Microsoft : AI-300 Questions & Answers
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Microsoft AI-300 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Design and implement an MLOps infrastructure | 15–20% | - Implement infrastructure as code for Machine Learning
|
| Implement machine learning model lifecycle and operations | 25–30% | - Register, version, and package models
|
| Design and implement a GenAIOps infrastructure | 20–25% | - Set up Microsoft Foundry environment
|
| Implement generative AI quality assurance and observability | 10–15% | - Evaluate and test generative AI applications
|
| Optimize generative AI systems and model performance | 15–20% | - Improve efficiency and cost-effectiveness
|
Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:
You manage an Azure Machine Learning workspace.
You schedule a pipeline job by using Azure Machine Learning Python SDK v2.
You need to decide whether the time-based schedule with cron expression is implemented correctly.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
You develop a Prompt flow in Microsoft Foundry project.
You plan to use variants and invoke a custom API in the flow.
You need to add tools to the flow that will implement the planned functionality. Your solution must minimize development efforts.
Which tools should you use? To answer, move the appropriate tools to the correct functionalities. You may use each tool once, more than once, or not at all. You may need to move the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
Implement variants: LLM tool
Invoke a custom API: Python tool
For implementing variants , use the LLM tool . Microsoft documents that Prompt flow variants are supported specifically on LLM tool nodes. A variant represents an alternative configuration of the same node, such as different prompt text, temperature values, deployment settings, or other LLM parameters. This allows multiple prompt/model configurations to be tested without duplicating the entire flow, which directly minimizes development effort.
For invoking a custom API , use the Python tool . The Python tool executes custom Python logic within a Prompt flow node and can integrate with external or third-party services. Microsoft explicitly describes Prompt flow tools as supporting integration with third-party APIs and Python packages, and the Python tool can also consume a custom connection when authentication credentials are required. This makes it the appropriate choice for calling an API that is not covered by a built-in Prompt flow tool.
The Embedding tool is designed to generate vector embeddings for text and is therefore unrelated to either requirement. It does not provide variant functionality and is not the general-purpose mechanism for invoking a custom API.
Study Guide Reference: Design and implement a GenAIOps infrastructure - Prompt flow tools, LLM variants, Python nodes, custom integrations, and flow experimentation.
You manage an Azure Machine Learning workspace.
You must define the execution environments for your jobs and encapsulate the dependencies for your code.
You need to configure the environment from a Docker build context.
How should you complete the rode segment? To answer, select the appropriate option in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
You are designing an Azure Machine Learning solution for traffic optimization.
The model must be deployed as a web service on a serverless compute and provide real-time predictions based on current traffic and weather conditions. You need to choose an inferencing strategy for the solution.
Which compute should you use?
- A. Azure Machine Learning serverless compute
- B. Azure Machine Learning batch endpoint
- C. Azure Machine Learning Kubernetes online endpoints
- D. Azure Machine Learning online endpoint
Correct Answer: D 🗳️
-
You have a Microsoft Foundry project with a connected Azure OpenAI Service model.
You have a set of text files stored locally on your computer.
You must set up a flow that will generate responses based on the content of your local files.
You need to implement a solution.
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.
Correct Answer:
Explanation:
Correct sequence:
* Create a data asset.
* Create a Foundry Search resource.
* Create a vector index.
* Create a flow.
First, create a data asset so that the locally stored text files are available within the project as a managed data source. The content must then be made searchable for Retrieval-Augmented Generation.
Next, create a Foundry Search resource to provide the search infrastructure that will store and serve the indexed document content. Microsoft documents Azure AI Search as a supported vector store for Foundry and Azure Machine Learning RAG workloads.
Third, create a vector index from the source documents. During vector-index creation, the content is processed into chunks and embeddings, which enables semantic similarity retrieval. Microsoft documents that vector indexes can be created from local files, folders, or registered data assets and then consumed by an Index Lookup operation.
Finally, create a flow and configure it to query the vector index and supply the retrieved context to the connected Azure OpenAI model. Microsoft specifically documents adding an existing vector index to a prompt flow through the Index Lookup tool.
Create a connection is the unused action in this question because the scenario already specifies a connected Azure OpenAI Service model.
Study Guide Reference: Design and implement a GenAIOps infrastructure - RAG architecture, project data, vector indexing, Azure AI Search, prompt flow, and grounding with enterprise content.
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