AI-900 Microsoft Azure AI Fundamentals Exam Questions and Answers – Page 1

The latest Microsoft AI-900 Azure AI Fundamentals certification actual real practice exam question and answer (Q&A) dumps are available free, which are helpful for you to pass the Microsoft AI-900 Azure AI Fundamentals exam and earn Microsoft AI-900 Azure AI Fundamentals certification.

Exam Question 91

What information does the client application need to use a published custom vision model?

Correct Answer:
Project ID, Model name, Prediction endpoint, Prediction key

Exam Question 92

Object Detection

Correct Answer:
trained to classify individual objects within an image and identify their location with a bounding box

Exam Question 93

What information does an object detection model return?

Correct Answer:
Class of object identified, probability score of the object classification, coordinates of a bounding box for each object

Exam Question 94

What’s the difference between object detection and image classification?

Correct Answer:
Image classification is a machine learning based form of computer vision in which a model is trained to categorize images based on the primary subject matter they contain. Object detection goes further than this to classify individual objects within the image, and to return the coordinates of a bounding box that indicates the objects location

Exam Question 95

What are some uses of object detection?

Correct Answer:
Evaluating the safety of a building by looking for fire extinguishers or other emergency equipment, creating software for self-driving cars or vehicles with lane assist capabilities, medical imaging such as an MRI or x-rays that can detect known objects for medical diagnosis

Exam Question 96

What are the 3 main tasks for creating an object detection solution with custom vision?

Correct Answer:
Uploading and tagging images, training model, publishing model

Exam Question 97

What resources to you need to create an object detection solution?

Correct Answer:
Custom Vision(can be either training or prediction resource)
Cognitive Services(can be used for training, prediction or both)

Exam Question 98

What are key considerations for tagging training images?

Correct Answer:
having sufficient images from multiple angles and making sure bounding boxing are tightly defined

Exam Question 99

Face detection

Correct Answer:
involves identifying regions of an image that contain a human face, typically by returning bounding box coordinates that form a rectangle around the face

Exam Question 100

Face analysis

Correct Answer:
moves beyond simple face detection, some algorithms can also return other information such as facial landmarks(nose, eyes, eyebrows, lips). These can be used as features with which to train a machine learning model from which you can infer information about a person such as their gender, age or emotional state.