Microsoft Designing and Implementing a Microsoft Azure AI Solution (AI-102 Korean Version) - AI-102 Korean무료 덤프문제 풀어보기

Explanation:

The code creates a SpeechConfig and an AudioOutputConfig with filename= " path/to/file.wav " , then constructs a SpeechSynthesizer and calls speak_text_async(input) .
Existing file behavior: AudioOutputConfig with a filename directs synthesis output to that file path; the documentation emphasizes that it specifies the output file and that the parent directory must already exist. It does not require a unique filename (i.e., it does not fail purely because a file exists). In practice, the SDK writes to the specified path. Therefore, the statement claiming failure if a file already exists is No.
Voice source: The filename in AudioOutputConfig is output, not input. To choose a voice, you set speech_config.speech_synthesis_voice_name or use SSML; the WAV file isn't "sampled" to create a voice.
Hence this statement is No. Microsoft Learn
File generation: Using AudioOutputConfig(filename=...) with SpeechSynthesizer and speak_text_async(...) writes synthesized audio to that file. Many samples show the same pattern (often with .get() to wait for completion). So the function's purpose is to generate an audio file from the input text-Yes.
Note: In production, call .get() (or await) on speak_text_async(...) so the program waits for synthesis to complete before exiting. Microsoft Learn References (Microsoft Azure AI Speech) How to synthesize speech from text (create AudioOutputConfig to write to a file; synthesize with speak_text_async ).
Python AudioConfig class (audio output can be a speaker, audio file (WAV), or stream). Microsoft Learn NET AudioConfig.FromWavFileOutput (specifies output file; parent directory must already exist).
Microsoft Learn
Azure Q & A example (Python: speak_text_async(...).get() usage). Microsoft Learn

Explanation:

You have press releases stored as PDF files. The task involves two parts:
Extract text
The correct service for OCR and text extraction from images and PDFs is Azure Computer Vision (specifically, the OCR and Read API).
While Form Recognizer can also extract text, it is primarily used for structured documents like invoices, receipts, or forms. Since the documents here are press releases (unstructured text), the best service is Computer Vision.
Perform sentiment analysis
Sentiment analysis falls under natural language processing (NLP).
The correct service is Azure AI Language (Text Analytics API).
It provides sentiment analysis, opinion mining, key phrase extraction, entity recognition, and more.
Why not other options?
Azure Cognitive Search is used for search indexing, not OCR or sentiment.
Form Recognizer specializes in structured document extraction, not sentiment analysis.
Computer Vision cannot perform sentiment analysis (only visual/text extraction).

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Explanation:

Box 1: Azure Blob storage
At the start of the pipeline, you have unstructured text or non-text content (such as images, scanned documents, or JPEG files). Data must exist in an Azure data storage service that can be accessed by an indexer.
Box 2: Computer Vision API
Scenario: Provide users with the ability to search insight gained from the images, manuals, and videos associated with the products.
The Computer Vision Read API is Azure ' s latest OCR technology (learn what ' s new) that extracts printed text (in several languages), handwritten text (English only), digits, and currency symbols from images and multi-page PDF documents.
Box 3: Translator API
Scenario: Product descriptions, transcripts, and all text must be available in English, Spanish, and Portuguese.
Box 4: Azure Files
Scenario: Store all raw insight data that was generated, so the data can be processed later.
Reference:
https://docs.microsoft.com/en-us/azure/search/cognitive-search-concept-intro
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/overview-ocr

Explanation:

Scenario Recap
You are building a remote training monitoring solution.
Requirement: Use video and audio feeds to detect if a learner is present, paying attention, and talking.
Services available: Face, Speech, Text Analytics.
Analysis
From a learner's video feed, verify whether the learner is present.
The Face API can detect and identify faces in a video feed.
It can tell if a person is present and recognized, fulfilling the requirement.
From a learner's facial expression in the video feed, verify whether the learner is paying attention.
Again, the Face API provides facial expression and emotion recognition (happiness, anger, neutral, etc.).
This can be mapped to "paying attention vs. distracted."
From a learner's audio feed, detect whether the learner is talking.
The Speech service detects spoken input and can determine if speech is present.
Text Analytics works on text (not raw audio) and is therefore not appropriate here.
Final Answer (Answer Area Selections)
From a learner's video feed, verify whether the learner is present: Face From a learner's facial expression in the video feed, verify whether the learner is paying attention: Face From a learner's audio feed, detect whether the learner is talking: Speech Microsoft References Face API - Face detection & identification Face API - Emotion recognition Azure Speech service

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Explanation:

The code snippet shows a helper function provision_resource that provisions Azure Cognitive Services accounts by specifying:
Name (resource name)
Kind (the type of Cognitive Service, e.g., ComputerVision, FormRecognizer, etc.) Location (Azure region, such as eastus) SKU tier (pricing tier, e.g., F0 = free, S0 = standard) Step 1 - Identify the service The dropdown includes ComputerVision , CustomVision.Prediction , CustomVision.Training , and FormRecognizer .
The question context indicates a need to provision an Azure AI service resource, and the answer area highlights the FormRecognizer option. This aligns with document processing scenarios requiring Azure AI Document Intelligence (formerly Form Recognizer).
Step 2 - Select pricing tier and location
The second dropdown shows combinations of pricing and regions:
" eastus " , " S1 "
" useast " , " S1 "
" S0 " , " eastus "
" S0 " , " useast "
Azure AI Form Recognizer (Document Intelligence) supports S0 (Standard) and F0 (Free) SKUs in common regions like East US. " S0 " , " eastus " is a valid and recommended configuration.
Other options like " S1 " are not correct here, as Form Recognizer uses S0/F0, not S1.
The answer:
Service Kind: FormRecognizer
Location and SKU: " S0 " , " eastus "
Microsoft References
Azure AI Document Intelligence (Form Recognizer) documentation
Azure Cognitive Services resource creation parameters
Pricing tiers for Form Recognizer

Explanation:

You are adding a custom plugin to a Semantic Kernel-based agent and you want function calling to be enforced and self-completing:
To register a plugin that exposes one or more callable functions to the agent, use CreatePluginFromFunctions . This builds a plugin from Kernel functions (prompt- or code-based) and makes them available for tool/function calling by the model.
To ensure the model must call functions (and request any required parameters it doesn't have), set the prompt execution settings to FunctionChoiceBehaviour.Required . In Semantic Kernel, this corresponds to the OpenAI tool/function-calling control that forces the assistant to invoke appropriate functions. When required parameters are marked in the function's schema, the assistant will ask follow-up questions to collect them before (or while) invoking the function.
References (Microsoft Azure AI Solution)
Semantic Kernel: Plugins and Functions, ChatCompletionAgent, OpenAIPromptExecutionSettings, and FunctionChoiceBehavior for enforced tool/function calling.

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Explanation:

Comprehensive Detailed Explanation
You are tasked with building a solution using Custom Vision to detect faults in factory components. Custom Vision follows a defined workflow:
Create a project
Before anything else, you must create a Custom Vision project in the Azure AI portal.
This project specifies the type of model you are building: classification (classifying entire images) or object detection (locating items within images).
Since the question is about detecting faults in specific components (a business-specific scenario), it aligns with classification unless explicitly stated otherwise.
Upload and tag images
You must provide training data.
Upload a set of images showing both faulty and non-faulty components.
Tag each image correctly so that the model can learn from labeled examples.
Train the classifier model
Once images are tagged, you run training on the dataset to generate the classifier model.
The model can then be published and used for inference via API calls.
Why not the other options?
Initialize the training dataset: This is not a direct step in Custom Vision; instead, uploading and tagging images inherently prepares the training dataset.
Train the object detection model: Object detection is used if you need bounding boxes around items within an image. Since the question is about recognizing faults in a component (faulty vs non-faulty), a classifier model is appropriate.
Correct Sequence:
Create a project
Upload and tag images
Train the classifier model
Microsoft References
Quickstart: Build an image classification project with the Custom Vision portal Custom Vision overview Train and test a Custom Vision model

Explanation:

Explanation: