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Microsoft AI-900 Online Practice Exam Questions
The questions of AI-900 were last updated on Apr 24,2024 .
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HOTSPOT To complete the sentence, select the appropriate option in the answer area.
Explanation: In the most basic sense, regression refers to prediction of a numeric target. Example: Regression Model: A Boosted Decision Tree algorithm was used to create and train the model for predicting the repayment rate.
You need to predict the income range of a given customer by using the following dataset. Which two fields should you use as features? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.
Explanation: First Name, Last Name, Age and Education Level are features. Income range is a label (what you want to predict). First Name and Last Name are irrelevant in that they have no bearing on income. Age and Education level are the features you should use.
You need to create a training dataset and validation dataset from an existing dataset. Which module in the Azure Machine Learning designer should you use?
Explanation: A common way of evaluating a model is to divide the data into a training and test set by using Split Data, and then validate the model on the training data. Use the Split Data module to divide a dataset into two distinct sets. The studio currently supports training/validation data splits Reference: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-configure-cross-validation-data-splits2
You build a machine learning model by using the automated machine learning user interface (UI). You need to ensure that the model meets the Microsoft transparency principle for responsible AI. What should you do?
Explanation: Model Explain Ability. Most businesses run on trust and being able to open the ML “black box” helps build transparency and trust. In heavily regulated industries like healthcare and banking, it is critical to comply with regulations and best practices. One key aspect of this is understanding the relationship between input variables (features) and model output. Knowing both the magnitude and direction of the impact each feature (feature importance) has on the predicted value helps better understand and explain the model. With model explain ability, we enable you to understand feature importance as part of automated ML runs. Reference: https://azure.microsoft.com/en-us/blog/new-automated-machine-learning-capabilities-in-azure-machine-learning-service/
You run a charity event that involves posting photos of people wearing sunglasses on Twitter. You need to ensure that you only retweet photos that meet the following requirements: Include one or more faces. Contain at least one person wearing sunglasses. What should you use to analyze the images?
Explanation: Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/face/overview
Exam Code: AI-900Q & A: 229 Q&AsUpdated: Apr 24,2024
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