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| Section | Weight | Objectives |
|---|---|---|
| Design and implement an MLOps infrastructure | 15–20% | - Implement infrastructure as code for Machine Learning
|
| Implement generative AI quality assurance and observability | 10–15% | - Monitor generative AI systems
|
| Implement machine learning model lifecycle and operations | 25–30% | - Monitor and maintain models in production
|
| Optimize generative AI systems and model performance | 15–20% | - Improve efficiency and cost-effectiveness
|
| Design and implement a GenAIOps infrastructure | 20–25% | - Set up Microsoft Foundry environment
|
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NEW QUESTION # 56
Hotspot Question
A team is preparing a generative AI application for production deployment. The application generates structured responses that must be evaluated for quality before each release.
The organization requires repeatable evaluation results that can be compared across builds and environments.
You need to configure evaluation inputs so quality metrics can be reliably calculated across test runs.
How should you prepare the evaluation inputs? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Reference dataset
Provide expected results for metric comparison.
A reference dataset contains predefined input prompts matched with ground-truth "expected results" (or golden outputs). To calculate quality metrics reliably and compare them across builds and environments, the evaluation framework needs this baseline data to score the generated outputs against a consistent benchmark.
Box 2: Output mapping
Align model responses to evaluation fields.
Output mapping is the process of aligning generative model responses to specific evaluation fields. For structured outputs, your evaluation dataset needs to know exactly which part of the generated response corresponds to the ground truth or target metrics. Configuring output mapping ensures that data fields are aligned consistently across different test runs, making the evaluation repeatable and comparable across builds and environments.
Box 3: Consistent test dataset
Ensure results are comparable across runs.
A consistent test dataset is used to reliably compare evaluation metrics across different builds and environments, you must test the application using the exact same inputs each time. A fixed, high-quality test dataset ensures that changes in metrics reflect changes in the model or application logic rather than variations in the test data itself.
Reference:
https://testquality.com/llm-evaluation-metrics-testing-strategies/
https://www.freecodecamp.org/news/how-to-evaluate-and-select-the-right-llm-for-your-genai-application/
NEW QUESTION # 57
Drag and Drop Question
A team is developing a generative AI assistant. The team is experimenting with two prompt variants to improve performance before rolling out changes to production.
The team observes the following prompt results:
- PromptA variant generates longer responses and may be more expensive
to operate.
- PromptB may produce lower-quality answers.
The team must control operating costs while still selecting the better-performing prompt.
You need to identify cost drivers and compare the output quality of the two prompt variances to make an informed decision.
Which actions should you perform? To answer, move the appropriate actions to the correct requirements. 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.
Answer:
Explanation:
NEW QUESTION # 58
You create a multi-class image classification deep learning model.
The model must be retrained monthly with the new image data fetched from a public web portal. You create an Azure Machine Learning pipeline to fetch new data, standardize the size of images and retrain the model.
You need to use the Azure Machine Learning Python SEX v2 to configure the schedule for the pipeline. The schedule should be defined by using the frequency and interval properties with frequency set to month ' and interval set to " 1:
Which three classes should you instantiate 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:
NEW QUESTION # 59
Drag and Drop Question
You have an Azure Machine Learning workspace named WS1 and a GitHub account named account1 that hosts a private repository named repo1.
You need to clone repo1 to make it available directly from WS1. The configuration must maximize the performance of the repo1 clone.
Which four actions should you perform in sequence?
Answer:
Explanation:
Explanation:
Step 1: Create a compute instance
Git integration for Azure Machine Learning
Clone Git repositories into your workspace file system
Azure Machine Learning provides a shared file system for all users in the workspace. To clone a Git repository into this file share, we recommend that you create a compute instance (Step 1) & open a terminal (Step 2). Once the terminal is opened, you have access to a full Git client and can clone and work with Git via the Git CLI experience.
We recommend that you clone the repository into your user directory so that others will not make collisions directly on your working branch.
Step 2: Open a terminal window
Step 3: Generate a Secure Shell (SSH) key pair
Authenticate your Git Account with SSH
Generate a new SSH key
1. Open the terminal window in the Azure Machine Learning Notebook Tab.
2. Paste the text below, substituting in your email address.
ssh-keygen -t rsa -b 4096 -C your_email@example.com
This creates a new ssh key, using the provided email as a label.
3. When you're prompted to "Enter a file in which to save the key" press Enter. This accepts the default file location.
4. Verify that the default location is '/home/azureuser/.ssh' and press enter. Otherwise specify the location '/home/azureuser/.ssh'.
5. At the prompt, type a secure passphrase. We recommend you add a passphrase to your SSH key for added security Step 4: Add a public key to account1.
Add the public key to Git Account
1. In your terminal window, copy the contents of your public key file. If you renamed the key, replace id_rsa.pub with the public key file name.
cat ~/.ssh/id_rsa.pub
2. Select and copy the SSH key output to your clipboard.
3. Next, follow the steps to add the SSH key to your preferred account type:
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/concept-train-model-git-integration
NEW QUESTION # 60
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 on 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: Create prompt variants and compare their outputs in the Evaluation experience.
Does the solution meet the goal?
Answer: A
Explanation:
The Evaluation experience in Microsoft Foundry ' s prompt flow editor shows aggregate quality scores across prompt variants - which variant produces more coherent answers, which scores higher on groundedness.
However, it does not expose per-run raw telemetry: individual token counts per call, per-request latency in milliseconds, or the exact input-output pairs for each execution. The Evaluation experience is designed for comparative quality scoring, not for detailed operational telemetry. To capture inputs, outputs, token usage, and latencies at the granular run level, Tracing must be enabled in Microsoft Foundry. Tracing records each LLM call as a structured span with timing, token consumption, and the complete input-output payload - a fundamentally different view than evaluation scores that directly satisfies all four capture requirements.
Microsoft Learn Reference Topic: Trace and debug prompt flows in Microsoft Foundry - Tracing vs.
Evaluation
NEW QUESTION # 61
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