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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement Knowledge Mining and Document Intelligence Solutions | 15-20% | - Design document processing pipelines - Implement semantic and vector search - Create custom skills for Azure AI Search - Implement Azure AI Search solutions |
| Topic 2: Implement Natural Language Processing Solutions | 20-25% | - Create conversational AI solutions with Azure AI Bot Service - Implement speech capabilities using Azure AI Speech - Build question answering solutions - Translate text and speech - Analyze text using Azure AI Language |
| Topic 3: Implement Computer Vision Solutions | 15-20% | - Process video and generate insights - Implement Azure AI Face service - Read and process documents using Azure AI Document Intelligence - Analyze images using Azure AI Vision - Implement object detection and image classification |
| Topic 4: Implement Generative AI Solutions | 10-15% | - Implement prompt engineering techniques - Use Azure OpenAI Service for generative AI - Apply responsible AI practices - Integrate Azure OpenAI models into applications |
| Topic 5: Plan and Manage an Azure AI Solution | 15-20% | - Manage AI costs and resource allocation - Implement governance and compliance for AI solutions - Plan and configure secure AI solutions - Select appropriate Azure AI services and technologies |
| Topic 6: Implement Decision Support Solutions | 10-15% | - Monitor and optimize decision support solutions - Integrate Azure AI services into applications - Design and implement decision automation |
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NEW QUESTION # 329
You are developing an application that includes language translation.
The application will translate text retrieved by using a function named get_text_to_be_translated. The text can be in one of many languages. The content of the text must remain within the Americas Azure geography.
You need to develop code to translate the text to a single language.
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:
NEW QUESTION # 330
You develop a test method to verify the results retrieved from a call to the Computer Vision API. The call is used to analyze the existence of company logos in images. The call returns a collection of brands named brands.
You have the following code segment.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-detecting-faces
NEW QUESTION # 331
You need to upload speech samples to a Speech Studio project. How should you upload the samples?
Answer: A
Explanation:
When working with Speech Studio in Azure AI Speech (for example, to build a custom speech model for recognition or training purposes), you must provide speech samples along with their transcripts.
* Audio File Format
* The supported format is .wav (PCM, 16-bit, 16 kHz or 8 kHz mono).
* Other formats like .wma, .flac, or .mp3 are not accepted for training custom models.
* Batch Upload via .zip
* Audio files must be placed in a .zip archive.
* The .zip file must also include a transcription file (usually a .txt or .json file) that maps the spoken audio in each .wav file to the correct text.
* Transcript Format
* Transcripts must be plain text, not Microsoft Word documents.
* The transcript must align correctly with each audio file to ensure the Speech service learns from accurate mappings.
Requirements for Uploading Speech Samples:
* A. Combine into one .wma file
* Incorrect. .wma is not supported. Also, Speech Studio expects multiple .wav samples, not one big file.
* B. Upload a .zip file containing .wav files + transcript
* Correct. This is the officially supported method for uploading training data into a Speech Studio project.
* C. Upload .flac files with Word transcript
* Incorrect. .flac is not supported. Transcripts must be plain text, not Word files.
* D. Upload individual .wma files
* Incorrect. .wma is not supported.
Option Analysis
B). Upload a .zip file that contains a collection of audio files in the .wav format and a corresponding text transcript file.
* Prepare training data for Custom Speech
* Speech Studio - Data Requirements
Microsoft References
NEW QUESTION # 332
You have an Azure subscription that contains an Azure OpenAI resource named All and an Azure Al Content Safety resource named CS1.
You build a chatbot that uses All to provide generative answers to specific questions and CSl to check input and output for objectionable content.
You need to optimize the content filter configurations by running tests on sample questions.
Solution: From Content Safety Studio, you use the Monitor online activity feature to run the tests Does this meet the requirement?
Answer: B
Explanation:
"Monitor online activity" in Content Safety Studio shows usage and moderation results and allows viewing category distributions; it's meant for observability of live traffic, not for running offline tests on sample questions to tune filter settings. Hence, it doesn't meet the requirement. Content Safety Studio References
* Azure AI Content Safety - Protected material detection concepts & quickstart.
* Azure OpenAI - System message (metaprompt) guidance. Microsoft Learn
* Content Safety Studio - Monitor online activity page. Content Safety Studio
* Azure OpenAI content filtering system and configuration overview.
NEW QUESTION # 333
You have an Azure subscription that contains an Azure Al Foundry Content Safety resource named resource1.
You are building an app that will analyze text by using resource1.
You need to identify text that contains hateful content.
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:
To detect hateful content with Azure AI Foundry Content Safety - Text , you should read the category analysis results returned by the analyze_text call. The response exposes a collection named categories_analysis, where each item contains the analyzed category (e.g., TextCategory.HATE) and its severity level.
Hence:
Iterate over response.categories_analysis, filter the item where item.category == TextCategory.HATE.
Print the severity using hate_result.severity.
This uses category analysis (not blocklist matches), which is the correct mechanism for detecting hate content with a severity score.
Microsoft References
Azure AI Content Safety - Analyze text (Python quickstart/samples): iterate response.categories_analysis, check TextCategory.HATE, read item.severity.
Azure AI Content Safety - Text categories and severity levels: explains categories (Hate, Violence, Sexual, Self-harm) and severity output.
NEW QUESTION # 334
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