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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Plan and Manage an Azure AI Solution | 15-20% | - Select appropriate Azure AI services and technologies - Manage AI costs and resource allocation - Plan and configure secure AI solutions - Implement governance and compliance for AI solutions |
| Topic 2: Implement Generative AI Solutions | 10-15% | - Apply responsible AI practices - Integrate Azure OpenAI models into applications - Use Azure OpenAI Service for generative AI - Implement prompt engineering techniques |
| Topic 3: Implement Decision Support Solutions | 10-15% | - Design and implement decision automation - Integrate Azure AI services into applications - Monitor and optimize decision support solutions |
| Topic 4: Implement Computer Vision Solutions | 15-20% | - Process video and generate insights - Implement object detection and image classification - Implement Azure AI Face service - Analyze images using Azure AI Vision - Read and process documents using Azure AI Document Intelligence |
| Topic 5: Implement Natural Language Processing Solutions | 20-25% | - Implement speech capabilities using Azure AI Speech - Build question answering solutions - Analyze text using Azure AI Language - Translate text and speech - Create conversational AI solutions with Azure AI Bot Service |
| Topic 6: Implement Knowledge Mining and Document Intelligence Solutions | 15-20% | - Implement semantic and vector search - Create custom skills for Azure AI Search - Implement Azure AI Search solutions - Design document processing pipelines |
試験は簡単ではないことは広く認められていますが、この分野の労働者にとって関連するAI-102認定は非常に重要であるため、多くの労働者がこの課題に対処する必要があります。より効率的で簡単な方法で試験に合格し、関連する認定を取得する必要があります。最近の10年間で、AI-102試験問題は、国際市場での温かい歓迎と迅速な販売に対応しました。 AI-102学習教材は、他のメーカーと同じくらいリーズナブルな価格であるだけでなく、次の点で明らかに優れています。
質問 # 307
You have a 20-GB video file named File1.avi that is stored on a local drive.
You need to index File1.avi by using the Azure AI Video Indexer website.
What should you do first?
正解：B
質問 # 308
You are building a chatbot by using the Microsoft Bot Framework Composer as shown in the exhibit. (Click the Exhibit tab.)
The chatbot contains a dialog named GetUserDetails. GetUserDetails contains a TextInput control that prompts users for their name.
The user input will be stored in a property named name.
You need to ensure that you can dispose of the property when the last active dialog ends.
Which scope should you assign to name?
正解：B
解説：
Comprehensive Detailed Explanation In the Microsoft Bot Framework Composer, there are four main property scopes for storing variables or properties:
* dialog scope
* Lives only for the lifetime of the dialog.
* Once the dialog ends, properties stored in the dialog scope are automatically cleared.
* Best suited for temporary values such as user inputs collected during a dialog, like "name" in this case.
* user scope
* Persists across all conversations with the user.
* Data stored here remains available even after dialogs end, making it unsuitable if you want to dispose of the property at the end of the dialog.
* conversation scope
* Persists throughout the lifetime of a single conversation (from start to end).
* Useful for maintaining state across multiple dialogs in the same conversation, but still not automatically disposed of when a dialog ends.
* turn (curn in the options is likely a typo)
* Lives only for a single turn (a single exchange of user and bot messages).
* Cleared at the end of the turn, which is too short-lived for this requirement.
* Requirement: "Ensure that you can dispose of the property when the last active dialog ends."
* That means the property should exist only during the dialog execution and then be discarded.
* Correct scope for this scenario is dialog.
Applying to the Question
The Answer: A. dialog
* Manage properties in Bot Framework Composer
* Scopes in Bot Framework Composer
* Bot Framework Composer memory model
Microsoft References
質問 # 309
You are developing a text processing solution.
You have the function shown below.
正解：
解説：
Explanation:
The output will include the following words: our and included. # No
The output will include the following words: Paris, Eiffel, and Tower. # Yes The function will output all the key phrases from the input string to the console. # No Comprehensive Detailed Explanation Let's examine the function:
static void GetKeyWords ( TextAnalyticsClient textAnalyticsClient, string text)
{
var response = textAnalyticsClient.RecognizeEntities(text);
Console.WriteLine( " Key words: " );
foreach (CategorizedEntity entity in response.Value)
{
Console.WriteLine( $ " {entity.Text} " );
}
}
The function uses RecognizeEntities from the Azure Text Analytics SDK.
RecognizeEntities detects and categorizes named entities in text (such as locations, people, organizations, dates, etc.).
It does not extract all words or key phrases.
For extracting key phrases, you would use ExtractKeyPhrases , not RecognizeEntities .
Evaluating the statements:
" The output will include the following words: our and included. "
These are common words, not entities. They will not appear.
The Answer: No
" The output will include the following words: Paris, Eiffel, and Tower. " These are named entities (location, landmark). They will be recognized and returned.
The Answer: Yes
" The function will output all the key phrases from the input string to the console. " No. The function extracts entities, not key phrases. Key phrases would require ExtractKeyPhrases .
The Answer: No
質問 # 310
Your company uses an Azure Cognitive Services solution to detect faces in uploaded images. The method to detect the faces uses the following code.
You discover that the solution frequently fails to detect faces in blurred images and in images that contain sideways faces.
正解：A
解説：
Evaluate different models.
The best way to compare the performances of the detection models is to use them on a sample dataset. We recommend calling the Face - Detect API on a variety of images, especially images of many faces or of faces that are difficult to see, using each detection model. Pay attention to the number of faces that each model returns.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/face/face-api-how-to-topics/specify-detection-model
質問 # 311
You are building a chatbot for a travel agent. The chatbot will use the Azure OpenAI GPT 3.5 model and will be used to make travel reservations.
You need to maximize the accuracy of the responses from the chatbot.
What should you do?
正解：A
解説：
To maximize answer accuracy (reduce randomness and hallucinations) with Azure OpenAI GPT-3.5, you should lower the sampling randomness. The primary control for this is temperature :
Temperature controls how "creative" or random the model is.
Lower values # more deterministic, focused, and conservative outputs.
temperature = 0 makes the model pick the highest-probability tokens, which typically yields the most accurate and repeatable responses for transactional bots like booking/reservations.
Top P (nucleus sampling) is an alternative to temperature. Microsoft recommends using one or the other.
While reducing Top P also narrows randomness, the clearest and most common approach for maximizing accuracy is setting temperature to 0. Setting Top P to 0 is generally not recommended and may be invalid depending on the SDK.
System message tuning (Option D) helps with tone, role, and policy adherence, but does not itself guarantee factual accuracy if decoding randomness remains high.
Including the travel agent's database (Option A) would require Azure OpenAI on your data or a RAG pattern.
That can improve grounding of facts, but it's not a model parameter change and is outside the scope of simply configuring GPT-3.5; moreover, accuracy still benefits from low temperature even when using your own data.
Therefore, the best single action here is: set Temperature to 0.
Microsoft Azure AI Solution References
Azure OpenAI Service - Chat Completions parameters (Temperature and Top P): "Lower values like 0.2 will make the output more focused and deterministic... use either temperature or top_p but generally not both."
https://learn.microsoft.com/azure/ai-services/openai/reference#chat-completions Prompt engineering with Azure OpenAI - controlling randomness and determinism:
https://learn.microsoft.com/azure/ai-services/openai/concepts/prompt-engineering Azure OpenAI on your data (grounding with enterprise data/RAG):
https://learn.microsoft.com/azure/ai-services/openai/concepts/use-your-data
質問 # 312
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