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[AI-102 practice test q1-q15] Free sharing of AI-102 exam questions and answers from Pass4itSure
QUESTION 1 #
You build a language model by using a Language Understanding service. The language model is used to search for
information on a contact list by using an intent named FindContact. A conversational expert provides you with the following list of phrases to use for training. Find contacts in London.
Who do I know in Seattle?
Search for contacts in Ukraine.
You need to implement the phrase list in Language Understanding.
Solution: You create a new entity for the domain.
Does this meet the goal?
A. Yes
B. No
Correct Answer: B
Instead, use a new intent for location.
Note: An intent represents a task or action the user wants to perform. It is a purpose or goal expressed in a user\\’s
utterance.
Define a set of intents that corresponds to actions users want to take in your application.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/luis-concept-intent
QUESTION 2 #
DRAG-DROP
You are developing a call to the Face API. The call must find similar faces from an existing list named employee’s faces.
The employee faces list contains 60,000 images.
How should you complete the body of the HTTP request? To answer, drag the appropriate values to the correct targets.
Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or
scroll to view content.
NOTE: Each correct selection is worth one point.
Select and Place:
Box 1: LargeFaceListID
LargeFaceList: Add a face to a specified large face list, up to 1,000,000 faces.
Note: Given query face\\’s face, to search the similar-looking faces from a faced array, a facelift, or a large face list. A
“faceListId” is created by FaceList – Create containing persistedFaceIds that will not expire. And a “largeFaceListId” is
created by LargeFaceList – Create containing persistedFaceIds that will also not expire.
Incorrect Answers:
Not “faceListId”: Add a face to a specified face list, up to 1,000 faces.
Box 2: matchFace
Find similar has two working modes, “matchPerson” and “watch face”. “matchPerson” is the default mode that tries to
find faces of the same person as possible by using internal same-person thresholds.
It is useful to find a known
person\\’s other photos. Note that an empty list will be returned if no faces pass the internal thresholds. “matchFace” mode ignores same-person thresholds and returns ranked similar faces anyway, even the similarity is low. It can be used in the cases like searching for celebrity-looking faces.
Reference:
https://docs.microsoft.com/en-us/rest/api/faceapi/face/findsimilar
QUESTION 3 #
You have the following C# method for creating Azure Cognitive Services resources programmatically.
You need to call the method to create a free Azure resource in the West US Azure region. The resource will be used to
generate captions of images automatically. Which code should you use?
A. create_resource(client, “res1”, “ComputerVision”, “F0”, “westus”)
B. create_resource(client, “res1”, “CustomVision.Prediction”, “F0”, “westus”)
C. create_resource(client, “res1”, “ComputerVision”, “S0”, “westus”)
D. create_resource(client, “res1”, “CustomVision.Prediction”, “S0”, “westus”)
Correct Answer: B
Many of the Cognitive Services have a free tier you can use to try the service. To use the free tier, use F0 as the SKU
for your resource. There are two tiers of keys for the Custom Vision service. You can sign up for an F0 (free) or S0
(standard) subscription through the Azure portal.
Incorrect Answers:
A: There is no free tier (F0) for ComputerVision.
Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/cognitive-services-apis-create-account-clientlibrary?pivots=programming-language-csharp https://docs.microsoft.com/en-us/azure/cognitive-services/custom-visionservice/limits-and-quotas
QUESTION 4 #
You successfully run the following HTTP request.
POST-https://management.azure.com/subscriptions/18c51a87-3a69-47a8-aedca54745f708a1/resourceGroups/RG1/providers/Microsoft.CognitiveServices/accounts/contosol/regenerateKey?apiversion=2017-04-18
Body{“keyName”: “Key2”}
What is the result of the request?
A. A key for Azure Cognitive Services was generated in Azure Key Vault.
B. A new query key was generated.
C. The primary subscription key and the secondary subscription key were rotated.
D. The secondary subscription key was reset.
Correct Answer: B
Accounts – Regenerate Key regenerates the specified account key for the specified Cognitive Services account.
Syntax: POST https://management.azure.com/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/
Microsoft.CognitiveServices/accounts/{accountName}/regenerateKey?api-version=2017-04-18
Reference:
https://docs.microsoft.com/en-us/rest/api/cognitiveservices/accountmanagement/accounts/regeneratekey
QUESTION 5 #
You are developing a photo application that will find photos of a person based on a sample image by using the Face
API.
You need to create a POST request to find the photos.
How should you complete the request? To answer, drag the appropriate values to the correct targets. Each value may
be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view
content.
NOTE: Each correct selection is worth one point.
Select and Place:
Correct Answer:
Box 1: detect
Face – Detect With Url: Detect human faces in an image, return face rectangles, and optionally with faces, landmarks,
and attributes. POST {Endpoint}/face/v1.0/detect
Box 2: match person
Find similar has two working modes, “matchPerson” and “watch face”. “matchPerson” is the default mode that tries to
find faces of the same person as possible by using internal same-person thresholds. It is useful to find a known
person\\’s other photos. Note that an empty list will be returned if no faces pass the internal thresholds. “matchFace” mode ignores same-person thresholds and returns ranked similar faces anyway, even the similarity is low. It can be used in the cases like searching for celebrity-looking faces.
Reference:
https://docs.microsoft.com/en-us/rest/api/faceapi/face/detectwithurl
https://docs.microsoft.com/en-us/rest/api/faceapi/face/findsimilar
QUESTION 6 #
You have a Video Indexer service that is used to provide a search interface over company videos on your company\’s
website.
You need to be able to search for videos based on who is present in the video.
What should you do?
A. Create a personal model and associate the model to the videos.
B. Create person objects and provide face images for each object.
C. Invite the entire staff of the company to Video Indexer.
D. Edit the faces in the videos.
E. Upload names to a language model.
Correct Answer: A
Video Indexer supports multiple Person models per account. Once a model is created, you can use it by providing the
model ID of a specific Person model when uploading/indexing or reindexing a video. Training a new face for a video
updates the specific custom model that the video was associated with.
Note: Video Indexer supports face detection and celebrity recognition for video content. The celebrity recognition feature covers about one million faces based on commonly requested data sources such as IMDB, Wikipedia, and top LinkedIn influencers.
Faces that aren\’t recognized by the celebrity recognition feature are detected but left unnamed. Once you
label a face with a name, the face and name get added to your account\’s Person model. Video Indexer will then
recognize this face in your future videos and past videos.
Reference: https://docs.microsoft.com/en-us/azure/media-services/video-indexer/customize-person-model-with-api
QUESTION 7 #
HOTSPOT
You need to create a new resource that will be used to perform sentiment analysis and optical character recognition
(OCR). The solution must meet the following requirements:
Use a single key and endpoint to access multiple services. Consolidate billing for future services that you might use.
Support the use of Computer Vision in the future.
How should you complete the HTTP request to create the new resource? To answer, select the appropriate options in
the answer area.
NOTE: Each correct selection is worth one point.
Hot Area:
Correct Answer:
Box 1: PUT
Sample Request: PUT https://management.azure.com/subscriptions/00000000-0000-0000-0000-000000000000/resourc
eGroups/test-rg/providers/Microsoft.DeviceUpdate/accounts/contoso?api-version=2020-03-01-preview
Incorrect Answers:
PATCH is for updates.
Box 2: CognitiveServices
Microsoft Azure Cognitive Services provide us to use its pre-trained models for various Business Problems related to
Machine Learning.
List of Different Services are:
Decision
Language (includes sentiment analysis)
Speech
Vision (includes OCR)
Web Search
Reference:
https://docs.microsoft.com/en-us/rest/api/deviceupdate/resourcemanager/accounts/create
https://www.analyticsvidhya.com/blog/2020/12/microsoft-azure-cognitive-services-api-for-ai-development/
QUESTION 8 #
You are building a multilingual chatbot.
You need to send a different answer for positive and negative messages.
Which two Text Analytics APIs should you use? Each correct answer presents part of the solution. (Choose two.) NOTE: Each correct selection is worth one point.
A. Linked entities from a well-known knowledge base
B. Sentiment Analysis
C. Key Phrases
D. Detect Language
E. Named Entity Recognition
Correct Answer: BD
B: The Text Analytics API\’s Sentiment Analysis feature provides two ways for detecting positive and negative
sentiment. If you send a Sentiment Analysis request, the API will return sentiment labels (such as “negative”, “neutral”
and “positive”) and confidence scores at the sentence and document level.
D: The Language Detection feature of the Azure Text Analytics REST API evaluates text input for each document and
returns language identifiers with a score that indicates the strength of the analysis.
This capability is useful for content stores that collect arbitrary text, where language is unknown.
Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/how-tos/text-analytics-how-tosentiment-analysis?tabs=version-3-1
https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/how-tos/text-analytics-how-to-languagedetection
QUESTION 9 #
HOTSPOT
You are building a chatbot by using the Microsoft Bot Framework SDK.
You use an object named UserProfile to store user profile information and an object named ConversationData to store
information related to a conversation. You create the following state accessors to store both objects in state.
var userStateAccessors = _userState.CreateProperty(name of(UserProfile));
var conversationStateAccessors = _conversationState.CreateProperty(name of(ConversationData));
The state storage mechanism is set to Memory Storage.
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
You create property accessors using the CreateProperty method that provides a handle to the BotState object. Each
state property accessor allows you to get or set the value of the associated state property.
Box 2: Yes
Box 3: No
Before you exit the turn handler, you use the state management objects\’ SaveChangesAsync() method to write all
state changes back to storage.
Reference:
https://docs.microsoft.com/en-us/azure/bot-service/bot-builder-howto-v4-state
QUESTION 10 #
You need to build a chatbot that meets the following requirements:
Supports chit-chat, knowledge base, and multilingual models Perform sentiment analysis on user messages Selects
the best language model automatically
What should you integrate into the chatbot?
A. QnA Maker, Language Understanding, and Dispatch
B. Translator, Speech, and Dispatch
C. Language Understanding, Text Analytics, and QnA Maker
D. Text Analytics, Translator, and Dispatch
Correct Answer: C
Language Understanding: An AI service that allows users to interact with your applications, bots, and IoT devices by
using natural language.
QnA Maker is a cloud-based Natural Language Processing (NLP) service that allows you to create a natural
conversational layer over your data. It is used to find the most appropriate answer for any input from your custom
knowledge base (KB) of information.
Text Analytics: my insights in unstructured text using natural language processing (NLP)–no machine learning
expertise required. Gain a deeper understanding of customer opinions with sentiment analysis. The Language Detection feature of the Azure Text Analytics, REST API evaluates text input
Incorrect Answers:
A, B, D: Dispatch uses sample utterances for each of your bot\’s different tasks (LUIS, QnA Maker, or custom), and
builds a model that can be used to properly route your user\’s request to the right task, even across multiple bots.
Reference:
https://azure.microsoft.com/en-us/services/cognitive-services/text-analytics/ https://docs.microsoft.com/enus/azure/cognitive-services/qnamaker/overview/overview
QUESTION 11 #
You are developing a smart e-commerce project.
You need to implement auto-completion as part of the Cognitive Search solution.
Which three actions should you perform? Each correct answer presents part of the solution. (Choose three.)
NOTE: Each correct selection is worth one point.
A. Make API queries to the autocomplete endpoint and include the suggested name in the body.
B. Add a suggester that has the three product name fields as source fields.
C. Make API queries to the search endpoint and includes the product name fields in the search fields query parameter.
D. Add a suggester for each of the three product name fields.
E. Set the search analyzer property for the three product name variants.
F. Set the analyzer property for the three product name variants.
Correct Answer: ABF
Scenario: Support autocompletion and autosuggestion based on all product name variants.
A: Call a suggester-enabled query, in the form of a Suggestion request or Autocomplete request, using an API. API
usage is illustrated in the following call to the Autocomplete REST API.
POST /indexes/myxboxgames/docs/autocomplete?searchandapi-version=2020-06-30
{ “search”: “minecraf”, “suggesterName”: “sg”}
B: In Azure Cognitive Search, typeahead or “search-as-you-type” is enabled through a suggester. A suggester provides a list of fields that undergo additional tokenization, generating prefix sequences to support matches on partial terms. For example, a suggester that includes a City field with a value for “Seattle” will have prefix combinations of “sea”, “seat”, “seat”, and “Seattle” to support typeahead.
F. Use the default standard Lucene analyzer (“analyzer”: null) or a language analyzer (for example, “analyzer”:
“en. Microsoft”) on the field.
Reference: https://docs.microsoft.com/en-us/azure/search/index-add-suggesters
QUESTION 12 #
You plan to use a Language Understanding application named app1 that is deployed to a container.
App1 was developed by using a Language Understanding authoring resource named lu1.
App1 has the versions shown in the following table.
You need to create a container that uses the latest deployable version of app1.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions
to the answer area and arrange them in the correct order. (Choose three.)
Select and Place:
Correct Answer:
QUESTION 13 #
You build a custom Form Recognizer model.
You receive sample files to use for training the model as shown in the following table.
B. File2
C. File3
D. File4
E. File5
F. File6
Correct Answer: ACF
Input requirements
Form Recognizer works on input documents that meet these requirements:
The format must be JPG, PNG, PDF (text or scanned), or TIFF. Text-embedded PDFs are best because there\\’s no
possibility of error in character extraction and location.
File size must be less than 50 MB.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/form-recognizer/overview
QUESTION 14 #
You develop an application to identify species of flowers by training a Custom Vision model.
You receive images of new flower species.
You need to add the new images to the classifier.
Solution: You add the new images and labels to the existing model. You retrain the model and then publish the model.
Does this meet the goal?
A. Yes
B. No
Correct Answer: A
The model needs to be extended and retrained.
QUESTION 15 #
You have 100 chatbots that each have its own Language Understanding model. Frequently, you must add the same phrases to each model.
You need to programmatically update the Language Understanding models to include the new phrases.
How should you complete the code? To answer, drag the appropriate values to the correct targets. Each value may be
used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Box 1: AddPhraseListAsync Example: Add phraselist feature
var phraselistId = await client.Features.AddPhraseListAsync(appId, versionId, new PhraselistCreateObject
{ EnabledForAllModels = false, IsExchangeable = true, Name = “QuantityPhraselist”, Phrases = “few,more,extra”
});
Box 2: PhraselistCreateObject
Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/luis/client-libraries-rest-api
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