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| Section | Weight | Objectives |
|---|---|---|
| Implement knowledge mining and information extraction solutions | 15-20% | - Ingest and process structured/unstructured data - Build knowledge bases and search indexes - Extract entities, relationships, and key phrases - Implement intelligent search and retrieval |
| Implement computer vision solutions | 10-15% | - Extract text and handwriting from images - Analyze images and detect objects/features - Process and index video content - Build and deploy custom vision models - Integrate vision capabilities into applications |
| Implement natural language processing solutions | 15-20% | - Perform text analysis, sentiment detection, and language detection - Implement translation and summarization - Build conversational AI and chatbots - Customize and deploy NLP models |
| Implement an agentic solution | 5-10% | - Test, deploy, and optimize agents - Develop multi-agent workflows and orchestration - Build agents with Microsoft Foundry Agent Service - Understand agent use cases and types |
| Implement generative AI solutions | 15-20% | - Implement model monitoring and feedback - Integrate Azure OpenAI and other generative models - Apply prompt engineering and fine-tuning - Deploy and manage generative models - Orchestrate multiple models and containers |
| Plan and manage an Azure AI solution | 20-25% | - Monitor, optimize, and secure AI solutions - Select suitable AI models - Select appropriate Microsoft Foundry Services - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining - Plan solutions aligned with responsible AI principles - Create and configure Azure AI resources |
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NEW QUESTION # 161
You are developing an application that includes language translation.
The application will translate text retrieved by using a function named getTextToBeTranslated. 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:
Explanation:
NEW QUESTION # 162
You are developing a solution to generate a word cloud based on the reviews of a company's products.
Which Text Analytics REST API endpoint should you use?
Answer: B
Explanation:
Reference:
Comprehensive Detailed ExplanationA word cloud is typically generated by extracting the most relevant keywords or key phrases from text data.
The Key Phrases API of Text Analytics returns the important words and phrases in a set of documents.
You can then use those key phrases and their frequency counts to build a word cloud visualization.
Why not the others?
B). sentiment: Identifies sentiment (positive/negative/neutral), not useful for word cloud keywords.
C). languages: Detects the language of the text, not relevant here.
D). entities/recognition/general: Extracts named entities (like people, locations, organizations), but this is narrower than general key phrase extraction.
Reference:
Text Analytics - Key Phrase Extraction
NEW QUESTION # 163
You have an Azure subscription.
You plan to build a solution that will analyze scanned documents and export relevant fields to a database.
You need to recommend an Azure Al Document Intelligence model for the following types of documents:
. Expenditure request authorization forms
. Structured and unstructured survey forms
. Structured employment application forms
The solution must minimize development effort and costs.
Which of model should you recommend for each document type?
Answer:
Explanation:
Explanation:
To recommend the appropriate Azure AI Document Intelligence models for the given document types while minimizing development effort and costs, here are the choices for each document type:
* Expenditure request authorization forms:
* These forms are likely to be structured with specific fields that need to be extracted. Using a prebuilt model that can handle structured forms will minimize development effort.
* Recommended model: Prebuilt layout (This is because prebuilt layout models are designed to handle a wide range of structured documents.)
* Structured employment application forms:
* These forms are also structured with specific fields such as name, address, job position, etc. Using a prebuilt model that handles structured documents will be efficient.
* Recommended model: Prebuilt layout (Similar reasoning as above, for structured forms.)
* Structured and unstructured survey forms:
* Survey forms can be both structured and unstructured, meaning they may have free-text responses along with structured questions. For such varied content, a custom neural model that can handle both types of data might be the best fit.
* Recommended model: Custom neural (This allows flexibility to handle both structured and unstructured data within the survey forms.) Based on this analysis, the appropriate models are:
* Expenditure request authorization forms: Prebuilt layout
* Structured employment application forms: Prebuilt layout
* Structured and unstructured survey forms: Custom neural
NEW QUESTION # 164
You have a Conversational Language Understanding model.
You export the model as a JSON file. The following is a sample of the file.
What represents the Weather.Historic entity in the sample utterance?
Answer: A
NEW QUESTION # 165
You have an Azure subscription that contains an Azure Al Document Intelligence resource named Aldoc1 in the SO tier.
You have the lies shown in the following table.
You need to train a custom extraction model by using AIdoc1.
Which files can you upload to Document Intelligence Studio?
Answer: C
NEW QUESTION # 166
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