Free Microsoft AI-102 Exam Dumps Questions

- (Exam Topic 2)
You need to implement a table projection to generate a physical expression of an Azure Cognitive Search index.
Which three properties should you specify in the skillset definition JSON configuration table node? Each correct answer presents part of the solution. (Choose three.)
NOTE: Each correct selection is worth one point.

Correct Answer:ABE
Defining a table projection.
Each table requires three properties:
tableName: The name of the table in Azure Storage.
generatedKeyName: The column name for the key that uniquely identifies this row.
source: The node from the enrichment tree you are sourcing your enrichments from. This node is usually the output of a shaper, but could be the output of any of the skills.
Reference:
https://docs.microsoft.com/en-us/azure/search/knowledge-store-projection-overview

- (Exam Topic 2)
You have the following data sources:
Finance: On-premises Microsoft SQL Server database
Sales: Azure Cosmos DB using the Core (SQL) API
Logs: Azure Table storage
HR: Azure SQL database
You need to ensure that you can search all the data by using the Azure Cognitive Search REST API. What should you do?

Correct Answer:B
On-premises Microsoft SQL Server database cannot be used as an index data source.
Note: Indexer in Azure Cognitive Search: : Automate aspects of an indexing operation by configuring a data source and an indexer that you can schedule or run on demand. This feature is supported for a limited number of data source types on Azure.
Indexers crawl data stores on Azure.
Azure Blob Storage
Azure Data Lake Storage Gen2 (in preview)
Azure Table Storage
Azure Cosmos DB
Azure SQL Database
SQL Managed Instance
SQL Server on Azure Virtual Machines Reference:
https://docs.microsoft.com/en-us/azure/search/search-indexer-overview#supported-data-sources

- (Exam Topic 1)
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.

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?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.
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

- (Exam Topic 2)
You need to measure the public perception of your brand on social media messages. Which Azure Cognitive Services service should you use?

Correct 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:

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

- (Exam Topic 2)
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.

Solution:
Box 1: detect
Face - Detect With Url: Detect human faces in an image, return face rectangles, and optionally with faceIds, landmarks, and attributes.
POST {Endpoint}/face/v1.0/detect Box 2: matchPerson
Find similar has two working modes, "matchPerson" and "matchFace". "matchPerson" is the default mode that it 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 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

Does this meet the goal?

Correct Answer:A

- (Exam Topic 2)
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.)

Solution:
Step 1: Export the model using the Export for containers (GZIP) option. Export versioned app's package from LUIS portal
The versioned app's package is available from the Versions list page.
Sign on to the LUIS portal.
Select the app in the list.
Select Manage in the app's navigation bar.
Select Versions in the left navigation bar.
Select the checkbox to the left of the version name in the list.
Select the Export item from the contextual toolbar above the list.
Select Export for container (GZIP).
The package is downloaded from the browser.

Step 2: Select v1.1 of app1.
A trained or published app packaged as a mounted input to the container with its associated App ID. Step 3: Run a contain and mount the model file.
Run the container, with the required input mount and billing settings. Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/luis-container-howto

Does this meet the goal?

Correct Answer:A