AI-103の実践教材を使用することで、以前に想像していた以上の成果を絶対に得ることができます。 AI-103の実際のテストを選択した顧客から収集された明確なデータがあり、合格率は98〜100％です。 したがって、成功を収めるチャンスは、AI-103ブレインダンプ資料によって大幅に増加します。 さらに、一連の利点があります。 したがって、AI-103の実際のテストの重要性は言うまでもありません。 今すぐご注文いただいた場合、1年間無料の更新をお送りします。
| Section | Objectives |
|---|---|
| Topic 1: Implement Computer Vision Solutions | - Image classification and object detection - OCR and document intelligence |
| Topic 2: Implement Natural Language Processing Solutions | - Language understanding and intent recognition - Text analytics and summarization - Translation and multilingual support |
| Topic 3: Develop Generative AI Applications and Agents | - AI agents architecture
|
| Topic 4: Knowledge Mining and Information Retrieval | - Azure AI Search configuration - Indexing and semantic search - RAG (Retrieval Augmented Generation) patterns |
| Topic 5: Plan and Manage Azure AI Solutions | - Azure AI resource provisioning and configuration - Model selection and lifecycle management - Responsible AI principles and governance |
AI-103「Developing AI Apps and Agents on Azure」はMicrosoftの一つ認証試験として、もしMicrosoft認証試験に合格してIT業界にとても人気があってので、ますます多くの人がAI-103試験に申し込んで、AI-103試験は簡単ではなくて、時間とエネルギーがかかって用意しなければなりません。
質問 # 143
You are building a text-to-speech solution that uses Azure Speech in Foundry Tools to read instructions from the script in a text file.
You discover that the solution often pronounces technical terms incorrectly.
You need to prevent the incorrect pronunciations. The solution must minimize development effort.
What should you do?
正解：C
解説：
Using Speech Synthesis Markup Language (SSML) with the <phoneme> element is the ideal way to fix mispronunciations for technical terms.
The <phoneme> tag lets you override the default text-to-speech model by explicitly defining the sounds using the International Phonetic Alphabet (IPA).
Reference:
https://learn.microsoft.com/en-us/answers/questions/5729867/pronunciation-issue-when-generating-audio-from-ssm
質問 # 144
You have a Microsoft Foundry project that contains a customer support agent built by using the Foundry Agent Service.
The agent uploads user-provided screenshots to Azure Storage through a ticketing tool and receives a blob URL for additional reasoning.
You need to use image moderation during agent runs and prevent harmful content from being returned during runs. Azure Al Content Safety must access the images by using the blob URL. The solution must follow the principle of least privilege.
What should you configure for Content Safety? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation:
Guardrails: Select User input, Output, Tool response, and Tool call and set Action to Block.
Storage access: A system-assigned managed identity that is assigned the Storage Blob Data Contributor role The guardrail must be applied to User input, Output, Tool response, and Tool call with the action set to Block . Microsoft Foundry guardrails support four intervention points: user input, tool call, tool response, and output. This scenario includes user-provided screenshots, a ticketing tool that uploads images and returns blob URLs, and final agent responses. Applying blocking controls at all four points ensures harmful image-related content is inspected throughout the agent run and prevented from continuing or being returned to the user.
Microsoft's guardrails guidance also states that tool call and tool response controls are specifically required when harmful content can pass through agent tools.
For storage, configure the Azure AI Content Safety resource with a system-assigned managed identity and grant it Storage Blob Data Contributor on the storage account or container. The Content Safety image moderation quickstart states that images can be supplied by blob storage URL and that the Content Safety resource must be given storage access by enabling its system-assigned managed identity and assigning Storage Blob Data Contributor or Owner; Contributor is the least-privileged valid option shown. Reference topics: Foundry guardrails, agent intervention points, image moderation, managed identity, and Azure Storage RBAC.
質問 # 145
In Azure AI Studio, you use Completions playground with the GPT-35 Turbo model.
You have a prompt that contains the following code.
function F(n)
{
var f = [0, 1];
for (var i = 2; i < n; i++) f[i] = f[i-1] + f[i-2];
return f;
}
You need the model to create an explanation of the code. The solution must minimize costs.
What should you do?
正解：C
解説：
By adding a comment like // what does function F do? to the prompt, you are instructing the GPT model to provide an explanation of the function's behavior. This approach directly asks the model to explain the code, which will help generate an explanation without requiring changes to the model or additional resource-intensive configurations.
質問 # 146
You have an application named App1 that uses Azure Speech in Foundry Tools to transcribe live calls.
Transcript segments often contain both English and Spanish. App1 sends each segment to Azure Translator in Foundry Tools to translate to another language.
Sometimes, mixed-language segments result in incomplete or incorrect translations.
You need to reduce translation errors. The solution must ensure that the entire transcript is translated successfully.
What should you do before sending the segments to Translator?
正解：B
解説：
To fix incomplete or incorrect translations from mixed-language transcripts, you must identify and isolate the languages at the sentence or phrase level before sending the text to Azure Translator.
Azure Translator performs best when a single request contains only one source language. When it receives a mixed-language segment under a single source language code, it often fails to parse the secondary language correctly.
Here is the step-by-step pipeline you should implement inside your application before hitting the Translator API.
1. Split Segments into Sentences
Live call transcript segments can contain multiple sentences. Do not send the raw, multi-sentence segment directly to the translator if it contains mixed languages.Break the segment into individual sentences.Use regex punctuation rules or a lightweight sentence-splitting library.
2. Run Language Detection Per Sentence
Azure Translator has a built-in Detect API, but for mixed-language live calls, executing a dedicated detection step per sentence provides better control.
Reference:
https://medium.com/neural-engineer/azure-ai-speech-to-text-real-time-transcription-58bfd5fd1a28
質問 # 147
You need to configure the model deployment for Agent1 to meet the technical requirements.
What should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation:
Deployment type: Standard
Version update policy: Once the current version expires
The correct deployment type is Standard . The case study specifies that Project1 is deployed in an EU Azure region and that model-processed data must remain within the EU. It also requires scalable, high-throughput generative AI workloads that dynamically handle variable customer support traffic without reserved throughput capacity. In Microsoft Foundry Models, Standard is a pay-per-token deployment type that processes data in a single Azure region, while Global Standard can process requests across regions and Global Provisioned uses reserved provisioned throughput. Microsoft's deployment-type guidance identifies Standard as single-region, pay-per-token, whereas Global Provisioned is cross-region with reserved capacity.
The correct version update policy is Once the current version expires . This keeps Agent1 on the selected model version during its supported lifecycle, which supports stable and consistent responses, but still preserves continuity by automatically moving to a supported replacement when the current version is retired.
Microsoft's model versioning guidance states that this policy updates only when the current model version expires, while upgrading when a new default is available changes the deployment sooner and opting out can cause the deployment to stop working after retirement. Reference topics: deployment types, regional data processing, model versioning, throughput capacity, and stable production deployments.
質問 # 148
......
すべての人にAI-103試験問題を試す機会を提供するために、当社の専門家がすべての人向けのAI-103準備ガイドの試用版を設計しました。当社の製品を購入することをheする場合。 AI-103テストプラクティスファイルを購入する前に、当社の試用版を試すことができます。試用版はデモを提供します。さらに重要なことは、当社のデモはすべての人にとって無料です。無料デモで、当社のAI-103準備資料を深く理解できます。
AI-103受験記対策: https://www.pass4test.jp/AI-103.html