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Microsoft AI-102 exam is designed for individuals who want to demonstrate their expertise in designing and implementing AI solutions on the Microsoft Azure platform. AI-102 exam is suitable for professionals who have experience in developing and deploying AI solutions and want to validate their skills through a recognized certification. AI-102 exam covers topics such as developing AI solutions using Azure Cognitive Services, implementing computer vision solutions, natural language processing, and speech-to-text and text-to-speech conversion.
The Designing and Implementing a Microsoft Azure AI Solution certification exam covers various topics related to designing and implementing AI solutions using Azure services, including natural language processing, computer vision, speech recognition, and machine learning. AI-102 Exam also covers topics related to data preparation, data wrangling, and data analysis, which are essential skills for designing and implementing AI solutions. Candidates are expected to have a good understanding of Azure services, including Azure Cognitive Services, Azure Machine Learning, and Azure Databricks.
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The AI-102 Certification Exam is intended for professionals who have experience in designing and implementing AI solutions on the Microsoft Azure platform. Candidates should have a solid understanding of the Azure platform and be familiar with the tools and technologies used to develop and deploy AI solutions. They should also have experience in working with data and be familiar with data science concepts.
NEW QUESTION # 320
You are building an app by using the Semantic Kernel.
You need to include complex objects in the prompt templates of the app. The solution must support objects that contain sub-properties.
Which two prompt templates can you use? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Answer: D,E
Explanation:
Semantic Kernel supports multiple prompt template languages. Handlebars and Liquid both allow you to bind complex objects (with nested/sub-properties), iterate over collections, and use conditionals-ideal when your prompt needs structured data.
* JSONL is a dataset/record format, not a prompt templating language.
* YAML is a configuration/serialization format; while SK can store configs in YAML, YAML itself isn't a prompt templating engine for rendering nested objects in prompts.
* "Semantic Kernel" as an option is not a template language; SK provides the runtime and also a simple legacy template syntax, but for complex/nested objects Microsoft recommends Handlebars (cross- language) or Liquid (.NET).
References (Microsoft Docs):
* Handlebars prompt templates in Semantic Kernel (supports SK prompts, expressions, iteration, object binding). Microsoft Learn
* Liquid prompt templates in Semantic Kernel (example shows passing objects and iterating over nested data). Microsoft Learn
* SK prompt template syntax overview (context on SK templating options). Microsoft Learn
NEW QUESTION # 321
You run the following command.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/luis-container-howto
NEW QUESTION # 322
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:
Explanation:
You are asked to build an app that translates speech from English into French, German, and Spanish using Azure AI Speech Translation.
Step 1 - Define target languages
* The SpeechTranslationConfig object requires language codes (not full names like "French", "German",
"Spanish").
* Valid language codes:
* French # "fr"
* German # "de"
* Spanish # "es"
* Therefore, the correct syntax for the output languages is:
var languages = new List<string> { "fr", "de", "es" };
Step 2 - Choose the recognizer type
* IntentRecognizer: Used for intent recognition with LUIS, not translation.
* SpeakerRecognizer: Used for speaker identification/verification, not translation.
* SpeechSynthesizer: Used for text-to-speech, not translation.
* TranslationRecognizer: Used specifically for speech-to-speech or speech-to-text translation.
* Therefore, the correct recognizer is TranslationRecognizer.
* Output Languages: {"fr","de","es"}
* Recognizer: TranslationRecognizer
* Speech Translation with Azure AI Speech
* TranslationRecognizer Class (C# API)
* Supported language codes
Microsoft References
NEW QUESTION # 323
You are building a chatbot by using the Microsoft Bot Framework Composer.
You have the dialog design shown in the following exhibit.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Box 1: No
User.name is a property.
Box 2: Yes
Box 3: Yes
The coalesce() function evaluates a list of expressions and returns the first non-null (or non-empty for string) expression.
Reference:
https://docs.microsoft.com/en-us/composer/concept-language-generation
https://docs.microsoft.com/en-us/azure/data-explorer/kusto/query/coalescefunction
NEW QUESTION # 324
You have an Azure subscription that contains an Azure AI Language custom question answering project named QA1.
You need to import question and answer pairs to QA1.
Which two file formats can you use? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Answer: A,D
Explanation:
AI Services, Language Service, Export-import-refresh in custom question answering Import a project
1. Sign in to the Language Studio with your Azure credentials.
2. Scroll down to the Answer questions section and select Open custom question answering.
*-> 3. Select Import and specify the file type you selected for the export process. Either Excel, or TSV.
4. Select Choose File and browse to the local zipped copy of your project that you exported previously.
5. Provide a unique name for the project you're importing.
6. Remember that a project that has only been imported still needs to be deployed/published if you want it to be live.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/language-service/question-answering/how- to/export-import-refresh
NEW QUESTION # 325
......
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