Microsoft Microsoft Certifications AI-900 Questions & Answers

• Question 121:

  You have a solution that analyzes social media posts to extract the mentions of city names and the city names discussed most frequently. Which type of natural language processing (NLP) workload does the solution use?

  A. speech recognition

  B. sentiment analysis

  C. key phrase extraction

  D. entity recognition

  Correct Answer: C

  Key phrase extraction is one of the features offered by Azure Cognitive Service for Language, a collection of machine learning and AI algorithms in the cloud for developing intelligent applications that involve written language. Use key phrase extraction to quickly identify the main concepts in text. For example, in the text "The food was delicious and the staff were wonderful.", key phrase extraction will return the main topics: "food" and "wonderful staff".

  Reference: https://learn.microsoft.com/en-us/azure/cognitive-services/language-service/key-phrase-extraction/overview

• Question 122:

  Which Computer Vision feature can you use to generate automatic captions for digital photographs?

  A. Recognize text.

  B. Identify the areas of interest.

  C. Detect objects.

  D. Describe the images.

  Correct Answer: D

  Describe images with human-readable language

  Computer Vision can analyze an image and generate a human-readable phrase that describes its contents. The algorithm returns several descriptions based on different visual features, and each description is given a confidence score. The

  final output is a list of descriptions ordered from highest to lowest confidence.

  The image description feature is part of the Analyze Image API.

  Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-describing-images

• Question 123:

  You need to create a model that labels a collection of your personal digital photographs. Which Azure Cognitive Services service should you use?

  A. Form Recognizer

  B. Custom Vision

  C. Language

  D. Computer Vision

  Correct Answer: D

  Computer Vision, an AI service that analyzes content in images and video.

  Extract rich information from images and video

  Boost content discoverability, automate text extraction, analyze video in real time, and create products that more people can use by embedding cloud vision capabilities in your apps with Computer Vision, part of Azure Cognitive Services. Use

  visual data processing to label content with objects and concepts, extract text, generate image descriptions, moderate content, and understand people's movement in physical spaces. No machine learning expertise is required.

  Reference: https://azure.microsoft.com/en-us/services/cognitive-services/computer-vision/

• Question 124:

  You have an AI solution that provides users with the ability to control smart devices by using verbal commands.

  Which two types of natural language processing (NLP) workloads does the solution use? Each correct answer presents part of the solution.

  NOTE: Each correct selection is worth one point.

  A. text-to-speech

  B. key phrase extraction

  C. speech-to-text

  D. language modeling

  E. translation

  Correct Answer: BC

  Key phrase extraction is one of the features offered by Azure Cognitive Service for Language, a collection of machine learning and AI algorithms in the cloud for developing intelligent applications that involve written language. Use key phrase extraction to quickly identify the main concepts in text. For example, in the text "The food was delicious and the staff were wonderful.", key phrase extraction will return the main topics: "food" and "wonderful staff".

  Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/language-service/key-phrase-extraction/overview

• Question 125:

  Natural language processing can be used to __________. Select the answer that correctly completes the sentence.

  A. Analyze video content

  B. Generate speech

  C. Classify email messages as work-related or personal.

  D. Classify images

  Correct Answer: C

  Natural language processing (NLP) has many uses: sentiment analysis, topic detection, language detection, key phrase extraction, and document categorization.

  Note: Specifically, you can use NLP to:

  *-> Classify documents. For instance, you can label documents as sensitive or spam.

  Do subsequent processing or searches. You can use NLP output for these purposes.

  Summarize text by identifying the entities that are present in the document.

  Tag documents with keywords. For the keywords, NLP can use identified entities.

  Do content-based search and retrieval. Tagging makes this functionality possible.

  Summarize a document's important topics. NLP can combine identified entities into topics.

  Categorize documents for navigation. For this purpose, NLP uses detected topics.

  Enumerate related documents based on a selected topic. For this purpose, NLP uses detected topics.

  Score text for sentiment. By using this functionality, you can assess the positive or negative tone of a document.

  Reference: https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing

• Question 126:

  Which two languages can you use to write custom code for Azure Machine Learning designer? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.

  A. Python

  B. R

  C. C#

  D. Scala

  Correct Answer: AB

  Use Azure Machine Learning designer for customizing using Python and R code. Reference: https://azure.microsoft.com/en-us/services/machine-learning/designer/#features

• Question 127:

  Your company manufactures widgets.

  You have 1,000 digital photos of the widgets.

  You need to identify the location of the widgets within the photos.

  What should you use?

  A. Computer Vision Spatial Analysis

  B. Custom Vision object detection

  C. Computer Vision Image Analysis

  D. Custom Vision classification

  Correct Answer: B

  Object detection is similar to tagging, but the API returns the bounding box coordinates (in pixels) for each object found in the image. For example, if an image contains a dog, cat and person, the Detect operation will list those objects 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 object in an image.

  The object detection function applies tags based on the objects or living things identified in the image.

  Incorrect:

  * Computer Vision Spatial Analysis

  Spatial analysis detects and locates human presence in video footage and outputs by using a bounding box around a human body. The AI models don't detect faces or determine individuals’ identities or demographics.

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

• Question 128:

  You need to convert handwritten notes into digital text. Which type of computer vision should you use?

  A. facial detection

  B. optical character recognition (OCR)

  C. image classification

  D. object detection

  Correct Answer: B

  Optical character recognition or optical character reader (OCR) is the electronic or mechanical conversion of images of typed, handwritten or printed text into machine-encoded text.

  Reference: https://en.wikipedia.org/wiki/Optical_character_recognition

• Question 129:

  You need to predict the animal population of an area. Which Azure Machine Learning type should you use?

  A. regression

  B. clustering

  C. classification

  Correct Answer: A

  Regression is a supervised machine learning technique used to predict numeric values.

  Reference: https://docs.microsoft.com/en-us/learn/modules/create-regression-model-azure-machine-learning-designer/1-introduction

• Question 130:

  During the process of Machine Learning, when should you review evaluation metrics.

  A. Before you train a model.

  B. After you clean the data.

  C. Before you choose the type of model.

  D. After you test a model on the validation data.

  Correct Answer: D