Microsoft AI-900 Online Practice Questions and Exam Preparation

Microsoft AI-900 Online Questions & Answers

• Question 211:

  You are using AutoML to train multiple models automatically and want to combine several models to improve prediction accuracy.

  Which ensemble technique can AutoML use?

  A. Vertex
  B. Stack
  C. Regression
  D. Classification
  B. Stack

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  Explanation

  Stacking (stacked ensemble) is a method that combines predictions from multiple trained models to improve performance. Regression and classification describe machine learning task types rather than ensemble techniques.

• Question 212:

  You need to count the number of animals in a photograph.

  Which type of computer vision should you use?

  A. facial detection
  B. image classification
  C. optical character recognition (OCR)
  D. object detection
  D. object detection

• Question 213:

  Which type of machine learning groups similar data points without predefined labels?

  A. classification
  B. regression
  C. clustering
  D. reinforcement learning
  C. clustering

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  Explanation

  Clustering is an unsupervised learning technique that groups similar data points based on patterns and features.

• Question 214:

  HOTSPOT

  For each of the following statements, select Yes if the statement is true. Otherwise, select No.

  NOTE: Each correct selection is worth one point.

  

  

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  Box 1: No

  The validation dataset is different from the test dataset that is held back from the training of the model.

  Box 2: Yes

  A validation dataset is a sample of data that is used to give an estimate of model skill while tuning model's hyperparameters.

  Box 3: No

  The Test Dataset, not the validation set, used for this. The Test Dataset is a sample of data used to provide an unbiased evaluation of a final model fit on the training dataset.

  References:

  https://machinelearningmastery.com/difference-test-validation-datasets/

• Question 215:

  A developer is working in Azure AI Foundry and needs an environment to write and test AI-related code.

  Where can the developer write code?

  A. Code editor
  B. Dataset viewer
  C. Model metrics dashboard
  D. Resource monitor
  A. Code editor

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  Explanation

  Azure AI Foundry provides development tools including a code editor environment where developers can write scripts, integrate APIs, and test AI workflows. Other options are primarily used for viewing data or monitoring models.

• Question 216:

  HOTSPOT

  Select the answer that correctly completes the sentence.

  

  

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  validation. In the Microsoft Azure AI Fundamentals (AI-900) study materials, a key concept in machine learning model development is splitting data into subsets for training, validation, and testing. A randomly extracted subset of data from a dataset is most commonly used for validation - that is, for evaluating the performance of the model during or after training.

  Here's how this process works: Training set - This portion of the dataset is used to train the machine learning model. The model learns patterns, relationships, and parameters from this data. Validation set - This is a randomly selected subset (separate from training data) used to fine-tune model hyperparameters and evaluate how well the model generalizes to unseen data. It helps detect overfitting. when the model performs well on training data but poorly on new data. training and tuning are complete. Test set - A final, untouched dataset used to measure the model's real-world performance after all

  By reserving a random subset for validation, data scientists ensure that the model's performance metrics reflect generalization, not memorization of the training data. Let's review the incorrect options: Algorithms - These are the mathematical frameworks or methods used to build models (e.g., decision trees, neural networks). They are not data subsets. Features - These are input variables (attributes) used by the model, not randomly selected data subsets. Labels - These are target values or outcomes the model predicts; again, not data subsets. Therefore, in alignment with Azure AI-900's machine learning fundamentals, the correct completion is: # "A randomly extracted subset of data from a dataset is commonly used for validation of the model."

• Question 217:

  You have an AI-based loan approval system.

  During testing, you discover that the system has a gender bias.

  Which responsible Al principle does this violate?

  A. accountability
  B. reliability and safety
  C. transparency
  D. fairness
  D. fairness

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  Explanation

  Fairness - AI systems should treat all people fairly

  Incorrect:

  Accountability - People should be accountable for AI systems Reliability & Safety - AI systems should perform reliably and safely Transparency - AI systems should be understandable

  References:

  https://dataninja.medium.com/6-principles-of-responsible-ai-b90f745b73dc

• Question 218:

  HOTSPOT

  For each of the following statements, select Yes if the statement is true. Otherwise, select No.

  NOTE: Each correct selection is worth one point.

  

  

• Question 219:

  You need to make the press releases of your company available in a range of languages.

  Which service should you use?

  A. Translator Text
  B. Text Analytics
  C. Speech
  D. Language Understanding (LUIS)
  A. Translator Text

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  Explanation

  Translator is a cloud-based machine translation service you can use to translate text in near real-time through a simple REST API call. The service uses modern neural machine translation technology and offers statistical machine translation technology. Custom Translator is an extension of Translator, which allows you to build neural translation systems.

  References:

  https://docs.microsoft.com/en-us/azure/cognitive-services/translator/

• Question 220:

  DRAG DROP

  You are designing a system that will generate insurance quotes automatically.

  Match the Microsoft responsible AI principles to the appropriate requirements.

  To answer, drag the appropriate principle from the column on the left to its requirement on the right. Each principle may be used once, more than once, or not at all.

  NOTE: Each correct match is worth one point.

  Select and Place: