Correct Answer:

In user want to change to deploy offline model

1.

Change model domain to compact model

2.

Retrain compact model

3.

Export model

Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/export-your-model

Correct Answer:

Box 1: Yes

http://localhost:5000/status : Also requested with GET, this verifies if the api-key used to start the container is valid without causing an endpoint query.

Box 2: Yes

The command saves container and LUIS logs to output mount at C:\output, located on container host

Box 3: Yes

http://localhost:5000/swagger : The container provides a full set of documentation for the endpoints and a Try it out feature. With this feature, you can enter your settings into a web-based HTML form and make the query without having to

write any code. After the query returns, an example CURL command is provided to demonstrate the HTTP headers and body format that's required.

Reference:

https://docs.microsoft.com/en-us/azure/cognitive-services/luis/luis-container-howto

Correct Answer:

Box 1: QnA Maker Editor

Scenario: Provide all employees with the ability to edit QandAs.

The QnA Maker Editor (read/write) has the following permissions:

Create KB API

Update KB API

Replace KB API

Replace Alterations

"Train API" [in new service model v5]

Box 2: Contributor

Scenario: Only senior managers must be able to publish updates.

Contributor permission: All except ability to add new members to roles

Reference:

https://docs.microsoft.com/en-us/azure/cognitive-services/qnamaker/reference-role-based-access-control

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 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.

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

Correct Answer: AB

Correct Answer: A

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

Correct Answer: B

Correct Answer: B

Instead, you could migrate to a Cognitive Search service that uses a higher tier.

Note: 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

Correct Answer: D

Technical Requirements says "AI solution responses must have a confidence score that is equal to or greater than 70 percent" and "When the response confidence score is low, ensure that the chatbot can provide other response options to the customers" https://docs.microsoft.com/en-us/azure/cognitive-services/qnamaker/concepts/confidence-score#set-threshold

Correct Answer: A

https://learn.microsoft.com/en-us/azure/azure-video-indexer/customize-language-model-overview Azure Video Indexer supports automatic speech recognition through integration with the Microsoft Custom Speech Service. You can customize the Language model by uploading adaptation text, namely text from the domain whose vocabulary you'd like the engine to adapt to. Once you train your model, new words appearing in the adaptation text will be recognized, assuming default pronunciation, and the Language model will learn new probable sequences of words.