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| Section | Weight | Objectives |
|---|---|---|
| Implement machine learning model lifecycle and operations | 25–30% | - Deploy models to production
|
| Design and implement a GenAIOps infrastructure | 20–25% | - Implement infrastructure for generative AI workloads
|
| 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
|
| Optimize generative AI systems and model performance | 15–20% | - Improve efficiency and cost-effectiveness
|
研究により、学習への関心を刺激することが最善の解決策であることがわかっています。したがって、AI-300準備ガイドの焦点は、AI-300試験の準備方法を変更することにより、厳格で無駄なメモリモードを改革することです。 AI-300実践教材のソフトバージョンは、知識と最新テクノロジーを組み合わせて学習力を大幅に刺激します。楽しい学習シーンと鮮明な説明をシミュレートすることにより、ユーザーは資格のあるAI-300試験に合格する自信が大きくなります。
質問 # 169
You train models on GPU-enabled clusters but deploy them on CPU-based endpoints. Recently, inference failures occur due to incompatible dependencies. What should you do to ensure consistency?
正解：D
解説：
Defining and reusing environment configurations ensures that dependencies remain consistent between training and inference. This prevents runtime errors caused by mismatched libraries.
Using identical compute resources is unnecessary and inefficient, as consistency depends on environment configuration rather than hardware.
質問 # 170
You manage a Microsoft Foundry project. You build a multi-turn chatbot application.
You plan to filter your traces to identify issues while observing how the application is responding.
The solution must not use an external knowledge base.
You need to select an evaluation metric.
Which built-in evaluator should you use?
正解：C
解説：
In a Microsoft Foundry project for a multi-turn chatbot, the best built-in evaluator to use-- especially when you want to avoid an external knowledge base--is the Coherence evaluator or a combination of Agentic Evaluators.
Because your application is multi-turn, you need to observe how the AI maintains a logical flow and resolves user intent over several exchanges. Since you specifically want to avoid an external knowledge base (eliminating "Groundedness" or "Retrieval" metrics), you should focus on quality metrics that only require the conversation history itself.
Reference:
https://www.cekura.ai/blogs/why-single-turn-testing-falls-short-in-evaluating-conversational-ai
質問 # 171
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.
You have an Azure Machine Learning workspace. You connect to a terminal session from the Notebooks page in Azure Machine Learning studio.
You plan to add a new Jupyter kernel that will be accessible from the same terminal session.
You need to perform the task that must be completed before you can add the new kernel.
Solution: Delete the Python 3.8 - AzureML kernel.
Does the solution meet the goal?
正解：A
解説：
No. Deleting an existing Azure Machine Learning kernel is neither required nor appropriate before adding another Jupyter kernel. Azure Machine Learning compute instances are explicitly designed to support multiple kernels simultaneously . Microsoft states that notebooks automatically discover the Jupyter kernels installed on the connected compute instance, allowing users to switch between them from the kernel selector.
To add a kernel, the supported process is to create a new Conda environment, activate it, install pip and ipykernel, and then register that environment by using python -m ipykernel install --user. Existing kernels do not need to be removed.
More importantly, Microsoft warns against deleting Conda environments or Jupyter kernels that you did not create , because doing so can damage Jupyter or JupyterLab functionality on the Azure Machine Learning compute instance. This makes removal of a built-in AzureML kernel particularly inappropriate as a prerequisite for creating another kernel.
The correct approach is therefore to leave the existing kernel intact and create a separate environment/kernel for the new workload.
Study Guide Reference: Design and implement an MLOps infrastructure - Azure Machine Learning notebook environments, kernel management, compute instances, and dependency isolation.
質問 # 172
You manage an Azure Machine Learning workspace.
An MLflow model is already registered. You plan to customize how the deployment does inference. You need to deploy the MLflow model to a batch endpoint for batch inferencing. What should you create first?
正解：C
質問 # 173
You are monitoring a fine-tuned large language model deployed in Microsoft Foundry.
You evaluate the model before and after fine-tuning by using the same evaluation dataset.
You review the following evaluation results:
You need to determine whether the fine-tuned model shows improved performance without introducing regression.
For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation:
When evaluating a fine-tuned model against the base model using the same evaluation dataset, the interpretation of each metric requires careful analysis. A statement that the fine-tuned model improves on the target task is True if and only if the target metric such as task-specific accuracy, F1 score, or ROUGE score shows a statistically meaningful improvement. A statement about regression on a complementary metric is True if the fine-tuned model ' s score on that metric is meaningfully lower than the base model ' s. In Microsoft Foundry ' s evaluation framework, both pre-fine-tuning and post-fine-tuning results are stored against the same experiment, enabling direct side-by-side comparison. The core principle is that improvement on the primary task is not sufficient if fine-tuning causes degradation on safety or coherence - this is called catastrophic forgetting, and the evaluation dataset is designed to detect it.
Microsoft Learn Reference Topic: Evaluate fine-tuned models in Microsoft Foundry - Compare base and fine- tuned model metrics
質問 # 174
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