Exam Name: Operationalizing Machine Learning and Generative AI Solutions
Certification Provider: Microsoft
Corresponding Certification: Microsoft Certified
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Microsoft AI-300 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement generative AI quality assurance and observability | 10–15% | - Evaluate and test generative AI applications
|
| Topic 2: Optimize generative AI systems and model performance | 15–20% | - Optimize model selection and configuration
|
| Topic 3: Design and implement a GenAIOps infrastructure | 20–25% | - Set up Microsoft Foundry environment
|
| Topic 4: Design and implement an MLOps infrastructure | 15–20% | - Implement infrastructure as code for Machine Learning
|
| Topic 5: Implement machine learning model lifecycle and operations | 25–30% | - Deploy models to production
|
Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:
You have a deployment of an Azure OpenAI Service base model.
You plan to fine-tune the model.
You need to prepare a file that contains training data for multi-turn chat.
Which file encoding method should you use?
- A. UTF-16
- B. ASCII
- C. ISO-8859-1
- D. UTF-8
Explanation: Only visible for ITPassLeader members. You can sign-up / login (it's free).
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You work in Microsoft Foundry with a prompt flow.
You must manually evaluate prompts and compare results across prompt variants.
You need to capture the inputs, outputs, token usage, and latencies for each flow run for the evaluation.
Solution: Configure Azure Monitor to collect logs from the workspace. Use the logs to perform prompt evaluation.
Does the solution meet the goal?
- A. No
- B. Yes
Explanation: Only visible for ITPassLeader members. You can sign-up / login (it's free).
You have a Microsoft Foundry project and a CSV file stored in Azure Blob Storage.
You plan to add the CSV file as the grounding data to the project for RAG by using the file data type.
You need to specify the URL schema to designate the blob location.
Which URL schema should you specify?
- A. hdfs
- B. wasbs
- C. nfs
- D. file
Explanation: Only visible for ITPassLeader members. You can sign-up / login (it's free).
Drag and Drop Question
You have an existing GitHub repository containing Azure Machine Learning project files.
You need to clone the repository to your Azure Machine Learning shared workspace file system.
Which four 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.
NOTE: More than one order of answer choices is correct. You will receive credit for any of the correct orders you select.
Hotspot Question
You manage a Microsoft Foundry project.
You plan to build a RAG solution.
The solution must include two models:
- One for text output, named Model1. This model must resemble human
language and read naturally.
- One for creating embeddings, named Model2. This model must maximize
the retrieval of relevant results (high recall) while minimizing
irrelevant or incorrect matches (high precision).
You need to compare different models by using benchmarking metrics to select the appropriate models for Model1 and Model2.
Which benchmarking metric should you select for each model? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
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