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What are the key components of the data quality standard?
''Accuracy, Completeness, Consistency are the key components of the data quality standard. Data quality standard is a set of criteria or measures that define and evaluate the quality of data for a specific purpose or task. Data quality standard can vary by industry, domain, or application, but some common components are accuracy, completeness, and consistency. Accuracy means that the data values are correct and valid for the data attribute. Completeness means that the data values are not missing any relevant information for the data attribute. Consistency means that the data values are uniform and follow a common standard or format across different records, fields, or sources.''
What is an example of ethical debt?
''Launching an AI feature after discovering a harmful bias is an example of ethical debt. Ethical debt is a term that describes the potential harm or risk caused by unethical or irresponsible decisions or actions related to AI systems. Ethical debt can accumulate over time and have negative consequences for users, customers, partners, or society. For example, launching an AI feature after discovering a harmful bias can create ethical debt by exposing users to unfair or inaccurate results that may affect their trust, satisfaction, or well-being.''
How does a data quality assessment impact business outcome for companies using AI?
''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.''
Cloud kicks wants to decrease the workload for its customer care agents by implementing a chatbot on its website that partially deflects incoming cases by answering frequency asked questions
Which field of AI is most suitable for this scenario?
''Natural language processing is the field of AI that is most suitable for this scenario. Natural language processing (NLP) is a branch of AI that enables computers to understand and generate natural language, such as speech or text. NLP can be used to create conversational interfaces that can interact with users using natural language, such as chatbots. Chatbots can help automate and streamline customer service processes by providing answers, suggestions, or actions based on the user's intent and context.''
A business analyst (BA) is preparing a new use case for Al. They run a report to check for null values in the attributes they plan to use.
Which data quality component Is the BA verifying by checking for null values?
By checking for null values, a business analyst (BA) is verifying the data quality component of completeness. Completeness refers to the absence of missing values or gaps in the data, which is essential for the accuracy and reliability of reports and analytics used in AI models. Null values can indicate incomplete data, which may adversely affect the performance of AI applications by leading to incorrect predictions or insights. Salesforce emphasizes the importance of data completeness for effective data analysis and provides tools for data quality assessment and improvement. Details on handling data completeness in Salesforce can be explored at Salesforce Help Data Management.
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Exam domains verified against: Official Salesforce Salesforce-AI-Associate exam guide, last checked September 2026.
Grasp the core principles of how AI operates within Salesforce platforms and systems. Learn to distinguish between machine learning, generative AI, and other AI types and recognize what each can and cannot do in a business context.
Explore the specific AI features built into Salesforce CRM systems and understand how they solve real customer relationship challenges. Examine the practical benefits that AI delivers to sales, service, marketing and other CRM operations.
Identify the ethical risks of AI deployment including bias in training data, lack of transparency in decision-making, and accountability gaps. Apply Salesforce's Trusted AI Principles framework to analyze scenarios and recommend responsible AI practices.
Sample question from this domain above: Q3
Recognize why data quality directly affects AI model accuracy and business outcomes. Understand the key components of data quality such as completeness, accuracy, consistency and timeliness.
Common questions about the exam itself
