Top 25 AI-900 Exam Questions and Answers Part-1

Top 25 AI-900 Exam Questions and Answers Part-1. [image] Microsoft CERTIFIED AZURE A1 FUNDAMENTALS.

[Audio] When working on a machine learning project, one important step is to split the data for training and evaluation. This is crucial in order to obtain accurate results from the model. But how exactly should the data be split? Option A suggests using features for training and labels for evaluation. While this may seem logical, it can actually result in biased results and overfitting of the model. Option B, on the other hand, recommends randomly splitting the data into rows for training and rows for evaluation. This approach allows the model to be tested on unseen data, leading to more accurate results and avoiding overfitting. Option C proposes using labels for training and features for evaluation, which goes against the standard practice of using features for training. Lastly, Option D suggests randomly splitting the data into columns for training and columns for evaluation, which may not be suitable for machine learning as it does not take into account the relationship between the features and labels. In conclusion, the correct answer is B, to randomly split the data into rows for training and rows for evaluation, as this ensures that the model is trained and tested on different data sets, resulting in more accurate and unbiased results..

[Audio] We have reached slide number 3 in our presentation on the top 25 (A I ) 900 exam questions and answers. This slide covers the guiding principles for responsible (A-I ) set by Microsoft. As society continues to integrate (A-I ) technology, it is crucial to ensure its development and usage align with ethical and responsible standards. Microsoft has identified three guiding principles for this purpose. The first is inclusiveness, which emphasizes creating (A-I ) systems accessible to all, regardless of background or abilities, with considerations for diversity, equity, and inclusion. The second is fairness, where (A-I ) should not exacerbate biases or discrimination and must treat everyone fairly and without discrimination. Lastly, reliability and safety are essential, with thorough testing and monitoring to detect and address potential risks to individuals and society. Each correct answer on this question is worth one point. To recap, the answers are inclusiveness, fairness, and reliability and safety. Thank you for your attention, and let us continue to the next question in our presentation..

[Audio] Microsoft believes in the guiding principle of transparency when designing an (A-I ) system for loan approval. This means that the factors used in decision-making should be explainable to promote responsible use of (A-I ). Transparency allows the team to understand the data, algorithms, and transformation logic used in training the model and any associated assets. The correct answer to the question "which Microsoft guiding principle for responsible (A-I ) is demonstrated in this scenario?" is A Transparency. Achieving transparency in the decision-making process ensures ethical and responsible use of (A-I ), instills trust and confidence, and promotes accountability. In summary, prioritizing and implementing the guiding principle of transparency is crucial when designing an (A-I ) system, aligning with Microsoft's values and benefiting all. Thank you for your attention..

[Audio] To predict future sales, the most commonly used approach is relying on technology and data. However, with the variety of machine learning techniques available, it can be a challenge to determine the most suitable one. In this specific scenario, the goal is to forecast the number of gift cards that will be sold next month, and regression proves to be the most appropriate choice. Regression is a type of machine learning specifically designed for predicting numerical values. It considers past data and patterns to make accurate predictions about future outcomes, making it the ideal technique for forecasting sales numbers. While other options such as classification and clustering have their own strengths and applications, they are not as suitable for predicting numerical values. Classification is better for grouping data while clustering is useful for identifying patterns and similarities. Additionally, privacy and security are important factors in any data-driven project, but they are not directly relevant in predicting sales numbers. Therefore, they are not the best choices for answering the given question. In conclusion, when it comes to predicting the number of gift cards that will be sold next month, regression is the most suitable type of machine learning to use. Its ability to analyze patterns and make accurate predictions based on past data make it the ideal choice for this task. Moving forward, we can now focus on our next question..

[Audio] The final slide of our presentation explains the main takeaway of using Azure Machine Learning designer to publish an inference pipeline. It is important to note that there are two essential parameters that must be taken into account when accessing the web service. These parameters play a critical role in the success of your deployment and each one is worth one point. Let's quickly review the correct answers. The first parameter is the authentication key, indicated by option C This key is necessary to access the web service and ensure the security of your data. Without it, your deployment will not function properly. The second parameter is the rest endpoint, represented by option D This endpoint is the U-R-L that allows you to interact with your deployed service and make predictions as well as retrieve results from your model. Both of these parameters are crucial for successfully deploying your inference pipeline on Azure Machine Learning designer. Therefore, it is important to keep them in mind and use them accordingly. We would like to express our gratitude for your attention and participation in our presentation on the top 25 (A I ) 900 exam questions and answers. We hope that it has provided valuable insights and information to assist you in acing your exam. Wishing you the best of luck on your journey towards becoming an Azure certified professional..