Microsoft AI-102 Exam Practice Test Instant Access

Question 1

You build a custom Form Recognizer model.

You receive sample files to use for training the model as shown in the following table.

Which three files can you use to train the model? Each correct answer presents a complete solution. (Choose three.)

NOTE: Each correct selection is worth one point.

AFile1

BFile2

CFile3

DFile4

EFile5

FFile6

Answer : A, B, F

Input requirements

Form Recognizer works on input documents that meet these requirements:

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.

https://docs.microsoft.com/en-us/azure/cognitive-services/form-recognizer/overview

Question 2

You need to measure the public perception of your brand on social media messages. Which Azure Cognitive Services service should you use?

AText Analytics

BContent Moderator

CComputer Vision

DForm Recognizer

Answer : A

Text Analytics Cognitive Service could be used to quickly determine the public perception for a specific topic, event or brand.

Example: A NodeJS app which pulls Tweets from Twitter using the Twitter API based on a specified search term. Then pass these onto Text Analytics for sentiment scoring before storing the data and building a visualisation in PowerBI. The Architecture looked something like this:

https://www.linkedin.com/pulse/measuring-public-perception-azure-cognitive-services-steve-dalai

Question 3

You plan to perform predictive maintenance.

You collect IoT sensor data from 100 industrial machines for a year. Each machine has 50 different sensors that generate data at one-minute intervals. In total, you have 5,000 time series datasets.

You need to identify unusual values in each time series to help predict machinery failures.

Which Azure Cognitive Services service should you use?

AAnomaly Detector

BCognitive Search

CForm Recognizer

DCustom Vision

Answer : A

Question 4

You build a Language Understanding model by using the Language Understanding portal.

You export the model as a JSON file as shown in the following sample.

To what does the Weather.Historic entity correspond in the utterance?

Aby month

Bchicago

Crain

Dlocation

Answer : A

Question 5

You are developing the smart e-commerce project.

You need to implement autocompletion 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.

AMake API queries to the autocomplete endpoint and include suggesterName in the body.

BAdd a suggester that has the three product name fields as source fields.

CMake API queries to the search endpoint and include the product name fields in the searchFields query parameter.

DAdd a suggester for each of the three product name fields.

ESet the searchAnalyzer property for the three product name variants.

FSet the analyzer property for the three product name variants.

FUse the default standard Lucene analyzer ('analyzer': null) or a language analyzer (for example, 'analyzer': 'en.Microsoft') on the field.

Answer : A, B, F, F

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?search&api-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', 'seatt', and 'seattl' to support typeahead.

https://docs.microsoft.com/en-us/azure/search/index-add-suggesters

Question 6

You are developing the knowledgebase by using Azure Cognitive Search.

You need to process wiki content to meet the technical requirements.

What should you include in the solution?

Aan indexer for Azure Blob storage attached to a skillset that contains the language detection skill and the text translation skill

Ban indexer for Azure Blob storage attached to a skillset that contains the language detection skill

Can indexer for Azure Cosmos DB attached to a skillset that contains the document extraction skill and the text translation skill

Dan indexer for Azure Cosmos DB attached to a skillset that contains the language detection skill and the text translation skill

Answer : C

The wiki contains text in English, French and Portuguese.

Scenario: All planned projects must support English, French, and Portuguese.

The Document Extraction skill extracts content from a file within the enrichment pipeline. This allows you to take advantage of the document extraction step that normally happens before the skillset execution with files that may be generated by other skills.

Note: The Translator Text API will be used to determine the from language. The Language detection skill is not required.

https://docs.microsoft.com/en-us/azure/search/cognitive-search-skill-document-extraction

https://docs.microsoft.com/en-us/azure/search/cognitive-search-skill-text-translation

Question 7

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?

ACreate a person model and associate the model to the videos.

BCreate person objects and provide face images for each object.

CInvite the entire staff of the company to Video Indexer.

DEdit the faces in the videos.

EUpload names to a language model.

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 source 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.

https://docs.microsoft.com/en-us/azure/media-services/video-indexer/customize-person-model-with-api

Microsoft AI-102 Designing and Implementing a Microsoft Azure AI Solution Exam Practice Test