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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement generative AI quality assurance and observability | 10–15% | - Monitor generative AI systems
|
| Topic 2: Design and implement an MLOps infrastructure | 15–20% | - Implement infrastructure as code for Machine Learning
|
| Topic 3: Design and implement a GenAIOps infrastructure | 20–25% | - Set up Microsoft Foundry environment
|
| Topic 4: Implement machine learning model lifecycle and operations | 25–30% | - Register, version, and package models
|
| Topic 5: Optimize generative AI systems and model performance | 15–20% | - Improve efficiency and cost-effectiveness
|
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NEW QUESTION # 130
You need to configure an optimization method to meet Fabrikam Inc.'s technical requirements.
Which strategy should you apply first? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Domain specialization: Supervised fine-tuning
Poor response accuracy of a RAG-based solution: Apply prompt engineering For domain specialization , Supervised Fine-Tuning (SFT) is the correct first strategy. Microsoft describes SFT as the foundational fine-tuning technique for training a model from labeled input-output pairs , and specifically identifies domain specialization as one of its principal use cases. Microsoft also recommends starting with SFT for most customization projects because it supports task specialization, instruction following, style, and domain-specific behavior. Fabrikam already possesses evaluation data containing input- output pairs, which aligns directly with the SFT data model.
For poor RAG response accuracy , the first action is prompt engineering . The case explicitly requires advanced fine-tuning only when prompt engineering is insufficient. In a RAG system, prompt engineering determines how the model interprets retrieved context, constrains answers to grounding information, handles missing evidence, and formats responses. Microsoft notes that inadequate RAG prompting can produce false or incomplete answers even when retrieval returns appropriate content.
DPO is primarily appropriate for alignment using preferred versus non-preferred responses, while RFT targets complex reward-based reasoning optimization. Neither is the initial technique for domain specialization in this requirement.
Study Guide Reference: Optimize generative AI systems and model performance - prompt engineering, RAG optimization, supervised fine-tuning, preference optimization, and model customization strategy.
NEW QUESTION # 131
Hotspot Question
You create an Azure Machine Learning workspace.
You must use the Python SDK v2 to implement an experiment from a Jupyter notebook in the workspace. The experiment must log a table in the following format:
You need to complete the Python code to log the table.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: dump
Log a table, SDK v2 with MLflow
# Add a metric for each column prefixed by metric name. Similar to log_row row1 = {"table.col1": 5, "table.col2": 10}
# To be done for each row in the table
mlflow.log_metrics(row1)
# Using mlflow.log_artifact
import json
with open("table.json", 'w') as f:
json.dump(table, f)
mlflow.log_artifact("table.json")
Box 2: mlflow.log_artifact
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/reference-migrate-sdk-v1-mlflow-tracking
NEW QUESTION # 132
You manage an Azure Machine Learning workspace.
You experiment with an MLflow model that trains interactively by using a notebook in the workspace. You need to log dictionary type artifacts of the experiments in Azure Machine Learning by using MLflow. Which syntax should you use?
Answer: C
NEW QUESTION # 133
A company is creating an internal tool that summarizes long meeting transcripts and extracts action items.
The model must:
Process text inputs up to 200k tokens long.
Generate concise summaries in seconds.
Support interactive testing before integration into the app.
You need to select, deploy, and test a model that supports summarization with low latency.
How should you complete the configuration plan? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
For a tool that must process text inputs up to 200k tokens long, generate concise summaries in seconds, and support interactive testing, the configuration must address three requirements. The large token context window points to GPT-4o, which supports up to 128k tokens and is among the largest-context Azure OpenAI models available in Foundry, making it suitable for long documents such as meeting transcripts. Low latency with the need to generate responses in seconds rules out batch deployment types; Data Zone Standard provides the best latency for single-tenant enterprise use cases with zone-level routing. Interactive testing before integration points directly to Microsoft Foundry ' s built-in Chat Playground or Prompt Playground, where you can paste transcripts, adjust system prompts, and evaluate outputs interactively before writing any application integration code.
Microsoft Learn Reference Topic: Deploy and test models in Microsoft Foundry - Model selection for long- context summarization
NEW QUESTION # 134
You train and register an Azure Machine Learning model
You plan to deploy the model to an online endpoint
You need to ensure that applications will be able to use the authentication method with a non-expiring artifact to access the model.
Solution:
Create a managed online endpoint and set the value of its auth.mode parameter to aml.token. Deploy the model to the online endpoint.
Does the solution meet the goal?
Answer: B
NEW QUESTION # 135
......
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