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Microsoft AI-103 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Plan and Manage Azure AI Solutions | - Azure AI resource provisioning and configuration - Model selection and lifecycle management - Responsible AI principles and governance |
| Topic 2: Implement Natural Language Processing Solutions | - Text analytics and summarization - Language understanding and intent recognition - Translation and multilingual support |
| Topic 3: Develop Generative AI Applications and Agents | - Azure OpenAI Service integration
|
| Topic 4: Knowledge Mining and Information Retrieval | - Azure AI Search configuration - RAG (Retrieval Augmented Generation) patterns - Indexing and semantic search |
| Topic 5: Implement Computer Vision Solutions | - OCR and document intelligence - Image classification and object detection |
Microsoft Developing AI Apps and Agents on Azure Sample Questions:
A customer support agent must answer from an internal knowledge base that is updated weekly with new product documentation, and its answers must always reflect the latest content. Which approach should you implement?
- A. Apply Direct Preference Optimization (DPO) to the documents
- B. Implement retrieval-augmented generation (RAG) over an index of the documents
- C. Fine-tune the base model on the new documents every week
- D. Raise the model temperature to improve recall of recent facts
Explanation: Only visible for ValidVCE members. You can sign-up / login (it's free).
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.
- A. AI quality (AI assisted) metrics
- B. similarity evaluators
- C. risk and safety metrics
- D. AI quality (NLP) metrics
- E. fluency evaluator
Explanation: Only visible for ValidVCE members. You can sign-up / login (it's free).
A production application authenticates to Microsoft Foundry using an API key stored in an environment variable. Your security team requires that you remove hardcoded secrets and enable per-principal auditing of every call. Which approach meets both requirements?
- A. Share a single API key across services and restrict it with IP allow-listing
- B. Embed the API key in the deployment pipeline as a masked variable
- C. Authenticate with Microsoft Entra ID using a managed identity (keyless)
- D. Move the API key into Azure Key Vault and rotate it monthly
Explanation: Only visible for ValidVCE members. You can sign-up / login (it's free).
A legal team must extract clauses and inferred fields from unstructured contracts. One required field is the contract end date, which is not stated explicitly and must be derived from the start date plus the term. The team has no labelled training data. Which tool best fits?
- A. The Azure AI Search semantic ranker
- B. Azure Content Understanding using a prebuilt-contract or zero-shot custom analyser
- C. Azure Translator in Foundry Tools
- D. An Azure AI Document Intelligence custom model trained on labelled samples
Explanation: Only visible for ValidVCE members. You can sign-up / login (it's free).
Drag and Drop Question
You are developing an application that will detect faulty components produced on a factory production line. The components are specific to your business.
You need to use the Azure Custom Vision API to help detect common faults.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Explanation:
Step 1: Create a project
You must first set up a new project in the Custom Vision portal or via the SDK to store your data and configuration.
Step 2: Upload and tag images
Next, you need to upload photos of your specific factory components and draw bounding boxes around the faults to tag them.
Step 3: Train the object detection model
Because you need to identify and locate specific, localizable faults on a component, you must train an object detection model rather than a general classifier model.
Reference:
https://thegroundtruth.blog/tag/computervision/
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