microsoft ai-900 practice test
microsoft azure ai fundamentals
Question 1
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.
Hot Area:
Answer:
Reference:
https://docs.microsoft.com/en-us/azure/bot-service/bot-service-manage-channels?view=azure-bot-service-4.0
Question 2
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
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/qnamaker/how-to/chit-chat-knowledge-base?tabs=v1
Question 3
Which type of machine learning should you use to predict the number of gift cards that will be sold next month?
- A. classification
- B. regression
- C. clustering
Answer:
b
In the most basic sense, regression refers to prediction of a numeric target.
Linear regression attempts to establish a linear relationship between one or more independent variables and a numeric outcome, or dependent variable.
You use this module to define a linear regression method, and then train a model using a labeled dataset. The trained model can then be used to make predictions.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/linear-regression
Question 4
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. Face
- B. Form Recognizer
- C. Text Analytics
- D. Computer Vision
Answer:
a
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/face/overview https://docs.microsoft.com/en-us/azure/cognitive-services/face/face-api-how-to-topics/howtodetectfacesinimage
Question 5
You are building a tool that will process images from retail stores and identify the products of competitors.
The solution will use a custom model.
Which Azure Cognitive Services service should you use?
- A. Custom Vision
- B. Form Recognizer
- C. Face
- D. Computer Vision
Answer:
a
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/overview
Question 6
In which two scenarios can you use a speech synthesis solution? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
- A. an automated voice that reads back a credit card number entered into a telephone by using a numeric keypad
- B. generating live captions for a news broadcast
- C. extracting key phrases from the audio recording of a meeting
- D. an AI character in a computer game that speaks audibly to a player
Answer:
ad
Azure Text to Speech is a Speech service feature that converts text to lifelike speech.
Incorrect Answers:
C: Extracting key phrases is not speech synthesis.
Reference:
https://azure.microsoft.com/en-in/services/cognitive-services/text-to-speech/
Question 7
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.
Answer:
Question 8
HOTSPOT To complete the sentence, select the appropriate option in the answer area.
Hot Area:
Answer:
Reference:
https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai
Question 9
HOTSPOT To complete the sentence, select the appropriate option in the answer area.
Hot Area:
Answer:
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-object-detection
Question 10
You are developing a natural language processing solution in Azure. The solution will analyze customer reviews and determine how positive or negative each review is.
This is an example of which type of natural language processing workload?
- A. language detection
- B. sentiment analysis
- C. key phrase extraction
- D. entity recognition
Answer:
b
Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral.
Reference:
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing