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| Section | Weight | Objectives |
|---|---|---|
| Implement Knowledge Mining and Document Intelligence Solutions | 15-20% | - Design document processing pipelines - Implement Azure AI Search solutions - Create custom skills for Azure AI Search - Implement semantic and vector search |
| Plan and Manage an Azure AI Solution | 15-20% | - Plan and configure secure AI solutions - Manage AI costs and resource allocation - Implement governance and compliance for AI solutions - Select appropriate Azure AI services and technologies |
| Implement Generative AI Solutions | 10-15% | - Use Azure OpenAI Service for generative AI - Integrate Azure OpenAI models into applications - Implement prompt engineering techniques - Apply responsible AI practices |
| Implement Decision Support Solutions | 10-15% | - Monitor and optimize decision support solutions - Integrate Azure AI services into applications - Design and implement decision automation |
| Implement Natural Language Processing Solutions | 20-25% | - Create conversational AI solutions with Azure AI Bot Service - Build question answering solutions - Translate text and speech - Analyze text using Azure AI Language - Implement speech capabilities using Azure AI Speech |
| Implement Computer Vision Solutions | 15-20% | - Process video and generate insights - Read and process documents using Azure AI Document Intelligence - Implement Azure AI Face service - Analyze images using Azure AI Vision - Implement object detection and image classification |
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NEW QUESTION # 156
You are developing a text processing solution.
You have the function shown below.
Answer:
Explanation:
Explanation:
The output will include the following words: our and included. # No
The output will include the following words: Paris, Eiffel, and Tower. # Yes The function will output all the key phrases from the input string to the console. # No Comprehensive Detailed Explanation Let's examine the function:
static void GetKeyWords ( TextAnalyticsClient textAnalyticsClient, string text)
{
var response = textAnalyticsClient.RecognizeEntities(text);
Console.WriteLine( " Key words: " );
foreach (CategorizedEntity entity in response.Value)
{
Console.WriteLine( $ " {entity.Text} " );
}
}
The function uses RecognizeEntities from the Azure Text Analytics SDK.
RecognizeEntities detects and categorizes named entities in text (such as locations, people, organizations, dates, etc.).
It does not extract all words or key phrases.
For extracting key phrases, you would use ExtractKeyPhrases , not RecognizeEntities .
Evaluating the statements:
" The output will include the following words: our and included. "
These are common words, not entities. They will not appear.
The Answer: No
" The output will include the following words: Paris, Eiffel, and Tower. " These are named entities (location, landmark). They will be recognized and returned.
The Answer: Yes
" The function will output all the key phrases from the input string to the console. " No. The function extracts entities, not key phrases. Key phrases would require ExtractKeyPhrases .
The Answer: No
NEW QUESTION # 157
You have 100 chatbots that each has 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.
Answer:
Explanation:
Explanation:
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
NEW QUESTION # 158
You are developing an app that will use the Azure AI vision API to analyze an image.
You need configure the request that will be used by the app to identity whether an image is clipart or a line drawing.
How should you complete the request? To answer. Select the appropriate options in the answer area.
NOTE: Each correct select is worth one point.
Answer:
Explanation:
NEW QUESTION # 159
You have an Azure subscription that contains an Azure Al Content Safety resource named CS1.
You create a test image that contains a circle.
You submit the test image to CS1 by using the curl command and the following command-line parameters.
What should you expect as the output?
Answer: B
Explanation:
You are analyzing an image with Azure AI Content Safety using the following request payload:
{
" image " : {
" content " : " < base_64_string > "
},
" categories " : [
" Violence "
],
" outputType " : " EightSeverityLevels "
}
Key Points:
Category specified # " Violence "
The Content Safety API will check for violent content in the image.
Test image description # A simple circle.
This is not violent in any way.
Output type # " EightSeverityLevels "
This setting returns a floating-point severity score from 0.0 (no severity) up to 7.0 (highest severity).
Expected result:
Since the image is only a circle (non-violent), the violence severity level will be at the lowest possible value:
0.0
The answer: B. 0.0
Microsoft References
Azure AI Content Safety - Image moderation
Content Safety severity levels
NEW QUESTION # 160
You use the Custom Vision service to build a classifier.
After training is complete, you need to evaluate the classifier.
Which two metrics are available for review? Each correct answer presents a complete solution. (Choose two.) NOTE: Each correct selection is worth one point.
Answer: A,B
Explanation:
Explanation
Custom Vision provides three metrics regarding the performance of your model: precision, recall, and AP.
Reference:
https://www.tallan.com/blog/2020/05/19/azure-custom-vision/
NEW QUESTION # 161
......
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