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NEW QUESTION # 86
What is an example of Salesforce's Trusted AI Principle of Inclusivity in practice?
Answer: C
Explanation:
"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."
NEW QUESTION # 87
What are some of the ethical challenges associated with AI development?
Answer: C
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 # 88
Cloud Kicks uses Einstein to generate predictions but is not seeing accurate results. What is a potential reason for this?
Answer: B
Explanation:
AI models rely on high-quality data to produce accurate and reliable predictions. Poor data quality-such as missing values, inconsistent formatting, or biased data-can negatively impact AI performance.
Option A (Incorrect): If Cloud Kicks is using Einstein AI, it is unlikely that they are using the wrong product, as Einstein is designed for predictive analytics. The issue is more likely related to data quality or model training.
Option B (Correct): Poor data quality is one of the most common reasons for inaccurate AI predictions. If the input data contains errors, biases, or incomplete information, the AI model will generate flawed insights.
Regular data cleaning and preprocessing are essential for improving prediction accuracy.
Option C (Incorrect): Having too much data does not necessarily result in inaccurate predictions. In fact, more data can improve model performance if properly structured and cleaned. However, if the data is noisy or unstructured, it may lead to inconsistencies.
NEW QUESTION # 89
What can bias in AI algorithms in CRM lead to?
Answer: B
Explanation:
Explanation
"Bias in AI algorithms in CRM can lead to ethical challenges in CRM systems. Bias means that AI algorithms favor or discriminate certain groups or outcomes based on irrelevant or unfair criteria. Bias can affect the fairness and ethics of CRM systems, as they may affect how customers are perceived, treated, or represented by AI algorithms. For example, bias can lead to ethical challenges in CRM systems if AI algorithms make inaccurate or harmful predictions or recommendations based on customers' identity or characteristics."
NEW QUESTION # 90
A system admin recognizes the need to put a data management strategy in place.
What is a key component of data management strategy?
Answer: B
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 # 91
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