Microsoft AI-900 Online Practice Questions and Exam Preparation

Microsoft AI-900 Online Questions & Answers

• Question 291:

  HOTSPOT

  To complete the sentence, select the appropriate option in the answer area.

  

  

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  References:

  https://docs.microsoft.com/en-us/azure/machine-learning/concept-designer

• Question 292:

  You need to create a clustering model and evaluate the model by using Azure Machine Learning designer.

  What should you do?

  A. Split the original dataset into a dataset for training and a dataset for testing. Use the testing dataset for evaluation.
  B. Use the original dataset for training and evaluation.
  C. Split the original dataset into a dataset for features and a dataset for labels. Use the features dataset for evaluation.
  D. Split the original dataset into a dataset for training and a dataset for testing. Use the training dataset for evaluation.
  A. Split the original dataset into a dataset for training and a dataset for testing. Use the testing dataset for evaluation.

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  Explanation

  Understand steps for clustering

  You can think of the steps to train and evaluate a clustering machine learning model as:

  1. Prepare data: Identify the features and label in a dataset. Pre-process, or clean and transform, the data as needed.

  2. Train model: Split the data into two groups, a training and a validation set. Train a machine learning model using the training data set. Test the machine learning model for performance using the validation data set.

  3. Evaluate performance.

  4. Deploy a predictive service: After you train a machine learning model, you need to convert the training pipeline into a real-time inference pipeline. Then you can deploy the model as an application on a server or device so that others can use it.

  References:

  https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/train-clustering-model

• Question 293:

  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 294:

  A smart device that responds to the question "What is the stock price of Contoso. Ltd.?"

  is an example of which AI workload?

  A. computer vision
  B. anomaly detection
  C. knowledge mining
  D. natural language processing
  D. natural language processing

• Question 295:

  Extracting relationships between data from large volumes of unstructured data is an example of which type of Al workload?

  A. computer vision
  B. knowledge mining
  C. natural language processing (NLP)
  D. anomaly detection
  B. knowledge mining

• Question 296:

  DRAG DROP

  Match the types of computer vision to the appropriate scenarios.

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

  NOTE: Each correct selection is worth one point.

  Select and Place:

  

  

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  Box 1: Facial recognition

  Face detection that perceives faces and attributes in an image; person identification that matches an individual in your private repository of up to 1 million people; perceived emotion recognition that detects a range of facial expressions like happiness, contempt, neutrality, and fear; and recognition and grouping of similar faces in images.

  Box 2: OCR

  Box 3: Objection detection

  Object detection is similar to tagging, but the API returns the bounding box coordinates (in pixels) for each object found. For example, if an image contains a dog, cat and person, the Detect operation will list those objects together with their coordinates in the image. You can use this functionality to process the relationships between the objects in an image. It also lets you determine whether there are multiple instances of the same tag in an image.

  The Detect API applies tags based on the objects or living things identified in the image. There is currently no formal relationship between the tagging taxonomy and the object detection taxonomy. At a conceptual level, the Detect API only finds objects and living things, while the Tag API can also include contextual terms like "indoor", which can't be localized with bounding boxes.

  References:

  https://azure.microsoft.com/en-us/services/cognitive-services/face/https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-object-detection

• Question 297:

  You are building a chatbot that must answer questions using a knowledge base of FAQs.

  Which service should you use?

  A. Translator
  B. Text Analytics
  C. QnA Maker
  D. Speech
  C. QnA Maker

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  Explanation

  QnA Maker enables the creation of a knowledge base from structured and semi-structured data such as FAQs, allowing bots to answer user queries automatically.

• Question 298:

  You need to build an app that will read recipe instructions aloud to support users who have reduced vision.

  Which version service should you use?

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

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  Explanation

  References:

  https://azure.microsoft.com/en-us/services/cognitive-services/text-to-speech/#features

• Question 299:

  HOTSPOT

  Select the answer that correctly completes the sentence.

  

  

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  Fairness is a core ethical principle that all humans aim to understand and apply. This principle is even more important when AI systems are being developed. Key checks and balances need to make sure that the system's decisions don't discriminate or run a gender, race, sexual orientation, or religion bias toward a group or individual.

  According to the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module "Identify guiding principles for responsible AI," Fairness is one of Microsoft's six core principles of Responsible AI. The principle of fairness ensures that AI systems treat all individuals and groups equitably, and that the models do not produce biased or discriminatory outcomes. Bias in AI systems can occur when training data reflects existing prejudices, inequalities, or imbalances. For example, if a dataset used for a hiring model underrepresents a certain demographic group, the AI system might produce unfair recommendations. Microsoft emphasizes that AI should not reflect or reinforce bias and that developers must actively design, test, and monitor models to mitigate unfairness. Microsoft's

  Six Responsible AI Principles: Fairness - AI systems should treat everyone equally and avoid bias. Reliability and safety - AI systems must operate as intended even under unexpected conditions. Privacy and security - AI must protect personal and business data. Inclusiveness - AI should empower all people and be accessible to diverse users. Transparency - AI systems should be understandable and their decisions explainable. Accountability - Humans should be accountable for AI system outcomes. The other options do not fit this context: Accountability ensures human responsibility for AI decisions. Inclusiveness focuses on accessibility and empowering all users. Transparency relates to making AI systems understandable. Therefore, the correct answer is fairness, as it directly addresses the principle that AI systems should NOT reflect biases from the datasets used to train

  References:

  https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai

• Question 300:

  DRAG DROP

  You need to use Azure Machine Learning designer to build a model that will predict automobile prices.

  Which type of modules should you use to complete the model? To answer, drag the appropriate modules to the correct locations. Each module may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.

  NOTE: Each correct selection is worth one point.

  Select and Place:

  

  

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  Box 1: Select Columns in Dataset

  For Columns to be cleaned, choose the columns that contain the missing values you want to change. You can choose multiple columns, but you must use the same replacement method in all selected columns.

  Example:

  

  Box 2: Split data

  Splitting data is a common task in machine learning. You will split your data into two separate datasets. One dataset will train the model and the other will test how well the model performed.

  Box 3: Linear regression

  Because you want to predict price, which is a number, you can use a regression algorithm. For this example, you use a linear regression model.