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Microsoft AI-103 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Knowledge Mining and Information Retrieval | - RAG (Retrieval Augmented Generation) patterns - Indexing and semantic search - Azure AI Search configuration |
| Develop Generative AI Applications and Agents | - AI agents architecture
|
| Implement Natural Language Processing Solutions | - Language understanding and intent recognition - Text analytics and summarization - Translation and multilingual support |
| Plan and Manage Azure AI Solutions | - Model selection and lifecycle management - Responsible AI principles and governance - Azure AI resource provisioning and configuration |
| Implement Computer Vision Solutions | - Image classification and object detection - OCR and document intelligence |
Microsoft Developing AI Apps and Agents on Azure Sample Questions:
An application must translate a high volume of standard product descriptions into 20 languages quickly, consistently, and at the lowest cost. The text is straightforward, with no domain-specific nuance. Which approach best fits?
- A. A separately fine-tuned model for each of the 20 target languages
- B. Azure Translator in Foundry Tools
- C. A large language model translation flow with a custom system prompt
- D. Azure AI Document Intelligence
Explanation: Only visible for Exam4Free members. You can sign-up / login (it's free).
You build a chatbot that uses the Azure OpenAI GPT-4 model to generate song lyrics.
You need to ensure that responses do NOT contain lyrics from popular songs that might have been ingested during model training.
Which Azure AI Content Safety API should you use?
- A. Analyze Text
- B. Protected material text detection
- C. Custom categories
- D. Groundedness detection
Explanation: Only visible for Exam4Free members. You can sign-up / login (it's free).
Hotspot Question
You have a Microsoft Foundry project that contains a customer support application.
You create an evaluation named Run1 that has the following configurations:
- Includes risk and safety metrics
- Includes the protected material evaluation
- Includes harmful content metrics that use a medium severity threshold You create an evaluation named Run2 that has the following configurations:
- Includes risk and safety metrics
- Includes the protected material evaluation
- Includes harmful content metrics that use a high severity threshold
You run both evaluations against a dataset named DB1 and receive the following results:
- Content harm defect rate of Run1: 12%
- Content harm defect rate of Run2: 4%
- Protected material evaluation of Run1: 6%
- Protected material evaluation of Run1: 6%
You start a fine-tuning job by using DB1. The job fails during automatic RAI checks for multiple content harm types.
You discover that the content filtering configuration is set to high severity.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Explanation:
Box 1: No
Changing the content filtering configuration to low severity will not resolve the fine-tuning job issues.
Box 2: Yes
The difference between a 12% defect rate in Run1 and a 4% defect rate in Run2 is entirely consistent with the different severity thresholds configured for the two evaluations.
Box 3: Yes
The protected material evaluation metric is entirely unaffected by changes to the content harm severity threshold.
Reference:
https://learn.microsoft.com/en-us/python/api/azure-ai-evaluation/azure.ai.evaluation
You have a Microsoft Foundry project that contains a support-ticket triage agent built by using the Foundry Agent Service.
The agent uses tool to classify the ticket type and sot the ticket priority.
Sometimes, the same support case continues across multiple sessions over several days.
You need to persist state by using a durable ID to ensure that the agent can automatically reuse the full interaction history. The solution must preserve previous user messages, tool calls and tool outputs across turns and sessions.
Which runtime component should you use?
- A. conversation
- B. agent
- C. output item
- D. response
Explanation: Only visible for Exam4Free members. You can sign-up / login (it's free).
You have an Azure AI Search indexer that ingest PDF policy manuals.
Client applications must display page-level citations that have bounding polygons for both text and images.
You need to add a single built-in multimodal content extraction skill to the Azure AI Search skillset. The solution must meet the following requirements:
- Provide text and image location metadata.
- Extract tables that span multiple pages.
What should you add?
- A. Document Layout
- B. Azure Content Understanding
- C. Document Extraction
- D. GenAI Prompt
Explanation: Only visible for Exam4Free members. You can sign-up / login (it's free).
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