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Free AI-102 Exam Braindumps (page: 9)

Page 8 of 83
PDF with 329 Questions

You have a Language Understanding resource named lu1.
You build and deploy an Azure bot named bot1 that uses lu1.
You need to ensure that bot1 adheres to the Microsoft responsible AI principle of inclusiveness.
How should you extend bot1?

  1. Implement authentication for bot1.
  2. Enable active learning for lu1.
  3. Host lu1 in a container.
  4. Add Direct Line Speech to bot1.

Answer(s): D

Explanation:

Inclusiveness: AI systems should empower everyone and engage people.
Direct Line Speech is a robust, end-to-end solution for creating a flexible, extensible voice assistant. It is powered by the Bot Framework and its Direct Line
Speech channel, that is optimized for voice-in, voice-out interaction with bots.
Incorrect:
Not B: The Active learning suggestions feature allows you to improve the quality of your knowledge base by suggesting alternative questions, based on user- submissions, to your question and answer pair. You review those suggestions, either adding them to existing questions or rejecting them.


Reference:

https://docs.microsoft.com/en-us/azure/cognitive-services/speech-service/direct-line-speech



HOTSPOT (Drag and Drop is not supported)
You are building an app that will process incoming email and direct messages to either French or English language support teams.
Which Azure Cognitive Services API should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Hot Area:

  1. See Explanation section for answer.

Answer(s): A

Explanation:


Box 1: api/cognitive.microsofttranslator.com is used for translations.
Incorrect:
eastus.api.cognitive.microsoft.com is used for Face recognition.
Portal.azure.com is the URL of the Azure portal which is a web-based, unified console that provides an alternative to command-line tools. With the Azure portal, you can manage your Azure subscription using a graphical user interface. You can build, manage, and monitor everything from simple web apps to complex cloud deployments.
Box 2: /text/analytics/v3.1/entities/recognition/general
Named Entity Recognition
The API returns a list of general named entities in a given document.
Request URL: https://{endpoint}/text/analytics/v3.1/entities/recognition/general[?model-version][&showStats][&loggingOptOut][&stringIndexType]


Reference:

https://docs.microsoft.com/en-us/azure/cognitive-services/translator/reference/v3-0-translate https://westcentralus.dev.cognitive.microsoft.com/docs/services/TextAnalytics-v3-1/operations/EntitiesRecognitionGeneral



You have an Azure Cognitive Search instance that indexes purchase orders by using Form Recognizer.
You need to analyze the extracted information by using Microsoft Power BI. The solution must minimize development effort.
What should you add to the indexer?

  1. a projection group
  2. a table projection
  3. a file projection
  4. an object projection

Answer(s): B

Explanation:

Projections are the physical tables, objects, and files in a knowledge store that accept content from a Cognitive Search AI enrichment pipeline. If you're creating a knowledge store, defining and shaping projections is most of the work.
Objects is used when you need the full JSON representation of your data and enrichments in one JSON document. As with table projections, only valid JSON objects can be projected as objects, and shaping can help you do that.
Note: Form Recognizer analyzes your forms and documents, extracts text and data, maps field relationships as key-value pairs, and returns a structured JSON output. You quickly get accurate results that are tailored to your specific content without excessive manual intervention or extensive data science expertise.
Incorrect:
Not Tables: Tables is used for data that's best represented as rows and columns, or whenever you need granular representations of your data (for example, as data frames). Table projections allow you to define a schematized shape, using a Shaper skill or use inline shaping to specify columns and rows.
Not File: File is used when you need to save normalized, binary image files.


Reference:

https://docs.microsoft.com/en-us/azure/search/knowledge-store-projection-overview



Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You have an Azure Cognitive Search service.
During the past 12 months, query volume steadily increased.
You discover that some search query requests to the Cognitive Search service are being throttled.
You need to reduce the likelihood that search query requests are throttled.
Solution: You add replicas.
Does this meet the goal?

  1. Yes
  2. No

Answer(s): A

Explanation:

A simple fix to most throttling issues is to throw more resources at the search service (typically replicas for query-based throttling, or partitions for indexing-based throttling). However, increasing replicas or partitions adds cost, which is why it is important to know the reason why throttling is occurring at all.


Reference:

https://docs.microsoft.com/en-us/azure/search/search-performance-analysis






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