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AI-900: Microsoft Azure AI Fundamentals Practice Test CourseFree Sample Question 1 out of 3:A company employs a team of customer service agents to provide telephone and email support to customers.The company develops a webchat bot to provide automated answers to common customer queries.Which business benefit should the company expect as a result of creating the webchat bot solution?A. increased salesB. a reduced workload for the customer service agents C. improved product reliabilityCorrect Answer: BExplanation:The scenario describes a company implementing a webchat bot to handle common customer queries. The primary purpose of such a bot is to automate responses to frequent questions, thereby reducing the volume of inquiries that human customer service agents need to handle. This directly translates to a reduced workload for the agents.Option A, increased sales, is not a direct or guaranteed outcome of implementing a chatbot. While a well-designed chatbot could indirectly contribute to sales by providing product information or guiding customers through the purchase process, it's not the primary benefit in this context. The scenario focuses on handling existing customer queries, not necessarily driving new sales.Option C, improved product reliability, is unrelated to the implementation of a chatbot. Product reliability is concerned with the consistent performance and quality of the product itself, whereas the chatbot addresses customer support and communication. Therefore, implementing a chatbot would not directly improve product reliability.Free Sample Question 2 out of 3:You have a dataset that contains information about taxi journeys that occurred during a given period.You need to train a model to predict the fare of a taxi journey.What should you use as a feature?A. the number of taxi journeys in the datasetB. the trip distance of individual taxi journeys C. the fare of individual taxi journeysD. the trip ID of individual taxi journeysCorrect Answer: BExplanation:When training a model to predict the fare of a taxi journey, the trip distance is a key feature to consider. The model will learn the relationship between trip distance and fare, allowing it to predict fares for new journeys based on their distances.Here's a breakdown of the answer choices:A. the number of taxi journeys in the dataset: This is not a relevant feature for predicting the fare of an individual journey. It represents the overall number of journeys, not any specific details about a particular trip.B. the trip distance of individual taxi journeys: This is a correct choice. The trip distance is a crucial factor influencing the fare, and the model can learn this relationship to make predictions.C. the fare of individual taxi journeys: This is the target variable that the model is trying to predict. Including the fare itself as a feature would be redundant and would not provide any meaningful information for the model to learn from.D. the trip ID of individual taxi journeys: While the trip ID can uniquely identify a journey, it likely doesn't hold any intrinsic value for predicting the fare. It's not a measurable characteristic that influences the cost of the trip.Free Sample Question 3 out of 3:You have insurance claim reports that are stored as text.You need to extract key terms from the reports to generate summaries.Which type of AI workload should you use?A. natural language processing B. conversational AIC. anomaly detectionD. computer visionCorrect Answer: AExplanation:The task of extracting key terms from text reports to generate summaries falls under Natural Language Processing (NLP). NLP is the branch of AI that deals with understanding and processing human language. Key phrase extraction, a specific NLP technique, is perfectly suited for identifying important terms and concepts within a text document, which can then be used to create summaries.Here's why the other options are not the best fit:Conversational AI: Focuses on building systems that can engage in conversations with humans, such as chatbots. While it uses NLP, it's not the primary workload for simple key term extraction.Anomaly detection: Identifies unusual patterns or outliers in data. This is more relevant for tasks like fraud detection or identifying unusual events, not for summarizing text.Computer vision: Deals with processing and understanding images and videos. It's not applicable to text-based data like insurance claim reports.Therefore, NLP is the most appropriate AI workload for extracting key terms from text reports.Why Choose This Practice Test Course?• Comprehensive Coverage: Test your knowledge across all key areas of the AI-900 exam, including fundamental AI concepts, machine learning principles, and Azure AI services.• Detailed Explanations: Learn as you go with thorough explanations for each question to deepen your understanding and clarify complex topics.• Exam Simulation: Build confidence by experiencing a realistic exam environment and refining your test-taking strategies.• Up-to-Date Content: Stay focused with questions aligned to the latest AI-900 exam format, ensuring you're prepared for the most relevant concepts.Join thousands of learners who have enhanced their AI knowledge and achieved certification success. Start your journey to mastering Azure AI today and ace your AI-900 exam on your first try!

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