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Microsoft AI-103 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Plan and Manage Azure AI Solutions | - Azure AI resource provisioning and configuration - Responsible AI principles and governance - Model selection and lifecycle management |
| Implement Computer Vision Solutions | - Image classification and object detection - OCR and document intelligence |
| Implement Natural Language Processing Solutions | - Language understanding and intent recognition - Text analytics and summarization - Translation and multilingual support |
| Develop Generative AI Applications and Agents | - AI agents architecture
|
| Knowledge Mining and Information Retrieval | - Indexing and semantic search - RAG (Retrieval Augmented Generation) patterns - Azure AI Search configuration |
Microsoft Developing AI Apps and Agents on Azure Sample Questions:
You have a customer support agent that uses the Microsoft Foundry Agent Service.
Sometimes, customers return to a session days later to continue the same support case, and the agent must resume with the full historical context. The agent must provide the following:
* Multi-turn continuity within the session
* Cross-session continuity for the same case
* Access to the full interaction history, including user messages, agent messages, tool calls, and tool outputs You need to ensure that the agent automatically reloads the complete history on each new turn.
What should you do?
- A. Persist only the final model response stored in the client application and prepend the response to future prompts.
- B. Enable memory summarization on the agent definition to persist the context automatically.
- C. Create and reuse a conversation by storing the conversation's ID and supplying the ID on subsequent requests.
Correct Answer: C 🗳️
Explanation: Only visible for Actualtests4sure members. You can sign-up / login (it's free).
You have an Azure AI Search indexer that ingests PDF policy manuals.
You need a single built-in multimodal content extraction skill that:
* provides text and image location metadata;
* extracts tables spanning multiple pages.
- A. Azure Content Understanding
- B. GenAI Prompt
- C. Document Extraction
- D. Document Layout
Correct Answer: A 🗳️
Explanation: Only visible for Actualtests4sure members. You can sign-up / login (it's free).
You have a Microsoft Foundry project that contains an agent.
The agent uses a knowledge source built from documents stored in Azure Blob Storage. The documents include digitally scanned PDFs that contain multipage tables.
You have an ingestion job that extracts only plain text, causing loss of table structure, headings, and page- number metadata.
Users frequently ask questions that require the retrieval of specific table rows across the pages.
You need to configure an ingestion job for a Retrieval Augmented Generation (RAG) pipeline that performs optical character recognition (OCR) on scanned PDFs, preserves tables and headings as structure-aware chunks, and stores page-number metadata with each chunk.
How should you configure the ingestion job?
- A. Use advanced data parsing to reingest the documents.
- B. Use page-level OCR extraction and store each page as a single chunk.
- C. Use OCR and page-level chunking.
- D. Use basic parsing and fixed-size chunking.
Correct Answer: A 🗳️
Explanation: Only visible for Actualtests4sure members. You can sign-up / login (it's free).
you are building a model to detect objects in images.
The performance of the model based on training data is shown in the following exhibit.
Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
The first answer follows directly from precision . Precision is calculated as:
Precision = TP / (TP + FP)
where TP represents true positives and FP represents false positives. A precision value of 100% means that every detection counted as positive in the evaluation was correct. Therefore, there were no false-positive detections within the evaluated predictions, and the requested percentage is 0 .
The second expression shown in the question is:
TP / (TP + FN)
This is the definition of recall . The exhibit explicitly reports Recall = 25.0% , so the correct dropdown selection is 25 .
These results illustrate an important model-evaluation distinction. A detector can achieve very high precision while having poor recall. In this case, when the model reports a detection it is highly reliable, but it is finding only about one quarter of the actual target objects. Additional or better-balanced training data, improved annotations, and threshold adjustments might be needed to increase recall.
The AI-103 computer-vision objectives require candidates to understand and operationalize object/component identification workflows, for which precision and recall are fundamental performance measures.
You have a Microsoft Foundry project that uses Azure Content Understanding in Foundry Tools to analyze marketing videos.
Video segmentation is enabled.
You need to configure an analyzer to output a generated JSON field that describes the color scheme of each video segment.
How should you configure the analyzer? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
* Field method: generate
* Field value type: string
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Apps and Agents Developer/Course Guide/topics:
The field should use the generate method because "describe the color scheme" asks Content Understanding to infer visual characteristics from each segment and generate a new semantic description. The desired value is not simply literal text that can be extracted from the media.
The field value type is string because a color-scheme description is textual-for example, " warm red and orange palette " or " predominantly cool blue and gray tones " .
Microsoft ' s Content Understanding video documentation explains that after segmentation, the generative- model layer can interpret scenes and populate developer-defined fields for every segment. Field schemas define a field ' s name, type, and description, and generated fields can capture semantic information derived from the source content. Microsoft Learn Therefore, the two values shown in the supplied answer image are reversed. The technically correct configuration is:
method = generate
type = string
This directly aligns with AI-103 ' s computer-vision objectives to implement visual understanding by configuring Azure Content Understanding to extract visual characteristics and process and interpret video segments . Microsoft Learn
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