Salesforce SALESFORCE-AI-ASSOCIATE Online Practice Questions and Exam Preparation

Salesforce SALESFORCE-AI-ASSOCIATE Online Questions & Answers

• Question 1:

  Which data does Salesforce automatically exclude from marketing Cloud Einstein engagement model training to mitigate bias and ethic...

  A. Geographic
  B. Geographic
  C. Cryptographic
  B. Geographic

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  Explanation/Reference:

  "Demographic data is the data that Salesforce automatically excludes from Marketing Cloud Einstein engagement model training to mitigate bias and ethical concerns. Demographic data is data that describes the characteristics of a population or a group of people, such as age, gender, race, ethnicity, income, education, or occupation. Demographic data can lead to bias if it is used to discriminate or treat people differently based on their identity or attributes. Demographic data can also reflect existing biases or stereotypes in society or culture, which can affect the fairness and ethics of AI systems. Salesforce excludes demographic data from Marketing Cloud Einstein engagement model training to mitigate bias and ethical concerns by ensuring that the models are based on behavioral data rather than personal data."

• Question 2:

  A developer is tasked with selecting a suitable dataset for training an AI model in Salesforce to accurately predict current customer behavior.

  What Is a crucial factor that the developer should consider during selection?

  A. Number of variables ipn the dataset
  B. Size of the dataset
  C. Age of the dataset
  B. Size of the dataset

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  Explanation/Reference:

  "The size of the dataset is a crucial factor that the developer should consider during selection. The size of the dataset refers to the amount or volume of data available for training an AI model. The size of the dataset can affect the feasibility and quality of the AI model, as well as the choice of AI techniques and tools. The size of the dataset should be large enough to provide sufficient information for the AI model to learn from and generalize well to new data."

• Question 3:

  What does the term "data completeness" refer to in the context of data quality?

  A. The degree to which all required data points are present in the dataset
  B. The process of aggregating multiple datasets from various databases
  C. The ability to access data from multiple sources in real time
  A. The degree to which all required data points are present in the dataset

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  Explanation/Reference:

  Data completeness is a measure of data quality that assesses whether all required data points are present in a dataset. It checks for missing values or gaps in data necessary for accurate analysis and decision-making. In the context of Salesforce, ensuring data completeness is crucial for the effectiveness of CRM operations, reporting, and AI-driven applications like Salesforce Einstein, which rely on complete data to function optimally. Salesforce provides various tools and features, such as data validation rules and batch data import processes, that help maintain data completeness across its platform. Detailed guidance on managing data quality in Salesforce can be found in the Salesforce Help documentation on data management at Salesforce Help Data Management.

• Question 4:

  What is the main focus of the Accountability principle in Salesforce's Trusted AI Principles?

  A. Safeguarding fundamental human rights and protecting sensitive data
  B. Taking responsibility for one's actions toward customers, partners, and society
  C. Ensuring transparency In Al-driven recommendations and predictions
  B. Taking responsibility for one's actions toward customers, partners, and society

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  Explanation/Reference:

  "The main focus of the Accountability principle in Salesforce's Trusted AI Principles is taking responsibility for one's actions toward customers, partners, and society. Accountability means that AI systems should be designed and developed with respect for the impact and consequences of their actions on others. Accountability also means that AI developers and users should be aware of and adhere to the ethical, legal, and regulatory standards and expectations of their industry and domain."

• Question 5:

  What is an example of Salesforce's Trusted AI Principle of Inclusivity in practice?

  A. Testing models with diverse datasets
  B. Striving for model explain ability
  C. Working with human rights experts
  A. Testing models with diverse datasets

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  Explanation/Reference:

  "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."

• Question 6:

  Cloud Kicks uses Einstein to generate predictions out is not seeing accurate results?

  What to a potential mason for this?

  A. Poor data quality
  B. The wrong product
  C. Too much data
  A. Poor data quality

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  Explanation/Reference:

  "Poor data quality is a potential reason for not seeing accurate results from an AI model. Poor data quality means that the data is inaccurate, incomplete, inconsistent, irrelevant, or outdated for the AI task. Poor data quality 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."

• Question 7:

  How does AI which CRM help sales representatives better understand previous customer interactions?

  A. Creates, localizes, and translates product descriptions
  B. Triggers personalized service replies
  C. Provides call summaries
  A. Creates, localizes, and translates product descriptions
  C. Provides call summaries

• Question 8:

  Which action introduces bias in the training data used for AI algorithms?

  A. Using a large dataset that is computationally expensive
  B. Using a dataset that represents diverse perspectives and populations
  C. Using a dataset that underrepresents perspectives and populations
  C. Using a dataset that underrepresents perspectives and populations

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  Explanation/Reference:

  Introducing bias in training data for AI algorithms occurs when the dataset used underrepresents certain perspectives and populations. This type of bias can skew AI predictions, making the system less fair and accurate. For example, if a dataset predominantly contains information from one demographic group, the AI's performance may not generalize well to other groups, leading to biased or unfair outcomes. Salesforce discusses the impact of biased training data and ways to mitigate this in their AI ethics guidelines, which can be explored further in the Salesforce AI documentation on Responsible Creation of AI.

• Question 9:

  How does a data quality assessment impact business outcome for companies using AI?

  A. Improves the speed of AI recommendations
  B. Accelerates the delivery of new AI solutions
  C. Provides a benchmark for AI predictions
  C. Provides a benchmark for AI predictions

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  Explanation/Reference:

  "A data quality assessment impacts business outcomes for companies using AI by providing a benchmark for AI predictions. A data quality assessment is a process that measures and evaluates the quality of data for a specific purpose or task. A data quality assessment can help identify and address any issues or gaps in the data quality dimensions, such as accuracy, completeness, consistency, relevance, and timeliness. A data quality assessment can impact business outcomes for companies using AI by providing a benchmark for AI predictions, as it can help ensure that the predictions are based on high-quality data that reflects the true state or condition of the target population or domain."

• Question 10:

  Which AI tool is a web of connections, guided by weights and biases?

  A. Neural networks
  B. Predictive Analytics
  C. Rules- based systems
  D. Mark this item for later review,
  A. Neural networks

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  Explanation/Reference:

  Neural networks are a key AI tool designed as a web of interconnected nodes, similar to the human brain's structure. Each connection, or synapse, in a neural network is guided by weights and biases that are adjusted during the learning process. These weights and biases determine the strength and influence of one node over another, facilitating complex pattern recognition and decision-making processes. Neural networks are extensively used in machine learning for tasks like image and speech recognition, among others. For more on neural networks in the context of Salesforce AI, see the Salesforce AI documentation on Neural Networks.