unbiased, and representative of the target
population or domain." - ANSWER:
Question: 9.
Cloud Kicks wishes to develop a custom service analytics tool for analyzing cases
in Salesforce. To guarantee effective case resolution, the application should use
accurate data.
