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.
Table of Contents
Question 501
You plan to apply Text Analytics API features to a technical support ticketing system.
Match the Text Analytics API features to the appropriate natural language processing scenarios.
To answer, drag the appropriate feature from the column on the left to its scenario on the right. Each feature may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Select and Place:
API Features:
- Entity recognition
- Key phrase extraction
- Language detection
- Sentiment analysis
Answer Area:
- Understand how upset a customer is based on the text contained in the support ticket.
- Summarize important information from the support ticket.
- Extract key dates from the support ticket.
Answer
Sentiment analysis: Understand how upset a customer is based on the text contained in the support ticket.
Key phrase extraction: Summarize important information from the support ticket.
Entity recognition: Extract key dates from the support ticket.
Explanation
API Feature 1: Sentiment analysis. Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral.
API Feature 2: Broad entity extraction: Broad entity extraction: Identify important concepts in text, including key. Key phrase extraction/ Broad entity extraction: Identify important concepts in text, including key phrases and named entities such as people, places, and organizations.
API Feature 3: Entity Recognition: Named Entity Recognition: Identify and categorize entities in your text as people, places, organizations, date/time, quantities, percentages, currencies, and more. Well-known entities are also recognized and linked to more information on the web.
Question 502
_____ is used to generate additional features.
A. Feature engineering
B. Feature selection
C. Model evaluation
D. Model training
Answer
A. Feature engineering
Question 503
Returning a bounding box that indicates the location of a vehicle in an image is an example of _____.
A. image classification
B. object detection
C. optical character recognizer (OCR)
D. semantic segmentation
Answer
B. object detection
Question 504
Match the types of AI workloads 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.
Workload Types:
- Anomaly detection
- Computer vision
- Machine Learning (Regression)
- Natural language processing
Answer Area:
- Identify handwritten letters.
- Predict the sentiment of a social media post.
- Identify a fraudulent credit card payment.
- Predict next month’s toy sales.
Answer
Computer vision: Identify handwritten letters.
Natural language processing: Predict the sentiment of a social media post.
Anomaly detection: Identify a fraudulent credit card payment.
Machine Learning (Regression): Predict next month’s toy sales.
Question 505
You are building an AI-based app. You need to ensure that the app uses the principles for responsible AI. Which two principles should you follow? (Each correct answer presents part of the solution. Choose two.)
A. Implement an Agile software development methodology.
B. Implement a process of AI model validation as part of the software review process.
C. Establish a risk governance committee that includes members of the legal team, members of the risk management team, and a privacy officer.
D. Prevent the disclosure of the use of AI-based algorithms for automated decision making.
Answer
B. Implement a process of AI model validation as part of the software review process.
C. Establish a risk governance committee that includes members of the legal team, members of the risk management team, and a privacy officer.
Question 506
You are authoring a Language Understanding (LUIS) application to support a music festival. You want users to be able to ask questions about scheduled shows, such as: “Which act is playing on the main stage?”. The question “Which act is playing on the main stage?” is an example of which type of element?
A. an intent
B. an utterance
C. a domain
D. an entity
Answer
B. an utterance
Explanation
Utterances are input from the user that your app needs to interpret.
Question 507
You build a QnA Maker bot by using a frequently asked questions (FAQ) page. You need to add professional greetings and other responses to make the bot more user friendly. What should you do?
A. Increase the confidence threshold of responses.
B. Enable active learning.
C. Create multi-turn questions.
D. Add chit-chat.
Answer
D. Add chit-chat.
Question 508
You use drones to identify where weeds grow between rows of crops to send an Instruction for the removal of the weeds. This is an example of which type of computer vision?
A. scene segmentation
B. optical character recognition (OCR)
C. object detection
Answer
C. object detection
Explanation
Object detection is similar to tagging, but the API returns the bounding box coordinates for each tag applied. 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.
Incorrect Answers:
B: Optical character recognition (OCR) allows you to extract printed or handwritten text from images and documents.
C: Scene segmentation determines when a scene changes in video based on visual cues. A scene depicts a single event and it’s composed by a series of consecutive shots, which are semantically related.
Question 509
You need to build an image tagging solution for social media that tags images of your friends automatically. Which Azure Cognitive Services service should you use?
A. Computer Vision
B. Face
C. Text Analytics
D. Form Recognizer
Answer
B. Face
Question 510
You use Azure Machine Learning designer to build a model pipeline. What should you create before you can run the pipeline?
A. a Jupyter notebook
B. a registered model
C. a compute resource
Answer
C. a compute resource