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| Section | Objectives |
|---|---|
| Plan and Manage an Azure AI Solution | - Select appropriate Azure AI services - Plan solution requirements and architecture - Manage security, compliance, and responsible AI |
| Implement Knowledge Mining and Document Intelligence | - Build Azure AI Search solutions - Enrich and index data for search scenarios - Implement document intelligence solutions |
| Implement Generative AI Solutions | - Implement Azure OpenAI Service solutions - Build retrieval-augmented generation (RAG) solutions - Integrate prompt engineering techniques |
| Implement Computer Vision Solutions | - Extract information using OCR and form recognition - Analyze images and videos using Azure AI Vision - Implement custom vision models |
| Implement Natural Language Processing Solutions | - Implement question answering and conversational AI - Analyze text using Azure AI Language - Translate and interpret language using Azure AI services |
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NEW QUESTION # 27
You are building an app that uses a Language Understanding model to analyze text files. You need to ensure that the app can detect the following entities:
* Temperatures
* Currency values
* Email addresses
* Telephone numbers
The solution must minimize development effort.
Which model capability should you use?
Answer: D
Explanation:
To detect common data types such as temperatures, currency values, email addresses, and telephone numbers with minimal development effort, you should use pre-built entity components in Conversational Language Understanding (CLU). Prebuilt components are ready-made recognizers that automatically extract well- known patterns from utterances-no training, labeling, lists, or regex authoring required.
Specifically, CLU offers the following relevant prebuilt components:
* Quantity.Temperature - extracts temperatures (e.g., "34 degrees celsius").
* Quantity.Currency - extracts monetary amounts and currency units (e.g., "$20", "ยฃ15.50").
* Email - extracts email addresses.
* Phone Number - extracts phone numbers.
These components are designed to be dropped into your entities and immediately provide predictions, which minimizes development effort compared to:
* List entities (you must curate values/synonyms yourself),
* Learned entities (require labeling utterances and training),
* Regular expression components (you must craft and maintain regex patterns), or
* Utterances (training data, not a capability for detection by itself).
Microsoft References
* Supported prebuilt entity components in CLU (shows Quantity.Temperature, Quantity.Currency, Email, Phone Number, etc.). Microsoft Learn
* Entity components in CLU (explains prebuilt, learned, list, and regex components and why prebuilt components are automatically detected). Microsoft Learn
NEW QUESTION # 28
You are building a customer support chatbot.
You need to configure the bot to identify the following:
* Code names for internal product development
* Messages that include credit card numbers
The solution must minimize development effort.
Which Azure Cognitive Service for Language feature should you use for each requirement? To answer, drag the appropriate features to the correct requirements. Each feature 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:
The chatbot needs to recognize two different kinds of information:
* Code names for internal product development
* These are not standard entities like " locations " or " organizations " that prebuilt NER can detect.
* You need to train a custom model to recognize internal terms or codenames (for example, " Project Falcon " ).
* The correct Azure Cognitive Service for Language feature is Custom Named Entity Recognition (Custom NER), which allows defining and training entity categories specific to your business.
* Messages that include credit card numbers
* Credit card numbers are sensitive data falling under Personally Identifiable Information (PII).
* Azure Cognitive Service for Language provides a PII detection feature that automatically identifies and masks sensitive information such as credit card numbers, SSNs, and phone numbers.
* This minimizes development effort since it is prebuilt and ready to use.
Correct Answer Mapping:
* Identify code names for internal product development # Custom named entity recognition (NER)
* Identify messages that include credit card numbers # Personally Identifiable Information (PII) detection
* Custom Named Entity Recognition in Azure AI Language
* PII detection in Azure AI Language
Microsoft References
NEW QUESTION # 29
You plan to use a Language Understanding application named app1 that is deployed to a container.
App1 was developed by using a Language Understanding authoring resource named lu1.
App1 has the versions shown in the following table.
You need to create a container that uses the latest deployable version of app1.
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. (Choose three.)
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/luis-container-howto
NEW QUESTION # 30
You are building an app that will automatically translate speech from English to French, German, and Spanish by using Azure Al service.
You need to define the output languages and configure the Azure Al Speech service.
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 # 31
You build a language model by using Conversational Language Understanding. The language model is used to search for information on a contact list by using an intent named Findcontact. A conversational expert provides you with the following list of phrases to use for training
* Find contacts in London.
* Who do I know in Seattle?
* Search for contacts m Ukraine.
You need to implement the phrase list in Conversational Language Understanding.
Solution: You create a new utterance for each phrase in the FindContact intent.
Answer: B
Explanation:
* Conversational Language Understanding (CLU) is trained using utterances.
* Adding these example phrases as utterances for the FindContact intent is exactly how training is done.
* This ensures the model learns to recognize similar queries.
The answer: Yes
Reference: Train Conversational Language Understanding models
NEW QUESTION # 32
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