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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Optimize generative AI systems and model performance | 15–20% | - Improve efficiency and cost-effectiveness
|
| Topic 2: Implement machine learning model lifecycle and operations | 25–30% | - Deploy models to production
|
| Topic 3: Design and implement a GenAIOps infrastructure | 20–25% | - Implement infrastructure for generative AI workloads
|
| Topic 4: Implement generative AI quality assurance and observability | 10–15% | - Evaluate and test generative AI applications
|
| Topic 5: Design and implement an MLOps infrastructure | 15–20% | - Implement infrastructure as code for Machine Learning
|
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NEW QUESTION # 154
You manage an Azure Machine learning workspace. You develop a machine learning model.
You must deploy the model to use a low-priority VM with a pricing discount.
You need to deploy the model.
Which compute target should you use?
Answer: B
Explanation:
The key concept here is low-priority (spot) VMs, which are available at a significant discount because Azure can reclaim them at any time. Azure Machine Learning compute clusters are the only target in the list that directly supports low-priority VM nodes as a cost-saving configuration. You set the minimum and maximum node counts and specify that new nodes should be provisioned as low-priority. Azure Container Instances (ACI) does not support low-priority pricing. Local deployment runs on the developer ' s machine with no Azure billing model. Azure Kubernetes Service (AKS) does support spot node pools but requires significantly more infrastructure management and is not the primary mechanism for low-priority compute in Azure Machine Learning. The exam tests whether you know that AML compute clusters are the managed way to leverage low-priority discounts inside Azure Machine Learning.
Microsoft Learn Reference Topic: Create and manage Azure Machine Learning compute clusters - Low- priority VMs
NEW QUESTION # 155
You have a deployment of an Azure OpenAI Service base model.
You plan to fine-tune the model.
You need to prepare a file that contains training data for multi-turn chat.
Which file encoding method should you use?
Answer: A
Explanation:
UTF-8 is the universal encoding standard: it is backward-compatible with ASCII for standard Latin characters, supports every Unicode code point covering over 140,000 characters, and is the de facto standard for all modern APIs including Azure OpenAI. This matters for fine-tuning because training data often contains diverse characters from multiple languages, special punctuation, and domain-specific symbols. If they use ASCII encoding, any accented character or non-Latin script will be corrupted or lost entirely. UTF-
16 adds a Byte Order Mark and can cause parsing issues with tools that expect standard JSONL. ISO-8859-1 covers Western European characters only and fails immediately with CJK or Arabic scripts. The Azure OpenAI fine-tuning documentation explicitly states that training files must be UTF-8 encoded, making it the only safe choice.
Microsoft Learn Reference Topic: Azure OpenAI fine-tuning - File format and encoding requirements
NEW QUESTION # 156
A team is developing a generative AI assistant. The team is experimenting with multiple prompt variants to improve the user experience.
When comparing prompt variants, the team plans to assess whether the generated responses are grammatically correct.
You need to evaluate the quality of the language from the generated responses.
Which evaluator should you use?
Answer: D
Explanation:
The best evaluator within the Microsoft ecosystem for checking the grammatical correctness of generative AI responses--especially when testing multiple prompt variants--is the Azure AI Evaluator for Fluency, available within Azure AI Foundry.
Fluency Evaluator (builtin.fluency)
Purpose: Specifically designed to measure the effectiveness and clarity of written communication.
Grammatical Focus: It assesses grammatical accuracy, sentence structure, punctuation, and vocabulary usage in AI-generated text.
Result: It provides a 1-5 Likert scale score, allowing you to compare which prompt variants produce the most grammatically correct, natural-sounding responses.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/concept-model-monitoring- generative-ai-evaluation-metrics
NEW QUESTION # 157
Hotspot Question
A team manages an Azure Machine Learning workspace to train and register machine learning models.
Previous model versions must be retained for audit and rollback purposes but must not be used for new deployments.
You need to manage model versions.
What should you do 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 # 158
A pipeline step fails intermittently due to transient compute issues. You need to improve reliability without modifying core logic or increasing cost significantly. What is the BEST approach?
Answer: A
Explanation:
Retry policies allow pipeline steps to automatically recover from transient failures, such as temporary compute or network issues. This improves reliability without modifying core logic or increasing infrastructure costs. Increasing compute resources does not address transient failure scenarios effectively.
NEW QUESTION # 159
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