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| Section | Weight | Objectives |
|---|---|---|
| Design and implement a GenAIOps infrastructure | 20–25% | - Implement infrastructure for generative AI workloads
|
| Design and implement an MLOps infrastructure | 15–20% | - Create and manage Machine Learning workspace resources and assets
|
| Optimize generative AI systems and model performance | 15–20% | - Improve efficiency and cost-effectiveness
|
| Implement machine learning model lifecycle and operations | 25–30% | - Deploy models to production
|
| Implement generative AI quality assurance and observability | 10–15% | - Evaluate and test generative AI applications
|
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NEW QUESTION # 149
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?
Answer: B
Explanation:
Fine-tuning is a data-driven process, and you cannot begin configuring or running a fine-tuning job without a properly formatted dataset. Microsoft ' s documentation explicitly states that your training data must be in JSONL format with prompt-completion pairs before you can initiate any fine-tuning job in Microsoft Foundry or Azure OpenAI Studio. Option A (prompt flow templates) is irrelevant at this stage since prompt engineering is a pre-fine-tuning step. Option B (deploying the base model first) is incorrect; fine-tuning trains the weights of the base model without requiring a prior deployment. Option C (enabling tracing in evaluation pipelines) is a post-deployment observability step, not a prerequisite for fine-tuning. Dataset preparation and upload is always the foundational first step in the fine-tuning workflow - without correctly formatted data, no other step can proceed.
Microsoft Learn Reference Topic: Fine-tune models in Azure AI Foundry - Prepare your training dataset
NEW QUESTION # 150
Your ML pipeline contains independent feature engineering steps that currently execute sequentially, increasing overall runtime. You want to optimize execution without modifying logic.
What is the BEST solution?
Answer: C
Explanation:
Parallel execution allows independent pipeline steps to run simultaneously, reducing total execution time without altering logic. This is more efficient than increasing compute resources, which may raise costs without guaranteeing proportional performance improvements.
NEW QUESTION # 151
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 # 152
Hotspot Question
You manage a Retrieval-Augmented Generation (RAG) system that retrieves internal policy documents from a vector index.
Recent analysis shows that:
- Retrieved results frequently include duplicated content from the same document.
- Retrieved chunks sometimes span unrelated policy sections.
You review the following retrieval and ingestion configurations:
You need to reduce duplicated retrieval results and improve chunk relevance across policy sections. 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:
NEW QUESTION # 153
A financial services company is deploying Microsoft Foundry to host generative AI workloads that process regulated customer data. The Microsoft Foundry environment must prevent any public network exposure while still allowing services managed by Microsoft Foundry to communicate with dependent Azure resources.
Security auditors require that all traffic to and from the Microsoft Foundry resource remain on private networks, with no public endpoints available.
You need to configure the Microsoft Foundry environment so that network access is restricted while maintaining full platform functionality.
Which two actions should you perform? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point. Choose two .
Answer: B,C
Explanation:
Microsoft ' s Azure AI Foundry documentation describes two required configurations for fully private network isolation. First, configure a managed virtual network for the Foundry resource: this provisions a Microsoft-managed VNet that encapsulates all internal service communications - the Foundry control plane communicates with dependent Azure services such as storage, key vault, and cognitive services over private endpoints within this managed VNet, so no traffic crosses the public internet. Second, disable public network access to the Foundry resource: this removes the public endpoint entirely, ensuring that only clients on approved private networks via private endpoint connection can reach it. Together, these two actions ensure all traffic remains on private networks with no public endpoints available, satisfying the security auditor requirement for complete network isolation.
Microsoft Learn Reference Topic: Configure managed virtual network for Azure AI Foundry - Network isolation and private endpoints
NEW QUESTION # 154
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