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Microsoft AI-300 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Optimize generative AI systems and model performance | 15–20% | - Optimize model selection and configuration
|
| Topic 2: Design and implement an MLOps infrastructure | 15–20% | - Create and manage Machine Learning workspace resources and assets
|
| Topic 3: Design and implement a GenAIOps infrastructure | 20–25% | - Implement infrastructure for generative AI workloads
|
| Topic 4: Implement machine learning model lifecycle and operations | 25–30% | - Orchestrate model training and experimentation
|
| Topic 5: Implement generative AI quality assurance and observability | 10–15% | - Evaluate and test generative AI applications
|
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-8
- B. ISO-8859-1
- C. UTF-16
- D. ASCII
Correct Answer: A 🗳️
Explanation: Only visible for CertkingdomPDF members. You can sign-up / login (it's free).
-
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.
Correct Answer:
Explanation:
Correct sequence:
* From the terminal window in the Azure Machine Learning interface, run the ssh-keygen command.
* From the terminal window in the Azure Machine Learning interface, run the cat ~/.ssh/id_rsa.
pub command.
* Add a public key to the GitHub account.
* From the terminal window in the Azure Machine Learning interface, run the git clone command.
Azure Machine Learning supports cloning Git repositories directly into the workspace file system from a compute instance terminal . For an SSH-based GitHub connection, the required workflow is to generate an SSH key pair, obtain the public-key value, associate that public key with the Git account, and then clone the repository using its SSH URL. Microsoft documents this exact logical sequence for Git integration with Azure Machine Learning.
First, ssh-keygen creates the private/public SSH key pair on the Azure Machine Learning compute instance.
Next, the cat ~/.ssh/id_rsa.pub command displays the public-key contents so they can be copied. The public key is then added to the GitHub account, enabling GitHub to authenticate connections originating from the compute instance. The private key must remain on the compute instance and must never be uploaded to GitHub.
Finally, execute git clone with the repository ' s SSH clone URL. Azure Machine Learning documentation confirms that repositories can be cloned directly into its shared workspace file system and recommends performing Git operations from the compute-instance terminal.
Add a private key to the GitHub account is therefore the unused and incorrect action.
Study Guide Reference: Design and implement an MLOps infrastructure - source control integration, Azure Machine Learning workspace files, SSH authentication, Git repositories, and secure development workflows.
You have an Azure Machine Learning (ML) model deployed to an online endpoint.
You need to review container logs from the endpoint by using Azure Ml Python SDK v2. The logs must include the console log from the inference server with print/log statements from the models scoring script.
What should you do first?
- A. Create an instance of the OnlineDeploymentOperations class.
- B. Connect by using SSH to the inference server.
- C. Create an instance of the the MLCIient class.
- D. Connect by using Docker tools to the inference server.
Correct Answer: C 🗳️
You need to configure an optimization method to meet Fabrikam Inc.'s technical requirements.
Which strategy should you apply first? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
Domain specialization: Supervised fine-tuning
Poor response accuracy of a RAG-based solution: Apply prompt engineering For domain specialization , Supervised Fine-Tuning (SFT) is the correct first strategy. Microsoft describes SFT as the foundational fine-tuning technique for training a model from labeled input-output pairs , and specifically identifies domain specialization as one of its principal use cases. Microsoft also recommends starting with SFT for most customization projects because it supports task specialization, instruction following, style, and domain-specific behavior. Fabrikam already possesses evaluation data containing input- output pairs, which aligns directly with the SFT data model.
For poor RAG response accuracy , the first action is prompt engineering . The case explicitly requires advanced fine-tuning only when prompt engineering is insufficient. In a RAG system, prompt engineering determines how the model interprets retrieved context, constrains answers to grounding information, handles missing evidence, and formats responses. Microsoft notes that inadequate RAG prompting can produce false or incomplete answers even when retrieval returns appropriate content.
DPO is primarily appropriate for alignment using preferred versus non-preferred responses, while RFT targets complex reward-based reasoning optimization. Neither is the initial technique for domain specialization in this requirement.
Study Guide Reference: Optimize generative AI systems and model performance - prompt engineering, RAG optimization, supervised fine-tuning, preference optimization, and model customization strategy.
You are monitoring a fine-tuned large language model deployed in Microsoft Foundry.
You evaluate the model before and after fine-tuning by using the same evaluation dataset.
You review the following evaluation results:
You need to determine whether the fine-tuned model shows improved performance without introducing regression.
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:
When evaluating a fine-tuned model against the base model using the same evaluation dataset, the interpretation of each metric requires careful analysis. A statement that the fine-tuned model improves on the target task is True if and only if the target metric such as task-specific accuracy, F1 score, or ROUGE score shows a statistically meaningful improvement. A statement about regression on a complementary metric is True if the fine-tuned model ' s score on that metric is meaningfully lower than the base model ' s. In Microsoft Foundry ' s evaluation framework, both pre-fine-tuning and post-fine-tuning results are stored against the same experiment, enabling direct side-by-side comparison. The core principle is that improvement on the primary task is not sufficient if fine-tuning causes degradation on safety or coherence - this is called catastrophic forgetting, and the evaluation dataset is designed to detect it.
Microsoft Learn Reference Topic: Evaluate fine-tuned models in Microsoft Foundry - Compare base and fine- tuned model metrics
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