Microsoft AI-900 Online Practice Questions and Exam Preparation

Microsoft AI-900 Online Questions & Answers

• Question 91:

  Which Azure OpenAI model should you use to summarize the text from a document?

  A. Whisper
  B. DALL-E
  C. Codex
  D. GPT
  D. GPT

• Question 92:

  For a machine learning progress, how should you split data for training and evaluation?

  A. Use features for training and labels for evaluation.
  B. Randomly split the data into rows for training and rows for evaluation.
  C. Use labels for training and features for evaluation.
  D. Randomly split the data into columns for training and columns for evaluation.
  B. Randomly split the data into rows for training and rows for evaluation.

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  Explanation

  https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-module-reference/split-data

• Question 93:

  You are processing photos of runners in a race.

  You need to read the numbers on the runners' shirts to identity the runners in the photos.

  Which type of computer vision should you use?

  A. facial recognition
  B. optical character recognition (OCR)
  C. semantic segmentation
  D. object detection
  B. optical character recognition (OCR)

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  Explanation

  Optical character recognition (OCR) allows you to extract printed or handwritten text from images and documents.

  References:

  https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/overview-ocr

• Question 94:

  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 95:

  Which scenario is an example of a webchat bot?

  A. Determine whether reviews entered on a website for a concert are positive or negative, and then add a thumbs up or thumbs down emoji to the reviews.
  B. Translate into English questions entered by customers at a kiosk so that the appropriate person can call the customers back.
  C. Accept questions through email, and then route the email messages to the correct person based on the content of the message.
  D. From a website interface, answer common questions about scheduled events and ticket purchases for a music festival.
  D. From a website interface, answer common questions about scheduled events and ticket purchases for a music festival.

• Question 96:

  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: Yes

  Content Moderator is part of Microsoft Cognitive Services allowing businesses to use machine assisted moderation of text, images, and videos that augment human review.

  The text moderation capability now includes a new machine-learning based text classification feature which uses a trained model to identify possible abusive, derogatory or discriminatory language such as slang, abbreviated words, offensive, and intentionally misspelled words for review.

  Box 2: No

  Azure's Computer Vision service gives you access to advanced algorithms that process images and return information based on the visual features you're interested in. For example, Computer Vision can determine whether an image contains adult content, find specific brands or objects, or find human faces.

  Box 3: Yes

  Natural language processing (NLP) is used for tasks such as sentiment analysis, topic detection, language detection, key phrase extraction, and document categorization.

  Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral.

  References:

  https://azure.microsoft.com/es-es/blog/machine-assisted-text-classification-on-content-moderator-public-preview/https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing

• Question 97:

  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: Yes

  In machine learning, if you have labeled data, that means your data is marked up, or annotated, to show the target, which is the answer you want your machine learning model to predict.

  In general, data labeling can refer to tasks that include data tagging, annotation, classification, moderation, transcription, or processing.

  Box 2: No

  Box 3: No

  Accuracy is simply the proportion of correctly classified instances. It is usually the first metric you look at when evaluating a classifier. However, when the test data is unbalanced (where most of the instances belong to one of the classes), or you are more interested in the performance on either one of the classes, accuracy doesn't really capture the effectiveness of a classifier.

  References:

  https://www.cloudfactory.com/data-labeling-guidehttps://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance

• Question 98:

  You have a dataset.

  You need to build an Azure Machine Learning classification model that will identify defective products.

  What should you do first?

  A. Load the dataset.
  B. Create a clustering model.
  C. Split the data into training and testing datasets.
  D. Create a classification model.
  C. Split the data into training and testing datasets.

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  Explanation

  Understand steps for classification

  You can think of the steps to train and evaluate a classification 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: Compare how close the model's predictions are to the known labels.

  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://docs.microsoft.com/en-us/learn/modules/create-classification-model-azure-machine-learning-designer/classification-steps

• Question 99:

  A developer needs to extract printed text from scanned images before performing text analysis.

  Which technique should be used?

  A. Optical Character Recognition (OCR)
  B. Image segmentation
  C. Object tracking
  D. Image classification
  A. Optical Character Recognition (OCR)

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  Explanation

  OCR converts text within images into machine-readable text, enabling further analysis such as translation or sentiment analysis.

• Question 100:

  HOTSPOT

  Select the answer that correctly completes the sentence.

  

  

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  Azure Kubernetes Service (AKS). According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn documentation on Azure Machine Learning, the Azure Kubernetes Service is commonly used to host and deploy machine learning models, including Automated ML models, into production environments. Once a model is trained using Azure Machine Learning (Azure ML), it must be deployed as a web service endpoint so it can receive data and return predictions. Azure ML offers two primary options for hosting and deploying models: Azure Kubernetes Service (AKS) - for high-scale, production-grade deployments that require fast response times, high availability, and scalability.

  Azure Container Instances (ACI) - for testing or low-scale workloads where cost and simplicity are more important than performance. AKS provides a managed Kubernetes cluster that allows for automated container orchestration, load balancing, scaling, and monitoring of deployed machine learning models. When you use Automated ML in Azure ML Studio, the generated model can be containerized and deployed directly to AKS, making it accessible as a REST API endpoint. This enables applications, systems, or users to send data and receive predictions in real time. The other options serve different purposes: Azure Data Factory is used for data integration and pipeline orchestration, not model hosting.

  Azure Automation focuses on automating administrative tasks and runbooks, not ML deployment. Azure Logic Apps is used to automate workflows and integrate services, not to serve ML models. Therefore, the correct service to host automated machine learning (AutoML) models in production is Azure Kubernetes Service (AKS), as it provides a reliable, scalable, and secure environment for real-time inference and enterprise AI workloads.