Microsoft AI-900 Online Practice Questions and Exam Preparation

Microsoft AI-900 Online Questions & Answers

• Question 241:

  You need to develop a mobile app for employees to scan and store their expenses while travelling.

  Which type of computer vision should you use?

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

  ---

  Explanation

  Azure's Computer Vision API includes Optical Character Recognition (OCR) capabilities that extract printed or handwritten text from images. You can extract text from images, such as photos of license plates or containers with serial numbers, as well as from documents - invoices, bills, financial reports, articles, and more.

  References:

  https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-recognizing-text

• Question 242:

  You have a dataset that contains information about taxi journeys that occurred during a given period.

  You need to train a model to predict the fare of a taxi journey.

  What should you use as a feature?

  A. the number of taxi journeys in the dataset
  B. the trip distance of individual taxi journeys
  C. the fare of individual taxi journeys
  D. the trip ID of individual taxi journeys
  B. the trip distance of individual taxi journeys

  ---

  Explanation

  The label is the column you want to predict. The identified Featuresare the inputs you give the model to predict the Label.

  Example:

  The provided data set contains the following columns:

  vendor_id: The ID of the taxi vendor is a feature. rate_code: The rate type of the taxi trip is a feature. passenger_count: The number of passengers on the trip is a feature. trip_time_in_secs: The amount of time the trip took. You want to predict the fare of the trip before the trip is completed. At that moment, you don't know how long the trip would take. Thus, the trip time is not a feature and you'll exclude this column from the model. trip_distance: The distance of the trip is a feature. payment_type: The payment method (cash or credit card) is a feature. fare_amount: The total taxi fare paid is the label.

  References:

  https://docs.microsoft.com/en-us/dotnet/machine-learning/tutorials/predict-prices

• Question 243:

  A company employs a team of customer service agents to provide telephone and email support to customers.

  The company develops a webchat bot to provide automated answers to common customer queries.

  Which business benefit should the company expect as a result of creating the webchat bot solution?

  A. increased sales
  B. a reduced workload for the customer service agents
  C. improved product reliability
  B. a reduced workload for the customer service agents

• Question 244:

  HOTSPOT

  Select the answer that correctly completes the sentence.

  

  

  ---

  Privacy and security. According to Microsoft's Responsible AI Principles, implementing filters to block harmful or inappropriate content in a Generative AI chat solution demonstrates a commitment to the Privacy and Security principle. This principle ensures that AI systems are designed and operated in a way that protects users, their data, and society from harm. When a chat system uses Generative AI models (like Azure OpenAI's GPT-based services), there is a risk that the model might produce unsafe, offensive, or sensitive content. Microsoft addresses this through content filters and safety systems, which automatically detect and block violent, hate-based, or sexually explicit outputs. This is part of responsible deployment practices to ensure that user interactions remain safe, private, and compliant with ethical standards.

  Implementing these filters aligns with the Privacy and Security principle because it: Protects users from exposure to harmful or abusive content. Ensures that conversations are safeguarded against malicious or unsafe use. Upholds user trust by maintaining a safe digital environment for all participants. Let's briefly clarify why the other options are incorrect: Fairness deals with ensuring unbiased treatment and equitable outcomes in AI decisions. Transparency focuses on explaining how AI systems make decisions. Accountability refers to human oversight and responsibility for AI actions. Thus, content filtering mechanisms are explicitly an example of Privacy and Security, as they protect users and data from harm or misuse while maintaining ethical AI behavior. Therefore, the verified correct answer is Privacy and security.

• Question 245:

  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 246:

  What should you use to extract details from scanned images of contracts?

  A. Azure Al Document Intelligence
  B. Azure Al Immersive Reader
  C. Azure OpenAI
  D. Azure Al Search
  A. Azure Al Document Intelligence

  ---

  Explanation

  The correct answer is A. Azure AI Document Intelligence (previously known as Form Recognizer). This Azure Cognitive Service is specifically designed to extract structured data and key information from scanned documents, forms, and contracts using advanced Optical Character Recognition (OCR) combined with machine learning models. According to the Microsoft Learn module "Extract data from documents with Azure AI Document Intelligence", this service enables automated data extraction from unstructured or semi-structured documents such as contracts, invoices, receipts, and purchase orders. It identifies key-value pairs, tables, and fields such as names, dates, amounts, and signatures. This makes it ideal for digitizing legal and business documents like contracts into structured formats that can be easily searched or stored in databases. Azure AI Document Intelligence offers several model types: Prebuilt models for common documents (invoices, receipts, business cards, etc.). Custom models trained on your specific contract layouts. Layout model for extracting raw text, tables, and structures. The other options are incorrect:

  B. Azure AI Immersive Reader enhances reading comprehension and accessibility but does not extract data from documents.

  C. Azure OpenAI provides natural language generation and understanding but is not used for scanning or data extraction.

  D. Azure AI Search indexes and searches textual or document content but relies on other services (like Document Intelligence) to extract the data first. Therefore, to automatically extract details such as contract terms, names, dates, and signatures from scanned contract images, the best Microsoft AI service is Azure AI Document Intelligence

• Question 247:

  You are creating a model to classify emails as spam or not spam.

  Which type of machine learning should you use?

  A. regression
  B. clustering
  C. classification
  D. anomaly detection
  C. classification

  ---

  Explanation

  Classification assigns items to predefined categories, such as spam or not spam.

• Question 248:

  You use natural language processing to process text from a Microsoft news story.

  You receive the output shown in the following exhibit.

  

  Which type of natural languages processing was performed?

  A. entity recognition
  B. key phrase extraction
  C. sentiment analysis
  D. translation
  A. entity recognition

  ---

  Explanation

  You can provide the Text Analytics service with unstructured text and it will return a list of entities in the text that it recognizes. You can provide the Text Analytics service with unstructured text and it will return a list of entities in the text that it recognizes. The service can also provide links to more information about that entity on the web. An entity is essentially an item of a particular type or a category; and in some cases, subtype, such as those as shown in the following table.

  https://docs.microsoft.com/en-us/learn/modules/analyze-text-with-text-analytics-service/2-get-started-azure

  https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/overview

• Question 249:

  You are building a chatbot that will use natural language processing (NLP) to perform the following actions based on the text input of a user:

  1. Accept customer orders.

  2. Retrieve support documents.

  3. Retrieve order status updates.

  Which type of NLP should you use?

  A. sentiment analysis
  B. translation
  C. language modeling
  D. named entity recognition
  D. named entity recognition

  ---

  Explanation

  What is custom named entity recognition (NER)?

  Custom NER is one of the custom features offered by Azure Cognitive Service for Language. It is a cloud-based API service that applies machine-learning intelligence to enable you to build custom models for custom named entity recognition tasks.

  Custom NER enables users to build custom AI models to extract domain-specific entities from unstructured text, such as contracts or financial documents.

  References:

  https://docs.microsoft.com/en-us/azure/cognitive-services/language-service/custom-named-entity-recognition/overview

• Question 250:

  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.

  

  

  ---

  Box 1: Yes

  For regression problems, the label column must contain numeric data that represents the response variable. Ideally the numeric data represents a continuous scale.

  Box 2: No

  K-Means Clustering

  Because the K-means algorithm is an unsupervised learning method, a label column is optional.

  If your data includes a label, you can use the label values to guide selection of the clusters and optimize the model.

  If your data has no label, the algorithm creates clusters representing possible categories, based solely on the data.

  Box 3: No

  For classification problems, the label column must contain either categorical values or discrete values. Some examples might be a yes/no rating, a disease classification code or name, or an income group. If you pick a noncategorical column, the component will return an error during training.

  References:

  https://docs.microsoft.com/en-us/azure/machine-learning/component-reference/train-modelhttps://docs.microsoft.com/en-us/azure/machine-learning/component-reference/k-means-clustering