200 Practice Questions For Azure AI-900 Fundamentals Exam
By: Bhargav Bachina
Tags: Machine Learning, Certification, Azure, AI, Data Science

Describe features of Natural Language Processing (NLP) workloads on Azure (15–20%)

Practice questions based on these concepts

Identify features of common NLP Workload Scenarios

Identify Azure tools and services for NLP workloads

127. What is Text Analytics?

Text analytics is a process where an artificial intelligence (AI) algorithm, running on a computer, evaluates these same attributes in text, to determine specific insights.

128. You need to use a service from Azure that determines the language of a document or text (for example, French or English). Which one should you use?

Text Analytics cognitive service

129. You need to use a service from Azure that performs sentiment analysis on text to determine a positive or negative sentiment. Which one should you use?

Text Analytics cognitive service

130. You need to use a service from Azure that extracts key phrases from the text that might indicate its main talking points. Which one should you use?

Text Analytics cognitive service

131. You need to use a service from Azure Identify and categorize entities in the text. Entities can be people, places, organizations, or even everyday items such as dates, times, quantities, and so on. Which one should you use?

Text Analytics cognitive service

132. You are planning to read text information only. Which resource you should provision?

A Text Analytics resource - choose this resource type if you only plan to use the Text Analytics service, or if you want to manage access and billing for the resource separately from other services.

133. You are planning to read text information and objects in the Image. Which resource you should provision?

A Cognitive Services resource - choose this resource type if you plan to use the Text Analytics service in combination with other cognitive services, and you want to manage access and billing for these services together.

134. The Text Analytics service has language detection capability and you can submit multiple documents at a time for analysis. Is this true?

True

135. You have submitted multiple documents to the Text Analytics service. What is the output for each document?

* The language name (for example "English")

* The ISO 6391 language code (for example, "en")

* A score indicating a level of confidence in the language detection.

136. Consider a scenario where you own and operate a restaurant where customers can complete surveys and provide feedback on the food, the service, staff, and so on. Suppose you have received the following reviews from customers:

Review 1: “A fantastic place for lunch. The soup was delicious.”

Review 2: “Comida maravillosa y gran servicio.”

Review 3: “The croque monsieur avec frites was terrific. Bon appetit!”

You can use the Text Analytics service to detect the language for each of these reviews, and it might respond with the following results:

What does the information in the above table mean?

Review 1: It detected English with 1.0 confidence

Review 2: It detected Spanish with 1.0 confidence

Review 3: The language detection service will focus on the predominant language in the text. The service uses an algorithm to determine the predominant language, such as length of phrases or total amount of text for the language compared to other languages in the text.The predominant language will be the value returned, along with the language code. The confidence score may be less than 1 as a result of the mixed language text.

137. When the text in the document is ambiguous or mixed language content. What is the output of the Text Analytics service?

An ambiguous content example would be a case where the document contains limited text, or only punctuation. For example, using the service to analyze the text ":-)", results in a value of unknown for the language name and the language identifier, and a score of NaN (which is used to indicate not a number).

138. What does the confidence score of NaN Text Analytics service output mean?

Ambiguous or mixed language content

139. What is the Sentiment Analysis?

The Text Analytics service can evaluate text and return sentiment scores and labels for each sentence. This capability is useful for detecting positive and negative sentiment in social media, customer reviews, discussion forums and more.

140. What are the score ranges of Sentiment Analysis from the Text Analytics service?

Using the pre-built machine learning classification model, the service evaluates the text and returns a sentiment score in the range of 0 to 1, with values closer to 1 being a positive sentiment. Scores that are close to the middle of the range (0.5) are considered neutral or indeterminate.

141. What does the sentiment analysis score of 0.5 mean?

Indeterminate sentiment A score of 0.5 might indicate that the sentiment of the text is indeterminate, and could result from text that does not have sufficient context to discern a sentiment or insufficient phrasing. For example, a list of words in a sentence that has no structure, could result in an indeterminate score.

142. You are using the Text Analytics service for sentiment analysis. You have used the wrong language code. For example, A language code (such as “en” for English, or “fr” for French) is used to inform the service which language the text is in. What score does the service return?

The service will return a score of precisely 0.5.

143. What is Keyphrase extraction?

Key phrase extraction is the concept of evaluating the text of a document, or documents, and then identifying the main talking points of the document(s).

144. You are running a restaurant and have collected thousands of reviews through a number of surveys. You don’t have time to go through each review but, you want to know the most talking points. What feature of Text Analytics would help here?

Key phrase extraction: you can use the key phrases to identify important elements of the review.

145. What is Entity Recognition?

You can provide the Text Analytics service with unstructured text and it will return a list of entities in the text that it recognizes. The service can also provide links to more information about that entity on the web. An entity is essentially an item of a particular type or a category;and in some cases, subtype

146. You want to use the Text Analytics service to determine the key talking points in a text document. Which feature of the service should you use?

Key phrase extraction

147. You use the Text Analytics service to perform sentiment analysis on a document, and a score of 0.99 is returned. What does this score indicate about the document sentiment?

The document is positive.

148. When might you see NaN returned for a score in Language Detection?

When the language is ambiguous

149. What is Speech recognition?

The ability to detect and interpret spoken input. Speech recognition is concerned with taking the spoken word and converting it into data that can be processed - often by transcribing it into a text representation. The spoken words can be in the form of a recorded voice in an audio file, or live audio from a microphone.

150. What is Speech synthesis?

The ability to generate spoken output. Speech synthesis is in many respects the reverse of speech recognition. It is concerned with vocalizing data, usually by converting text to speech

151. What are the models you use to accomplish Speech recognition?

An acoustic model that converts the audio signal into phonemes (representations of specific sounds). A language model that maps phonemes to words, usually using a statistical algorithm that predicts the most probable sequence of words based on the phonemes.

152. What are some of the use cases for speech recognition?

* Providing closed captions for recorded or live videos

* Creating a transcript of a phone call or meeting

* Automated note dictation

* Determining intended user input for further processing

153. What are some of the use cases for speech synthesis?

* Generating spoken responses to user input.

* Creating voice menus for telephone systems.

* Reading email or text messages aloud in hands-free scenarios.

* Broadcasting announcements in public locations, such as railway stations or airports.

154. What are the required elements for the speech synthesis?

The text to be spoken.

The voice to be used to vocalize the speech. To synthesize speech, the system typically tokenizes the text to break it down into individual words, and assigns phonetic sounds to each word. It then breaks the phonetic transcription into prosodic units (such as phrases, clauses, or sentences) to create phonemes that will be converted to audio format. These phonemes are then synthesized as audio by applying a voice, which will determine parameters such as pitch and timbre; and generating an audio wave form that can be output to a speaker or written to a file.

155. What are the services for speech recognition and speech synthesis from Azure?

The Speech-to-Text API

The Text-to-Speech API

156. You want to use a service from Azure for just translating user spoken output to text. Which resource you should be provisioned in the Azure subscription?

A Speech resource - choose this resource type if you only plan to use the Speech service, or if you want to manage access and billing for the resource separately from other services.

157. You can use the speech-to-text API to perform real-time or batch transcription of audio into a text format. What does it mean?

Real-time speech-to-text allows you to transcribe text in audio streams. You can use real-time transcription for presentations, demos, or any other scenario where a person is speaking. Not all speech-to-text scenarios are real time. You may have audio recordings stored on a file share, a remote server, or even on Azure storage. You can point to audio files with a shared access signature (SAS) URI and asynchronously receive transcription results.

158. You have a person speaking right now and you want to transcribe that into written output. Which transcription should you use?

Real-time transcription

159. You have thousands of stored audio files and you want to transcribe that into written output. Which transcription should you use?

Batch transcription

160. Why Batch transcription is asynchronous?

Batch transcription should be run in an asynchronous manner because the batch jobs are scheduled on a best-effort basis. Normally a job will start executing within minutes of the request but there is no estimate for when a job changes into the running state.

161. You plan to build an application that uses the Speech service to transcribe audio recordings of phone calls into text and then submits the transcribed text to the Text Analytics service to extract key phrases. You want to manage access and billing for the application services in a single Azure resource. Which type of Azure resource should you create?

Cognitive Services

162. You want to use the Speech service to build an application that reads incoming email message subjects aloud. Which API should you use?

Text-to-Speech

163. What is Text Translation?

Text translation can be used to translate documents from one language to another, translate email communications that come from foreign governments, and even provide the ability to translate web pages on the Internet. Many times you will see a Translate option for posts on social media sites, or the Bing search engine can offer to translate entire web pages that are turned in search results.

164. What is Speech Translation?

Speech translation is used to translate between spoken languages, sometimes directly (speech-to-speech translation) and sometimes by translating to an intermediary text format (speech-to-text translation).

165. What is the service from Microsoft Azure for Text Translation?

The Translator Text service, which supports text-to-text translation.

166. What is the service from Microsoft Azure for Speech Translation?

The Speech service, which enables speech-to-text and speech-to-speech translation.

167. What is the output if you use the Text Analytics service to detect entities in the following restaurant review extract:

“I ate at the restaurant in Seattle last week.”

168. What are the services you should provision in your Azure subscription if you want to manage access and billing for each service individually?

There are dedicated Translator Text and Speech resource types

169. The Text Translator service supports text-to-text translation of more than 60 languages. is this correct?

True

170. Using the Text Translate service you can specify one from a language with multiple to languages, enabling you to simultaneously translate a source document to multiple languages. Is this true?

True

171. How do you handle brand names which are the same in all languages when using Text Translate service?

Selective translation. You can tag content so that it isn't translated.

172. When using the Text Translate you can control profanity translation by either marking the translated text as profane or by omitting it in the results. Is this correct?

True Profanity filtering. Without any configuration, the service will translate the input text, without filtering out profanity. Profanity levels are typically culture-specific but you can control profanity translation by either marking the translated text as profane or by omitting it in the results.

173. ________ used to transcribe speech from an audio source to text format?

Speech-to-text

174. ________ used to generate spoken audio from a text source?

Text-to-speech

175. ________ used to translate speech in one language to text or speech in another?

Speech Translation

176. You are developing an application that must take English input from a microphone and generate a real-time text-based transcription in Hindi. Which service should you use?

Speech

177. You need to use the Translator Text service to translate email messages from Spanish into both English and French? What is the most efficient way to accomplish this goal?

Make a single call to the service; specifying a "from" language of "es", a "to" language of "en", and another "to" language of "fr".

178. On Microsoft Azure, language understanding is supported through the ___________?

Language Understanding Intelligent Service

179. To work with Language Understanding, you need to take into account three core concepts. What are these concepts?

utterances, entities, and intents.

180. What are Utterances?

An utterance is an example of something a user might say, and which your application must interpret. For example, when using a home automation system, a user might use the following utterances: "Switch the fan on." "Turn on the light."

181. What are the Entities?

An entity is an item to which an utterance refers. For example, fan and light in the following utterances: "Switch the fan on." "Turn on the light."

182. What are Intents?

An intent represents the purpose, or goal, expressed in a user's utterance. For example, for both of the previously considered utterances, the intent is to turn a device on; so in your Language Understanding application, you might define a TurnOn intent that is related to these utterances.

183. What is None intent?

In a Language Understanding application, the None intent is created but left empty on purpose. The None intent is a required intent and can't be deleted or renamed. Fill it with utterances that are outside of your domain.

184. Creating a language understanding application with Language Understanding consists of two main tasks. What are these tasks?

First you must define entities, intents, and utterances with which to train the language model - referred to as authoring the model.



Then you must publish the model so that client applications can use it for intent and entity prediction based on user input.

185. How many types of entities and what are those?

There are four types of entities: Machine-Learned: Entities that are learned by your model during training from context in the sample utterances you provide. List: Entities that are defined as a hierarchy of lists and sublists. For example, a device list might include sublists for light and fan. For each list entry, you can specify synonyms, such as lamp for light. RegEx: Entities that are defined as a regular expression that describes a pattern — for example, you might define a pattern like [0–9]{3}-[0–9]{3}-[0–9]{4} for telephone numbers of the form 555–123–4567. Pattern.any: Entities that are used with patterns to define complex entities that may be hard to extract from sample utterances.

186. You need to provision an Azure resource that will be used to author a new Language Understanding application. What kind of resource should you create?

Language Understanding

187. You are authoring a Language Understanding application to support an international clock. You want users to be able to ask for the current time in a specified city, for example, “What is the time in London?”. What should you do?

Define a "city" entity and a "GetTime" intent with utterances that indicate the city intent.

188. You have published your Language Understanding application. What information does a client application developer need to get predictions from it?