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| Section | Weight | Objectives |
|---|---|---|
| Implement computer vision solutions | 10–15% | - Build multimodal solutions
|
| Implement information extraction and knowledge mining | 10–15% | - Extract structured data from documents
|
| Implement text and speech analysis solutions | 10–15% | - Implement speech capabilities
|
| Implement generative AI and agentic solutions | 30–35% | - Build generative AI applications
|
| Plan and manage Azure AI solutions | 25–30% | - Design Azure AI infrastructure
|
This updated Microsoft AI-103 exam study material of ActualPDF consists of these 3 formats: Developing AI Apps and Agents on Azure (AI-103) PDF, desktop practice test software, and web-based practice exam. Each format of ActualPDF aids a specific preparation style and offers unique advantages, each of which is beneficial for strong Developing AI Apps and Agents on Azure (AI-103) exam preparation. The features of our three formats are listed below. You can choose any format as per your practice needs.
NEW QUESTION # 143
You plan to deploy a containerized version of an Azure Al Language service that will be used for sentiment analysis.
You configure https: //contoso.cognitiveservices.azure, cow as the endpoint URI for the service.
You need to run the container on an Azure virtual machine by using Docker.
How should you complete the command? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
First selection:
mcr.microsoft.com/azure-cognitive-services/textanalytics/sentiment
Second selection:
https://contoso.cognitiveservices.azure.com
Completed command:
docker run --rm -it -p 5000:5000 --memory 8g --cpus 1 \
mcr.microsoft.com/azure-cognitive-services/textanalytics/sentiment \
Eula=accept \
Billing=https://contoso.cognitiveservices.azure.com \
ApiKey=xxx
The image mcr.microsoft.com/azure-cognitive-services/textanalytics/sentiment is the official Microsoft Container Registry image for Azure AI Language sentiment analysis. The Billing argument must reference the endpoint of the provisioned Azure AI Language resource associated with the supplied API key. It is not the address used by applications to invoke the locally running container.
Azure
AI containers require three licensing and metering parameters: Eula=accept, a valid Billing endpoint, and ApiKey. Without all three values, the container does not start. The command maps host port 5000 to container port 5000, allocates 8 GB of memory and one CPU, creates an interactive terminal, and automatically removes the stopped container. Applications subsequently invoke the locally hosted service through an address such as http://
< vm-address > :5000. The Azure endpoint remains responsible for metering and authentication; analyzed customer text is processed by the container rather than sent to the billing endpoint.
Study Guide alignment: Implement text analysis solutions - perform sentiment analysis and opinion mining, deploy Azure Language containers, and configure container billing parameters.
NEW QUESTION # 144
You are building an image sharing app that will use Azure AI to prevent users from sharing sexually explicit images.
You need to ensure that inappropriate images are identified correctly. The solution must minimize development effort.
What should you use?
Answer: D
Explanation:
To prevent users from sharing sexually explicit images, you need a tool that can handle content moderation specifically designed for inappropriate content such as adult or sexually explicit material. Azure AI Content Safety Studio is a service designed for this purpose, providing pre- built AI models that can detect inappropriate content like adult images, violence, and other sensitive material. It minimizes development effort by offering an easy-to-use, pre-configured service for content safety without the need to build custom models.
NEW QUESTION # 145
Hotspot Question
You have a Microsoft Foundry project that contains an agent.
The agent accepts user-uploaded screenshots and uses a multimodal chat model.
Some screenshots contain potentially malicious embedded text.
You need to prevent a prompt injection attack and ensure that third-party content is treated as lower trust.
How should you configure prompt shields for document attacks? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 146
You have a chat app in a Microsoft Foundry project and an Azure AI Search vectorized index.
You need to connect to the index to meet the following requirements:
- Complex questions must retrieve information from multiple chunks.
- Multi-turn conversations must influence retrieval planning.
- Retrievals must run in parallel to reduce latency.
Which retrieval approach should you use?
Answer: A
Explanation:
An agentic Retrieval-Augmented Generation (RAG) architecture is the best fit for your requirements. Traditional, linear RAG pipelines struggle with complex, multi-turn dependencies, whereas an agentic approach natively solves them through iterative reasoning and tool orchestration.
Here is how an agentic RAG pattern specifically satisfies each the three technical requirements:
1. Complex Questions & Multi-Chunk Retrieval
The Challenge: Complex questions often require aggregating distinct pieces of information scattered across different documents or sections.
The Agentic Solution: An agent uses a Reasoning and Acting (ReAct) loop. It evaluates the user's query, breaks it down into sub-questions, and executes multiple distinct search queries. It then synthesizes the information from these diverse chunks before generating a final response.
2. Multi-Turn Conversations & Retrieval Planning
The Challenge: Standard RAG often just passes the latest user message or a simple chat history summary to the search index, which can lose context or misinterpret the user's true intent in long conversations.
The Agentic Solution: The agent acts as a query planner. It maintains the state of the conversation and dynamically decides if a retrieval is needed, what specific keywords or vectors to target based on past turns, and how to reformulate the query to bridge context gaps.
3. Parallel Retrievals for Low Latency
The Challenge: Sequential lookups drastically increase time-to-first-token (TTFT), harming the chat user experience.
The Agentic Solution: Modern AI agent frameworks allow the LLM to emit multiple tool calls simultaneously. The application layer can intercept these calls and execute parallel asynchronous queries against your Azure AI Search vectorized index, drastically minimizing latency.
Reference:
https://arxiv.org/html/2501.09136v1
NEW QUESTION # 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.
Answer:
Explanation:
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.
NEW QUESTION # 148
......
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