AI-900 Exam Questions

Total 262 Questions

Last Updated Exam : 16-Dec-2024

Topic 5: Describe features of conversational AI workloads on Azure

For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.






Extracting relationships between data from large volumes of unstructured data is an example of which type of Al workload?


A. computer vision


B. knowledge mining


C. natural language processing (NLP)


D. anomaly detection





B.
  knowledge mining

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
Achieving transparency helps the team to understand the data and algorithms used to train the model, what transformation logic was applied to the data, the final model generated, and its associated assets. This information offers insights about how the model was created, which allows it to be reproduced in a transparent way.
Box 2: No
A data holder is obligated to protect the data in an AI system, and privacy and security are an integral part of this system. Personal needs to be secured, and it should be accessed in a way that doesn't compromise an individual's privacy.
Box 3: No
Inclusiveness mandates that AI should consider all human races and experiences, and inclusive design practices can help developers to understand and address potential barriers that could unintentionally exclude people. Where possible, speech-to-text, text-to- speech, and visual recognition technology should be used to empower people with hearing, visual, and other impairments.

You plan to deploy an Azure Machine Learning model as a service that will be used by client applications.
Which three processes should you perform in sequence before you deploy the model? To answer, move the appropriate processes from the list of processes to the answer area and arrange them in the correct order.






You need to provide content for a business chatbot that will help answer simple user queries.
What are three ways to create question and answer text by using QnA Maker? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.


A. Generate the questions and answers from an existing webpage.


B. Use automated machine learning to train a model based on a file that contains the questions.


C. Manually enter the questions and answers.


D. Connect the bot to the Cortana channel and ask questions by using Cortana.


E. Import chit-chat content from a predefined data source.





A.
  Generate the questions and answers from an existing webpage.

C.
  Manually enter the questions and answers.

E.
  Import chit-chat content from a predefined data source.

Explanation:
Automatic extraction
Extract question-answer pairs from semi-structured content, including FAQ pages, support websites, excel files, SharePoint documents, product manuals and policies.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/qnamaker/concepts/contenttypes

You have 100 instructional videos that do NOT contain any audio. Each instructional video has a script. You need to generate a narration audio file for each video based on the script. Which type of workload should you use?


A. speech recognition


B. language modeling


C. speech synthesis


D. translation





C.
  speech synthesis

Select the answer that correctly completes the sentence.






You have the process shown in the following exhibit.

Which type AI solution is shown in the diagram?


A. a sentiment analysis solution


B. a chatbot


C. a machine learning model


D. a computer vision application





B.
  a chatbot

You are developing a model to predict events by using classification.
You have a confusion matrix for the model scored on test data as shown in the following exhibit.



Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic.
NOTE: Each correct selection is worth one point.








TP = True Positive.
The class labels in the training set can take on only two possible values, which we usually refer to as positive or negative. The positive and negative instances that a classifier predicts correctly are called true positives (TP) and true negatives (TN), respectively.
Similarly, the incorrectly classified instances are called false positives (FP) and false negatives (FN).

Box 2: 1,033 FN = False Negative

To complete the sentence, select the appropriate option in the answer area.







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