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| Section | Objectives |
|---|---|
| Topic 1: Implement Computer Vision Solutions | - Implement custom vision models - Analyze images and videos using Azure AI Vision - Extract information using OCR and form recognition |
| Topic 2: Implement Natural Language Processing Solutions | - Translate and interpret language using Azure AI services - Analyze text using Azure AI Language - Implement question answering and conversational AI |
| Topic 3: Implement Knowledge Mining and Document Intelligence | - Enrich and index data for search scenarios - Build Azure AI Search solutions - Implement document intelligence solutions |
| Topic 4: Implement Generative AI Solutions | - Build retrieval-augmented generation (RAG) solutions - Implement Azure OpenAI Service solutions - Integrate prompt engineering techniques |
| Topic 5: Plan and Manage an Azure AI Solution | - Plan solution requirements and architecture - Manage security, compliance, and responsible AI - Select appropriate Azure AI services |
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NEW QUESTION # 37
You have a product knowledgebase that contains multiple PDF documents.
You need to build a chatbot that will provide responses based on data in the knowledgebase. The solution must minimize development effort and costs.
What should you include in the solution?
Answer: B
Explanation:
The requirement: A chatbot that answers based on a product knowledgebase (multiple PDFs) .
Best choice: Custom Question Answering in Azure AI Language .
It allows you to ingest documents like PDFs, FAQs, manuals.
Minimal development effort since you just upload documents and the service automatically indexes them.
Other options:
CLU # for intent/intent-based classification, not for knowledgebases.
Language detection # only detects language, not Q & A.
Azure OpenAI # could work, but more costly and requires prompt engineering (not the "minimize effort
/cost" requirement).
Microsoft Reference:
Custom question answering in Azure AI Language
NEW QUESTION # 38
You are designing a content management system.
You need to ensure that the reading experience is optimized for users who have reduced comprehensive and learning differences, such as dyslexia.
Which Azure service should you include in the solution?
Answer: D
Explanation:
* The requirement is to optimize the reading experience for users with dyslexia and learning differences
.
* Azure AI Immersive Reader is specifically designed to make text more accessible, improving comprehension by enabling features like text-to-speech, line focus, and translation.
* Translator (A) is for translating text, not accessibility.
* Document Intelligence (B) is for extracting information from documents.
* Azure AI Language (D) is for NLP tasks, not improving reading accessibility.
Microsoft References:
* Azure AI Immersive Reader overview
NEW QUESTION # 39
You are building an app that will answer customer calls about the status of an order. The app will query a database for the order details and provide the customers with a spoken response.
You need to identify which Azure Al service APIs to use. The solution must minimize development effort.
Which object should you use for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
For a phone-style bot that listens to customers and replies with spoken output, you use two core Azure AI Speech SDK objects:
* SpeechRecognizer performs speech-to-text (STT), turning the caller's audio into text that your app can use to query the orders database. This minimizes effort because it handles audio capture/streaming, language models, and returns recognized text events/results directly.
* SpeechSynthesizer performs text-to-speech (TTS), converting the retrieved order status text into natural- sounding audio to play back to the customer. It supports neural voices and simple calls like SpeakTextAsync() .
Other options are not appropriate here:
* TranslationRecognizer is for real-time speech translation, not needed for simple STT.
* VoiceProfileClient is for speaker verification/identification, not for recognition or synthesis of the conversation content.
Microsoft References
* Azure AI Speech SDK - Speech to text (SpeechRecognizer) overview and quickstarts.
* Azure AI Speech SDK - Text to speech (SpeechSynthesizer) overview and quickstarts.
* Azure AI Speech SDK concepts - recognition vs. synthesis pipelines.
NEW QUESTION # 40
You have the following C# function.
You call the function by using the following code.
Following 'Key phrases', what output will you receive?
Answer: C
Explanation:
TextAnalyticsClient.ExtractKeyPhrases Method
Runs a model to identify a collection of significant phrases found in the passed-in document.
For example, for the document "The food was delicious and there were wonderful staff", the API returns the main talking points: "food" and "wonderful staff".
NEW QUESTION # 41
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: B,E
Explanation:
The question is about evaluating a Custom Vision classifier after training.
When you train an image classifier in Azure Custom Vision, the service automatically calculates performance metrics to help you evaluate the quality of the model.
* Precision:
* The percentage of correct positive predictions out of all positive predictions made.
* Formula: Precision = TP / (TP + FP)
* Helps determine how reliable positive predictions are.
* Recall:
* The percentage of actual positives that were correctly predicted.
* Formula: Recall = TP / (TP + FN)
* Helps determine how many of the true positives were captured.
Metrics Provided by Custom Vision:These two metrics are explicitly shown in the Custom Vision portal and via API.
* A. Recall
* Yes, Custom Vision reports recall.
* Correct
* B. F-score
* Not directly reported in the Custom Vision portal.
* Although it can be derived from precision and recall, it is not provided as a direct metric.
* Incorrect
* C. Weighted accuracy
* Not reported by Custom Vision.
* Incorrect
* D. Precision
* Yes, Custom Vision reports precision.
* Correct
* E. Area under the curve (AUC)
* Not reported by Custom Vision.
* More common in ROC curve analysis, not part of Custom Vision output.
* Incorrect
Option Analysis
The answer: A. recall, D. precision
* Evaluate the prediction performance of your classifier in Custom Vision
* Custom Vision training and evaluation
NEW QUESTION # 42
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