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| Section | Weight | Objectives |
|---|---|---|
| Implement generative AI solutions | 15-20% | - Orchestrate multiple models and containers - Apply prompt engineering and fine-tuning - Deploy and manage generative models - Implement model monitoring and feedback - Integrate Azure OpenAI and other generative models |
| Implement computer vision solutions | 10-15% | - Integrate vision capabilities into applications - Build and deploy custom vision models - Process and index video content - Extract text and handwriting from images - Analyze images and detect objects/features |
| Implement knowledge mining and information extraction solutions | 15-20% | - Extract entities, relationships, and key phrases - Build knowledge bases and search indexes - Ingest and process structured/unstructured data - Implement intelligent search and retrieval |
| Implement an agentic solution | 5-10% | - Understand agent use cases and types - Develop multi-agent workflows and orchestration - Build agents with Microsoft Foundry Agent Service - Test, deploy, and optimize agents |
| 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 |
| Plan and manage an Azure AI solution | 20-25% | - Select suitable AI models - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining - Create and configure Azure AI resources - Monitor, optimize, and secure AI solutions - Select appropriate Microsoft Foundry Services - Plan solutions aligned with responsible AI principles |
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NEW QUESTION # 338
You are developing a method that uses the Computer Vision client library. The method will perform optical character recognition (OCR) in images. The method has the following code.
During testing, you discover that the call to the GetReadResultAsync method occurs before the read operation is complete.
You need to prevent the GetReadResultAsync method from proceeding until the read operation is complete.
Which two actions should you perform? Each correct answer presents part of the solution. (Choose two.) NOTE: Each correct selection is worth one point.
Answer: C,D
Explanation:
Example code :
do
{
results = await client.GetReadResultAsync(Guid.Parse(operationId));
}
while ((results.Status == OperationStatusCodes.Running ||
results.Status == OperationStatusCodes.NotStarted));
Reference:
https://github.com/Azure-Samples/cognitive-services-quickstart-code/blob/master/dotnet/ComputerVision/Comp
NEW QUESTION # 339
You are building a chatbot that will use question answering in Azure Cognitive Service for Language.
You have a PDF named Docl.pdf that contains a product catalogue and a price list You upload Docl.pdf and train the model.
During testing, users report that the chatbot responds correctly to the following question: What is the price of
< product>?
The chatbot fails to respond to the following question: How much does <product* cost?
You need to ensure that the chatbot responds correctly to both questions.
Solution: From Language Studio, you create an entity for cost, and then retrain and republish the model.
Does this meet the goal?
Answer: B
Explanation:
In Azure Cognitive Service for Language (Question Answering), when users ask natural language questions, the service tries to match them to knowledge base content and synonyms.
* In the scenario, the model correctly answers "What is the price of <product>?" but fails on "How much does <product> cost?".
* This indicates that the service does not automatically recognize "price" and "cost" as equivalent terms.
* Creating an entity for cost will not fix this issue. Entities in custom text classification or conversational language understanding are used for extracting structured data (like dates, names, or locations) from text, not for synonym matching in question answering.
The correct solution is to add alternate phrasing (synonyms/alternate questions) in the QnA project for each knowledge base entry. For example, under the QnA pair that contains "What is the price of <product>?", you would add "How much does <product> cost?" as an alternate question. This way, both variations trigger the same answer.
Therefore, the proposed solution (create an entity for cost) does not meet the goal.
The answer: B. No
* Add alternative questions to improve matching - Azure AI Language Question Answering
* Improve accuracy with synonyms and alternate phrasing
* Overview of Question Answering in Azure AI Language
NEW QUESTION # 340
You have an Azure subscription that contains an Azure Al Search resource named AS1.
You implement a custom skill in AS1 that performs language and sentiment analysis of documents.
You ate evaluating the use of AS1 as part of an enrichment pipeline.
In which order will AS1 index the documents? To answer, move all indexing stages from the list of stages to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation:
Comprehensive Detailed Explanation
When an indexer runs in Azure AI Search, it processes each document through a fixed series of stages:
Document cracking
The indexer opens the item (file/row) and extracts text, images, and metadata, creating the root of the enriched document (/document). This is always the first stage.
Field mappings
Next, source fields are mapped directly to target index fields before any transformations . Field mappings occur after document cracking but before skillset execution . Microsoft Learn Skillset execution Built-in or custom skills (for example, language and sentiment analysis in your scenario) run and write outputs into the in-memory enriched document. Microsoft Learn Output field mappings Because skill outputs live only in memory, you must define output field mappings to route selected nodes of the enriched document into index fields. This step comes after skillset execution.
Push to index
Finally, the indexer hands off raw and enriched content to the search index (the physical data structures) so it becomes searchable-i.e., the content is "pushed to the index." Microsoft Learn This order- document cracking # field mappings # skillset execution # output field mappings # push to index -ensures raw content is extracted, direct source-to-index mappings are applied, AI enrichment is performed, enriched outputs are projected to fields, and the completed documents are written into the index.
Microsoft Azure AI References
Indexers in Azure AI Search - Stages of indexing (Document cracking; Field mappings; Skillset execution; Output field mappings, with field mappings occurring before transformations). Microsoft Learn Skillset concepts (Output of skillset is routed to the index via output field mappings after skillset execution).
Microsoft Learn
AI enrichment concepts (Field mappings move raw source content to the index; output field mappings move enriched content; indexing ingests raw and enriched content into the physical index). Microsoft Learn Annotations & enriched document tree (Enriched document is created during document cracking at
/document). Microsoft Learn
NEW QUESTION # 341
You are developing a service that records lectures given in English (United Kingdom).
You have a method named AppendToTranscriptFile that takes translated text and a language identifier.
You need to develop code that will provide transcripts of the lectures to attendees in their respective language.
The supported languages are English, French, Spanish, and German.
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
Box 1: {"fr", "de", "es"}
A common task of speech translation is to specify target translation languages, at least one is required but multiples are supported. The following code snippet sets both French and German as translation language targets.
static async Task TranslateSpeechAsync()
{
var translationConfig =
SpeechTranslationConfig.FromSubscription(SPEECH__SUBSCRIPTION__KEY,
SPEECH__SERVICE__REGION);
translationConfig.SpeechRecognitionLanguage = "it-IT";
// Translate to languages. See, https://aka.ms/speech/sttt-languages
translationConfig.AddTargetLanguage("fr");
translationConfig.AddTargetLanguage("de");
}
Box 2: TranslationRecognizer
After you've created a SpeechTranslationConfig, the next step is to initialize a TranslationRecognizer.
Example code:
static async Task TranslateSpeechAsync()
{
var translationConfig =
SpeechTranslationConfig.FromSubscription(SPEECH__SUBSCRIPTION__KEY,
SPEECH__SERVICE__REGION);
var fromLanguage = "en-US";
var toLanguages = new List<string> { "it", "fr", "de" };
translationConfig.SpeechRecognitionLanguage = fromLanguage;
toLanguages.ForEach(translationConfig.AddTargetLanguage);
using var recognizer = new TranslationRecognizer(translationConfig);
}
NEW QUESTION # 342
Hotspot Question
You have a Computer Vision resource named contoso1 that is hosted in the West US Azure region.
You need to use contoso1 to make a different size of a product photo by using the smart cropping feature.
How should you complete the API URL? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: https://westus.api.cognitive.microsoft.com
Box 2: generate Thumbnail
GenerateThumbnail takes in Height Width and SmartCropping = True/false and returns the actual binary image data.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-generating- thumbnails
https://docs.microsoft.com/en-us/rest/api/computervision/3.1/generate-thumbnail/generate- thumbnail#examples
NEW QUESTION # 343
......
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