Microsoft인증 AI-103시험을 통과하여 자격증을 취득하여 IT 업계에서의 자신의 자리를 지키려면 많은 노력이 필요합니다. 회사일도 바쁜데 시험공부까지 스트레스가 장난아니게 싸이고 몸도 많이 상하겠죠. PassTIP는 여러분을 위해 최신Microsoft인증 AI-103시험에 대비한Microsoft인증 AI-103덤프를 발췌하였습니다. Microsoft인증 AI-103덤프는Microsoft인증 AI-103시험의 기출문제와 예상문제가 묶어져 있어 시험적중율이 굉장히 높습니다. 쉽게 시험을 통과하려면PassTIP의 Microsoft인증 AI-103덤프를 추천합니다.
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement generative AI and agentic solutions | 30–35% | - Build generative AI applications
|
| Topic 2: Plan and manage Azure AI solutions | 25–30% | - Manage AI solution development lifecycle
|
| Topic 3: Implement computer vision solutions | 10–15% | - Build multimodal solutions
|
| Topic 4: Implement information extraction and knowledge mining | 10–15% | - Build knowledge bases and search solutions
|
| Topic 5: Implement text and speech analysis solutions | 10–15% | - Implement natural language processing
|
PassTIP는 여러분이 빠른 시일 내에Microsoft AI-103인증시험을 효과적으로 터득할 수 있는 사이트입니다.Microsoft AI-103인증 자격증은 일상생활에 많은 개변을 가져올 수 있는 시험입니다.Microsoft AI-103인증 자격증을 소지한 자들은 당연히 없는 자들보다 연봉이 더 높을 거고 승진기회도 많아지며 IT업계에서의 발전도 무궁무진합니다.
질문 # 18
You have an Azure subscription
You need to create a new resource that will generate fictional stories in response to user prompts. The solution must ensure that the resource uses a customer-managed key to protect data.
How should you complete the script? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
정답：
설명：
Explanation:
The solution requires a generative text model, so the Cognitive Services account must be created with the OpenAI kind from the values supplied in the exhibit.
The JSON object shown underneath contains Key Vault-based encryption settings such as the key source, key vault URI, key name, and key version. That JSON therefore belongs to the --encryption parameter.
Customer-managed key encryption lets the organization control the key used to protect supported stored data.
This provides additional control over rotation, revocation, key governance, and separation of duties compared with relying solely on Microsoft-managed keys.
The Azure AI resource also needs permission to use the Key Vault key. In an operational deployment, this generally involves configuring a managed identity and granting the required Key Vault permissions.
--api-properties configures service-specific API properties rather than encryption. --assign-identity enables the account identity but does not itself supply the customer-managed encryption configuration shown in the question.
Therefore, the correct selections are OpenAI and --encryption .
Study Guide alignment: design Azure infrastructure, configure model resources, and secure AI systems through identity and resource-security controls . Microsoft Learn
질문 # 19
You are building a chatbot.
You need to ensure that the chatbot can classify user input into separate categories. The categories must be dynamic and defined at the time of inference.
Which service should you use to classify the input?
정답：C
설명：
To classify user input into separate categories that are dynamic and defined at the time of inference, Azure OpenAI text classification is the best option. OpenAI models such as GPT can dynamically classify text into categories based on context and user-defined instructions. Since the categories need to be flexible and determined at inference time, OpenAI's natural language understanding capabilities are well-suited for this task.
질문 # 20
You plan to configure an evaluation in Microsoft Foundry for a Retrieval Augmented Generation (RAG) chat app.
You need to provide scores for groundedness, relevance, and harmful content categories.
Which two evaluation categories can you use? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
정답：A,E
설명：
The two evaluation categories that would be of use are AI quality (AI assisted) metrics and risk and safety metrics Risk and safety metrics: Scoring harmful content categories (such as violence, sexual content, hate speech, and self-harm) is handled directly by the specialized safety evaluators contained within this category.
AI quality (AI assisted) metrics: In Microsoft Foundry, evaluating RAG-specific criteria like groundedness and relevance requires large language models (LLMs) acting as a judge to assess semantic context. These are explicitly categorized under AI-assisted quality metrics rather than standard natural language processing (NLP) rules.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/concepts/evaluation-evaluators/rag-evaluators
https://learn.microsoft.com/en-us/azure/foundry-classic/how-to/develop/evaluate-sdk
질문 # 21
Hotspot Question
You have a Microsoft Foundry project that contains an internal Q&A agent.
Users report the following issues when they ask the agent questions:
- An increase in the following response: "No relevant information
found"
- Periodic HTTP 429 rate limit exceeded errors during peak hours
You need to identify whether each issue is caused by model unavailability, resource limits, or inference failures.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
정답：
설명：
질문 # 22
You have a Microsoft Foundry project that contains a prompt agent used by a customer support web app.
The agent is invoked from a Python service that does NOT run in the Foundry portal.
You need to implement end-to-end tracing to capture latency breakdowns and exceptions across agent runs.
Which two components can you use? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
정답：B,C
설명：
The correct components are OpenTelemetry and Application Insights . Microsoft Foundry tracing for prompt agents is designed to capture detailed telemetry for agent execution, including latency, exceptions, prompt activity, and retrieval operations. For a Python service that invokes the agent outside the Foundry portal, OpenTelemetry is the appropriate instrumentation mechanism because the Foundry SDK tracing setup uses OpenTelemetry packages and Azure SDK tracing integration for client-side traces. This enables distributed tracing across the external Python service and the agent run.
Application Insights is the telemetry backend used by Foundry tracing. The Foundry tracing setup requires an Azure Monitor Application Insights resource to store traces, and traces can then be viewed in Foundry or directly in Azure Monitor Application Insights. Application Insights also provides performance and failure investigation experiences for response times, slow transactions, errors, and exceptions.
A Log Analytics workspace may underlie Application Insights data storage, but it is not the agent tracing component itself. The Azure Monitor Agent collects machine and guest telemetry, not application-level agent traces from a Python SDK invocation. Microsoft Sentinel is a security information and event management solution, not an end-to-end agent tracing mechanism. Reference topics: Microsoft Foundry tracing, Azure Monitor Application Insights, OpenTelemetry instrumentation, prompt agent observability, and Python SDK telemetry.
질문 # 23
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PassTIP는 여러분이Microsoft 인증AI-103인증시험 패스와 추후사업에 모두 도움이 되겠습니다. PassTIP제품을 선택함으로 여러분은 시간도 절약하고 돈도 절약하는 일석이조의 득을 얻을수 있습니다. 또한 구매후 일년무료 업데이트 버전을 받을수 있는 기회를 얻을수 있습니다. Microsoft 인증AI-103 인증시험패스는 아주 어렵습니다. 자기에 맞는 현명한 학습자료 선택은 성공의 지름길을 내딛는 첫발입니다. 퍼펙트한 자료만이 시험에서 성공할수 있습니다. PassTIP시험문제와 답이야 말로 퍼펙트한 자료이죠. PassTIP Microsoft 인증AI-103인증시험자료는 100% 패스보장을 드립니다.
AI-103인증덤프 샘플체험: https://www.passtip.net/AI-103-pass-exam.html