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| Section | Objectives |
|---|---|
| Design and implement an MLOps infrastructure | - Implement security, governance, and compliance for MLOps - Set up Azure Machine Learning workspace and compute targets - Configure source control, CI/CD pipelines, and automation for ML workflows - Manage environments, data stores, and model registries |
| Implement machine learning model lifecycle and operations | - Train, register, and version models using Azure Machine Learning - Deploy models to real-time and batch endpoints - Monitor model performance, data drift, and operational health - Retrain, update, and manage model versions in production |
| Design and implement a GenAIOps infrastructure | - Implement RAG (Retrieval-Augmented Generation) pipelines and vector search - Configure prompt orchestration, prompt flows, and agent frameworks - Set up Microsoft Foundry and Azure AI services for generative AI workloads - Manage API keys, rate limits, and responsible AI guardrails |
| Optimize generative AI systems and model performance | - Implement cost management and scaling strategies for GenAI workloads - Fine-tune and distill models for specific use cases - Tune prompts, system messages, and grounding strategies - Optimize inference performance, caching, and throughput |
| Implement generative AI quality assurance and observability | - Evaluate generative AI outputs for quality, safety, and grounding - Conduct red teaming, adversarial testing, and content filtering - Monitor latency, token usage, cost, and error rates - Implement logging, tracing, and telemetry for GenAI applications |
You are fine-tuning a base language model to analyze customer feedback.
You label examples of support tickets. You must improve classification accuracy by configuring and fine- tuning the base model in Microsoft Foundry.
You need to configure and run fine-tuning.
What should you do first?
Correct Answer: C 🗳️
Explanation: Only visible for PrepAwayTest 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.
An organization is deploying several generative AI workloads by using Microsoft Foundry. Each workload must meet different requirements related to data governance, task specialization, and operational cost control.
The organization requires models that meet the following requirements:
Model behavior aligns with the task being performed.
Data handling aligns with internal governance policies.
Operational complexity and cost are justified by workload needs.
You need to select the foundation model options that meet the requirements.
Which three models can you select? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point. Choose three.
Correct Answer: A,B,C 🗳️
Explanation: Only visible for PrepAwayTest members. You can sign-up / login (it's free).
You create an Azure Machine Learning workspace.
You must use the Python SDK v2 to implement an experiment from a Jupyter notebook in the workspace. The experiment must log string metrics. You need to implement the method to log the string metrics. Which method should you use?
Correct Answer: C 🗳️
You create a workspace by using Azure Machine Learning Studio.
You must run a Python SDK v2 notebook in the workspace by using Azure Machine Learning Studio.
You need to reset the state of the notebook.
Which three actions should you use? Each correct answer presents a complete solution.
Correct Answer: B,C,D 🗳️
Explanation: Only visible for PrepAwayTest members. You can sign-up / login (it's free).
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