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| Section | Objectives |
|---|---|
| Optimize generative AI systems and model performance | - Implement cost management and scaling strategies for GenAI workloads - Optimize inference performance, caching, and throughput - Tune prompts, system messages, and grounding strategies - Fine-tune and distill models for specific use cases |
| Design and implement an MLOps infrastructure | - Configure source control, CI/CD pipelines, and automation for ML workflows - Manage environments, data stores, and model registries - Implement security, governance, and compliance for MLOps - Set up Azure Machine Learning workspace and compute targets |
| Implement machine learning model lifecycle and operations | - Monitor model performance, data drift, and operational health - Retrain, update, and manage model versions in production - Train, register, and version models using Azure Machine Learning - Deploy models to real-time and batch endpoints |
| Implement generative AI quality assurance and observability | - Monitor latency, token usage, cost, and error rates - Implement logging, tracing, and telemetry for GenAI applications - Conduct red teaming, adversarial testing, and content filtering - Evaluate generative AI outputs for quality, safety, and grounding |
| Design and implement a GenAIOps infrastructure | - Configure prompt orchestration, prompt flows, and agent frameworks - Implement RAG (Retrieval-Augmented Generation) pipelines and vector search - Manage API keys, rate limits, and responsible AI guardrails - Set up Microsoft Foundry and Azure AI services for generative AI workloads |
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NEW QUESTION # 105
Drag and Drop Question
You are fine-tuning an LLM base model by using Microsoft Foundry. You have a labeled dataset of customer emails.
You need to improve task-specific prediction accuracy so that the model can be tested and deployed later.
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.
Answer:
Explanation:
Explanation:
Step 1: Upload and format the training dataset in JSON format.
Upload and Format Training Data (JSONL)
You need to format your labeled emails so the model can learn from them.
Format Requirements: In Microsoft Foundry, training and validation files must be prepared in a JSONL (JSON Lines) format.
Structure: Each line should represent a single conversation or input-output pair. For email tasks (like classification, summarization, or routing), this usually consists of a messages array.
Step 2: Start the fine-tuning jobs in Microsoft Foundry.
Start Supervised Fine-Tuning (SFT) Jobs
Once your data is uploaded to a private Azure storage account or directly through the UI, you will initiate the training process.
Technique: Choose Supervised Fine-Tuning (SFT). SFT is designed for teaching models specific tasks (like specific tone alignment or labeling) on labeled data.
Execution: Through the Azure AI Foundry Model Catalog, select your base model (e.g., GPT-4o- mini or Llama Scout), upload your train/validation JSONL files, and submit the job.
Experimentation: Foundry also supports hyperparameter settings (like learning rates and batch sizes) which you can tweak if needed.
Step 3: Evaluate the performance of the model on a validation dataset.
Evaluate and Deploy
Fine-tuning is an iterative process. Before deploying to a production environment, you must evaluate the model to ensure it meets your specific task accuracy goals.
Evaluation: Use Foundry's built-in evaluation tools and your validation dataset to benchmark the fine-tuned student model against the baseline base model.
Deployment: Once task-specific prediction accuracy is confirmed using validation metrics, you can host and deploy the model directly on Azure AI Foundry for your applications to consume.
Reference:
https://devblogs.microsoft.com/foundry/beyond-the-prompt-why-and-how-to-fine-tune-your-own-models/
NEW QUESTION # 106
DRAG DROP
A team deploys a classification model to production and scores incoming customer data daily.
After several weeks, business stakeholders report unexpected changes in prediction behavior, even though the endpoint remains healthy.
You need to determine whether data drift is occurring and if it is, identify the appropriate actions.
Which action should you perform for each observed signal? To answer, move the appropriate actions to the correct observed signals. You may use each action 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.
Answer:
Explanation:
NEW QUESTION # 107
You are training machine learning models in Azure Machine Learning. You use Hyperdrive to tune the hyperparameters.
In previous model training and tuning runs, many models showed similar performance.
You need to select an early termination policy that meets the following requirements:
- accounts for the performance of all previous runs when evaluating the current run
- avoids comparing the current run with only the best performing run to date Which two early termination policies should you use? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: A,D
Explanation:
The Median Stopping Policy and the Truncation Selection Policy are the most appropriate early termination policies for this scenario. Both evaluate runs based on the performance of all previous runs instead of strictly benchmarking against the single best run.
Median Stopping Policy: This policy calculates the running averages of the primary metric across all historical and current training runs at each evaluation interval. It terminates any ongoing run if its performance is worse than the median of those averages. This directly accounts for all previous run performances rather than just the single best run.
Truncation Selection Policy: This policy evaluates all active runs at each interval and terminates a specified bottom percentage (X%) of the lowest-performing runs. Because it aggregates and compares the entire cohort of runs, it avoids making narrow comparisons against only the best- performing run.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters
NEW QUESTION # 108
-
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.
Answer:
Explanation:
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.
NEW QUESTION # 109
You need to standardize how Fabrikam Inc. manages machine learning assets.
Which action should you perform first?
Answer: B
Explanation:
In Azure Machine Learning, a workspace is the top-level resource that acts as a centralized hub for every ML activity. It stores experiments, pipelines, datasets, models, environments, and compute targets all in one place.
Before you can register assets or deploy endpoints, the workspace itself must exist and be shared across the team. Fabrikam ' s core challenge is inconsistent experiment tracking, ad-hoc versioning, and no standardized deployment path - all symptoms of the absence of a single, governed workspace. Option A (registering assets) is a subsequent action only possible after the workspace exists. Option C (online endpoint) is a deployment concern, not an asset-management foundation. Option D (Foundry project) is appropriate for generative AI workloads, not for the traditional ML pipeline standardization described here. The workspace is the prerequisite that makes every other governance action possible.
Microsoft Learn Reference Topic: Azure Machine Learning workspaces - Microsoft Learn: Manage Azure Machine Learning workspaces
NEW QUESTION # 110
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