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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
|
| Design and implement a GenAIOps infrastructure | 20–25% | - Set up Microsoft Foundry environment
|
| Optimize generative AI systems and model performance | 15–20% | - Improve efficiency and cost-effectiveness
|
| Implement machine learning model lifecycle and operations | 25–30% | - Monitor and maintain models in production
|
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NEW QUESTION # 85
Hotspot Question
You use Azure Machine Learning to train models across multiple experiments by using the same workspace.
You must record training runs in a centralized location to compare results from different jobs.
During training, performance values must be captured so they appear in the experiment run history.
You need to configure experiment tracking.
What should you configure for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 86
A team is validating a generative AI assistant for a company. The assistant generates responses by using internal knowledge sources.
The company requires assurance that responses are accurate, supported by sources, and related to the user prompts before enabling production access.
You need to implement quality metrics that confirm the assistant produces reliable and meaningful responses.
Which two evaluation metrics should you use? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: C,D
Explanation:
To ensure your Azure Machine Learning generative AI assistant (which utilizes a Retrieval- Augmented Generation or RAG architecture) produces reliable and meaningful responses before going live, you should use the RAG Triad of built-in quality evaluation metrics.
These primary automated metrics-Groundedness, Relevance, and Response Completeness- directly measure accuracy, connection to internal knowledge sources, and alignment with user prompts.
The Primary Metrics (The RAG Triad)
These metrics are evaluated on a 1-to-5 scale using Azure Machine Learning's built-in, AI- assisted "LLM-as-a-judge" evaluators:
Groundedness: Measures how well the assistant's generated answer aligns only with the information retrieved from your internal knowledge sources. Even if a response is factually correct in the real world, it is penalized if the information cannot be verified inside the retrieved context document. This is your primary defense against hallucinations.
Relevance: Assesses how pertinently the model's generated response directly addresses the user's specific prompt. This checks whether the system understood the user's intent or if it provided an off-topic or distracted response.
Response Completeness: Focuses on the "recall" aspect of the assistant. It measures whether the generated text effectively answers all parts of the user prompt using the ground truth data, ensuring no critical insights or data points are omitted.
Reference:
https://medium.com/thedeephub/a-deep-dive-into-evaluation-in-azure-prompt-flow-dd898ebb158c
NEW QUESTION # 87
You are reviewing a dataset that will be used for an advanced fine-tuning job in Microsoft Foundry.
The fine-tuning job uses preference comparison data.
You review the following dataset excerpt.
For each of the following statements, select Yes if the statement is true. Otherwise, select No . NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Preference comparison data with chosen versus rejected response pairs is the input format for Direct Preference Optimization (DPO) or RLHF-style fine-tuning - an advanced fine-tuning technique available in Microsoft Foundry. A valid DPO dataset record must have three fields: a prompt as the input, a chosen field containing the preferred response, and a rejected field containing the less preferred response. The file must be in JSONL format with UTF-8 encoding, where each line represents one complete preference pair. When evaluating statements about this dataset, mark True if the dataset contains all three required fields and chosen responses represent higher-quality outputs than rejected ones. Mark False if the format is incompatible with DPO requirements, if the required rejected field is missing, or if the chosen and rejected responses appear to be of equivalent quality with no clear preference signal.
Microsoft Learn Reference Topic: Advanced fine-tuning with preference data in Microsoft Foundry - DPO dataset format
NEW QUESTION # 88
You manage an Azure Machine Learning workspace. You create an experiment named experiment1 by using the Azure Machine Learning Python SDK v2 and MLflow.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
Answer:
Explanation:
Explanation:
NEW QUESTION # 89
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 must preserve the current values of variables set in the notebook for the current instance.
You need to maintain the state of the notebook.
What should you do?
Answer: C
Explanation:
To preserve the current values of variables set in the notebook and maintain the state for the current instance, you should stop the current kernel.
Memory State Retention: In Azure Machine Learning Studio notebooks, stopping the notebook kernel halts execution while allowing the compute instance to continue running.
Avoiding State Loss: While stopping or resetting the entire compute instance will completely clear your active in-memory variables and notebook state, cleanly stopping the kernel natively preserves your current progress and data states within that active notebook session.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/quickstart-create-resources
NEW QUESTION # 90
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