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AI ASSOCIATE EXAM
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AI ASSOCIATE EXAM 2024-2025What is Machine Learning?
A. AI that can increase its intelligence.
B. AI generates fresh material.
C. Salesforce's data model: ANSWER C
A data model is a machine learning capability in Salesforce. A data model is a
representation or abstraction of a real-world occurrence or process that employs
data structures and algorithms. A data model can be used to define, analyze, or
predict numerous elements of an event or process using machine learning
techniques.
What are the potential consequences of employing low-quality data in an AI
application?
A. AI model training gets slower and less efficient.
B. AI models can give biased or incorrect findings.
C. AI models become more interpretable - answer B.
Poor-quality data in AI applications has the potential to provide biased or incorrect
conclusions. Poor-quality data indicates that it is erroneous, incomplete,
inconsistent, inappropriate, or out of date for the AI task. Poor-quality data can
have an impact on AI model performance and reliability since it may not contain
enough or correct information to learn from or forecast accurately. Poor-quality
data can potentially introduce or worsen biases or errors in AI models, such as
human bias, social prejudice, confirmation bias, or overfitting or underfitting.
Cloud Kicks' data quality specialist wants to make sure that each new contact has at
least an email address.
,What feature should they use to achieve this?
A. Autofill
B. Duplicate Matching Rule
C. Validation rule: ANSWER C
A validation rule should be implemented to ensure that each new contact has at
least one email address or phone number. A validation rule is a feature that verifies
user-entered data for problems before saving it in Salesforce. A validation rule can
help assure data quality by enforcing particular requirements or constraints on the
data values.
What are the main components of the data quality standard?
A. Naming, formatting, and monitoring
B. Accuracy, completeness, and consistency
C. Reviewing, Updating, and Archiving: ANSWER B
Accuracy, completeness, and consistency are critical components of the data
quality standard. A data quality standard is a set of criteria or metrics that define
and assess the quality of data for a given purpose or activity. The criterion for data
quality varies by industry, domain, or application, but some common components
include correctness, completeness, and consistency. Accuracy indicates that the
data values are correct and valid for the data attribute. Completeness means that the
data values contain all important information for the data attribute. Consistency
implies that data values are uniform and adhere to a common standard or format
across various records, fields, or sources.
What are the implications of user permission for AI data privacy?
, A. Artificial intelligence ensures perfect data protection by automatically getting
user consent.
B. AI violates privacy when user consent is not acquired.
C. AI operates regardless of user privacy or consent. ANSWER B
AI violates privacy when user consent is not acquired. User consent is a user's
permission or agreement to enable others to acquire, utilize, share, or store their
personal information. User permission is a crucial feature of data privacy, as it
allows individuals to determine how their personal data is treated by others. AI
violates privacy when user consent is not acquired, as it violates the user's rights
and preferences over their data.
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 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.
What steps should be taken to build and apply trusted produced AI while keeping
Salesforce's safety guidelines in mind?
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