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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement Generative AI Solutions | 10-15% | - Integrate Azure OpenAI models into applications - Apply responsible AI practices - Implement prompt engineering techniques - Use Azure OpenAI Service for generative AI |
| Topic 2: Implement Natural Language Processing Solutions | 20-25% | - Analyze text using Azure AI Language - Translate text and speech - Create conversational AI solutions with Azure AI Bot Service - Implement speech capabilities using Azure AI Speech - Build question answering solutions |
| Topic 3: Implement Decision Support Solutions | 10-15% | - Monitor and optimize decision support solutions - Design and implement decision automation - Integrate Azure AI services into applications |
| Topic 4: Plan and Manage an Azure AI Solution | 15-20% | - Select appropriate Azure AI services and technologies - Plan and configure secure AI solutions - Implement governance and compliance for AI solutions - Manage AI costs and resource allocation |
| Topic 5: Implement Knowledge Mining and Document Intelligence Solutions | 15-20% | - Implement Azure AI Search solutions - Create custom skills for Azure AI Search - Design document processing pipelines - Implement semantic and vector search |
| Topic 6: 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 # 205
You are developing an application that will recognize faults in components produced on a factory production line. The components are specific to your business.
You need to use the Custom Vision API to help detect common faults.
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.
Answer:
Explanation:
Explanation
Graphical user interface, text, application, chat or text message Description automatically generated
Step 1: Create a project
Create a new project.
Step 2: Upload and tag the images
Choose training images. Then upload and tag the images.
Step 3: Train the classifier model.
Train the classifier
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/getting-started-build-a-classifie
NEW QUESTION # 206
You have data saved in the following format.
Which format was used?
Answer: D
NEW QUESTION # 207
You are developing a streaming Speech to Text solution that will use the Speech SDK and MP3 encoding.
You need to develop a method to convert speech to text for streaming MP3 data.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Scenario Recap You are building a streaming Speech-to-Text solution using the Speech SDK.
* Input audio format: MP3 encoding
* You must configure the audio stream format and the recognizer properly.
* Audio Format
* For MP3 streams, the correct method is:
* AudioStreamFormat.GetCompressedFormat(AudioStreamContainerFormat.MP3)
* This creates an audio format object for compressed MP3 input.
* Recognizer
* Since the task is speech-to-text transcription, you need the SpeechRecognizer class.
* Other options like KeywordRecognizer, SpeakerRecognizer, or SpeechSynthesizer are not appropriate (they handle keyword spotting, speaker identification, or text-to-speech).
Code Analysis
Completed Code Snippet var audioFormat = AudioStreamFormat.GetCompressedFormat (AudioStreamContainerFormat.MP3); var speechConfig = SpeechConfig.FromSubscription( " 18c51a87-3a69-47a8-aedc-a54745f708a1 " , " westus
" );
var audioConfig = AudioConfig.FromStreamInput(pushStream, audioFormat); using ( var recognizer = new SpeechRecognizer(speechConfig, audioConfig))
{
var result = await recognizer.RecognizeOnceAsync();
var text = result.Text;
}
* AudioFormat: AudioStreamFormat.GetCompressedFormat
* Recognizer: SpeechRecognizer
Final Answer (Answer Area Selections)
* Speech SDK - AudioStreamFormat
* SpeechRecognizer class
Microsoft References
NEW QUESTION # 208
You need to upload speech samples to a Speech Studio project. How should you upload the samples?
Answer: A
Explanation:
To upload your data, navigate to the Speech Studio . From the portal, click Upload data to launch the wizard and create your first dataset. You'll be asked to select a speech data type for your dataset, before allowing you to upload your data.
The default audio streaming format is WAV
Use this table to ensure that your audio files are formatted correctly for use with Custom Speech:
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/speech-service/how-to-custom-speech-test-and-train
NEW QUESTION # 209
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
Semi-structured data
Let's break down the options:
* Graph data
* Used in graph databases (e.g., Azure Cosmos DB Gremlin API, Neo4j).
* Represents nodes and edges with relationships.
* JSON is not graph data.
* Relational data
* Stored in relational databases with tables, rows, and columns.
* JSON does not follow a fixed schema with rows/columns.
* Semi-structured data
* Correct.
* JSON, XML, and Avro are common examples of semi-structured data.
* They don't require a rigid schema like relational data, but still have organizational elements (tags, key-value pairs).
* JSON is widely used in NoSQL/document databases like Azure Cosmos DB.
* Unstructured data
* Examples: images, videos, free text, audio.
* JSON does not fit here because it has a defined structure with keys and values.
The answer: Semi-structured data
* Structured, semi-structured, and unstructured data
* What is semi-structured data?
* Azure Cosmos DB and JSON data model
Microsoft References
NEW QUESTION # 210
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