Topic 2: Describe fundamental principles of machine learning 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.


Select the answer that correctly completes the sentence.


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 Al character in a computer game that speaks audibly to a player

A.   an automated voice that reads back a credit card number entered into a telephone by using a numeric keypad
D.   an Al character in a computer game that speaks audibly to a player

You need to generate cartoons for use in a brochure. Each cartoon will be based on a text description.
Which Azure OpenAI model should you use?

A. Codex

B. DALL-E

C. GPT-3.5

D. GPT-4

B.   DALL-E


You are developing a Chabot solution in Azure.
Which service should you use to determine a user’s intent?

A. Translator

B. Azure Cognitive Search

C. Speech

D. Language

B.   Azure Cognitive Search

You need to identify street names based on street signs in photographs.<br>
Which type of computer vision should you use?

A. object detection

B. optical character recognition (OCR)

C. image classification

D. facial recognition

A.   object detection

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.




Explanation:
Box1: Sentiment analysis
Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral.
Box 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.
Box 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.

You have an Azure Machine Learning pipeline that contains a Split Data module. The Split Data module outputs to a Train Model module and a Score Model module. What is the function of the Split Data module?

A. selecting columns that must be included in the model

B. creating training and validation datasets

C. diverting records that have missing data

D. scaling numeric variables so that they are within a consistent numeric range

A.   selecting columns that must be included in the model

You need to build an app that will read recipe instructions aloud to support users who have reduced vision.
Which version service should you use?

A. Text Analytics

B. Translator Text

C. Speech

D. Language Understanding (LUIS)

C.   Speech

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




Explanation:
The Text Analytics API is a cloud-based service that provides advanced natural language processing over raw text, and includes four main functions: sentiment analysis, key phrase extraction, named entity recognition, and language detection.
Box 1: Yes
You can detect which language the input text is written in and report a single language code for every document submitted on the request in a wide range of languages, variants, dialects, and some regional/cultural languages. The language code is paired with a score indicating the strength of the score.
Box 2: No

Box 3: Yes
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.

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