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Certified AI Associate: Salesforce Certified AI Associate Exam

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Exam Questions:
105
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Last Updated
April - 2025
Language
English
3 Quizzes
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The Certified AI Associate exam is a crucial step for anyone looking to validate their expertise in artificial intelligence. To increase your chances of success, practicing with real exam questions shared by those who have already passed can be incredibly helpful. In this guide, we’ll provide practice test questions and answers, offering insights directly from successful candidates.

Why Use Certified AI Associate Practice Test?

  • Real Exam Experience: Our practice tests accurately mirror the format and difficulty of the actual Certified AI Associate exam, providing you with a realistic preparation experience.
  • Identify Knowledge Gaps: Practicing with these tests helps you pinpoint areas that need more focus, allowing you to study more effectively.
  • Boost Confidence: Regular practice builds confidence and reduces test anxiety.
  • Track Your Progress: Monitor your performance to see improvements and adjust your study plan accordingly.

Key Features of Certified AI Associate Practice Test

  • Up-to-Date Content: Our community regularly updates the questions to reflect the latest exam objectives and industry trends.
  • Detailed Explanations: Each question comes with detailed explanations, helping you understand the correct answers and learn from any mistakes.
  • Comprehensive Coverage: The practice tests cover all key topics of the Certified AI Associate exam, including machine learning, data science, and AI ethics.
  • Customizable Practice: Tailor your study experience by creating practice sessions based on specific topics or difficulty levels.

Exam Details

  • Exam Number: AI Associate
  • Exam Name: Certified AI Associate Exam
  • Length of Test: 90 minutes
  • Exam Format: Multiple-choice and scenario-based questions
  • Exam Language: English
  • Number of Questions in the Actual Exam: 60 questions
  • Passing Score: 70%

Use the member-shared Certified AI Associate Practice Tests to ensure you're fully prepared for your certification exam. Start practicing today and take a significant step towards achieving your certification goals!

Related questions

Cloud Kicks implements a new product recommendation feature for its shoppers that recommends shoes of a given color to display to customers based on the color of the products from their purchase history.

Which type of bias is most likely to be encountered in this scenario?

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What is a potential source of bias in training data for AI models?

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Which features of Einstein enhance sales efficiency and effectiveness?

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Cloud Kicks wants to develop a solution to predict customers product interests based on historical data. The company found that employees from one region use a text field to capture the product category, while employees from all other locations use a plckllst.

Which data quality dimension is affected in this scenario?

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Which Einstein capability uses emails to create content for Knowledge articles?

Generate
Generate
Discover
Discover
Predict
Predict
Suggested answer: A
Explanation:

''Einstein Generate uses emails to create content for Knowledge articles. Einstein Generate is a natural language generation (NLG) feature that can automatically write summaries, descriptions, or recommendations based on data or text inputs. For example, Einstein Generate can analyze email conversations between agents and customers and generate draft articles for the Knowledge base.''

asked 23/09/2024
Himal Rai
47 questions

Why is it critical to consider privacy concerns when dealing with AI and CRM data?

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What is one technique to mitigate bias and ensure fairness in AI applications?

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A sales manager is looking to enhance the quality of lead data in their CRM system.

Which process will most likely help the team accomplish this goal?

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What is a possible outcome of poor data quality?

AI models maintain accuracy but have slower response times.
AI models maintain accuracy but have slower response times.
Biases in data can be inadvertently learned and amplified by AI systems.
Biases in data can be inadvertently learned and amplified by AI systems.
AI predictions become more focused and less robust.
AI predictions become more focused and less robust.
Suggested answer: B
Explanation:

''A possible outcome of poor data quality is that biases in data can be inadvertently learned and amplified by AI systems. 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 systems, as they may not have enough or correct information to learn from or make accurate predictions. Poor data quality can also introduce or exacerbate biases in data, such as human bias, societal bias, or confirmation bias, which can affect the fairness and ethics of AI systems.''

asked 23/09/2024
Mohammed Meddah
42 questions

What Is a benefit of data quality and transparency as it pertains to bias in generated AI?

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