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| Section | Objectives |
|---|---|
| Topic 1: Optimize generative AI systems and model performance | - Implement cost management and scaling strategies for GenAI workloads - Optimize inference performance, caching, and throughput - Tune prompts, system messages, and grounding strategies - Fine-tune and distill models for specific use cases |
| Topic 2: Design and implement a GenAIOps infrastructure | - Manage API keys, rate limits, and responsible AI guardrails - Implement RAG (Retrieval-Augmented Generation) pipelines and vector search - Set up Microsoft Foundry and Azure AI services for generative AI workloads - Configure prompt orchestration, prompt flows, and agent frameworks |
| Topic 3: Implement generative AI quality assurance and observability | - Conduct red teaming, adversarial testing, and content filtering - Implement logging, tracing, and telemetry for GenAI applications - Evaluate generative AI outputs for quality, safety, and grounding - Monitor latency, token usage, cost, and error rates |
| Topic 4: Implement machine learning model lifecycle and operations | - Monitor model performance, data drift, and operational health - Retrain, update, and manage model versions in production - Train, register, and version models using Azure Machine Learning - Deploy models to real-time and batch endpoints |
| Topic 5: Design and implement an MLOps infrastructure | - Configure source control, CI/CD pipelines, and automation for ML workflows - Implement security, governance, and compliance for MLOps - Set up Azure Machine Learning workspace and compute targets - Manage environments, data stores, and model registries |
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質問 # 20
You manage an Azure Machine Learning workspace. You create an experiment named experiment1 by using the Azure Machine Learning Python SDK v2 and MLflow. You are reviewing the results of experiment1 by using the following code segment:
For each of the following statements, Select Yes if the statement is true Otherwise, select No.
正解：
解説：
Explanation:
質問 # 21
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. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear on the review screen.
An organization provisions Azure Machine Learning workspaces for development, test, and production environments.
Each environment must be deployed consistently and updated through source control. The deployment process must be automated, repeatable, and auditable.
You need to deploy Azure Machine Learning resources in a consistent and controlled manner.
Solution: Define Azure Machine Learning resources in a Bicep template and deploy them within a GitHub Action.
Does the solution meet the goal?
正解：A
解説：
Correct:
* Define Azure Machine Learning resources in a Bicep template and deploy them within a GitHub Action.
This action is best because it fulfills all of your operational requirements:
Consistency & Controlled Manner:
Infrastructure as Code (IaC) via Bicep ensures that dev, test, and prod environments are configured identically, eliminating configuration drift.
Source Control: Storing the Bicep template in a Git repository satisfies the requirement that each environment must be updated through source control.
Automated & Repeatable: Orchestrating the deployment using GitHub Actions fully automates the workflow, allowing it to be executed reliably every time a change is merged.
Auditable: Git commit histories combined with GitHub deployment logs provide a comprehensive, compliant audit trail of exactly who modified the infrastructure and when.
Incorrect:
* Clone an existing Azure Machine Learning workspace to create additional environments.
* Create Azure Machine Learning workspaces manually in the Azure portal for each environment.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-github-actions-machine-learning
質問 # 22
You manage an Azure Machine Learning workspace named workspace1 by using the Python SDK v2.
The default datastore of workspace1 contains a folder named sample_data.
The folder structure contains the following content:
You write Python SDK v2 code to materialize the data from the files in the sample_data folder into a Pandas data frame.
You need to complete the Python SDK v2 code to use the MLTable folder as the materialization blueprint.
How should you complete the code? To answer, select the appropriate options in the answer area . NOTE:
Each correct selection is worth one point
正解：
解説：
Explanation:
The MLTable format in Azure Machine Learning Python SDK v2 is a structured abstraction over tabular data sources. It stores a YAML file alongside data files, describing how to read and transform the data. To materialize this into a Pandas DataFrame, you use the mltable library: first call mltable.load with the path to the MLTable folder to load the definition, then call to_pandas_dataframe() on the returned object. The path parameter should point to the folder containing the MLTable YAML file on the default datastore. MLTable abstracts the storage location and transformation steps, making the code storage-agnostic - the same code works whether data lives in Blob Storage, ADLS Gen2, or a local path. The SDK v2 approach with MLTable is the recommended pattern for governed, reusable data access in Azure Machine Learning.
Microsoft Learn Reference Topic: Create and use MLTable data assets in Azure Machine Learning Python SDK v2
質問 # 23
You are a data scientist working for a hotel booking website company. You use the Azure Machine Learning service to train a model that identifies fraudulent transactions.
You must deploy the model to an Azure Machine Learning online endpoint by using the Azure Machine Learning Python SDK v2. The deployed model must return real-time predictions of fraud based on transaction data input.
You need to create the script that is specified as the scoring_script parameter for the CodeConfiguration class used to deploy the model.
What should the entry script do?
正解：E
解説：
The entry script (scoring script) for an Azure Machine Learning online endpoint must initialize the model when the container starts and process incoming transaction data to return real-time fraud predictions.
init() function: Runs once when the container is initialized. It must locate and load the trained model into memory (typically using a global variable) from the path specified by the AZUREML_MODEL_DIR environment variable.
run(data) function: Executes every time the endpoint receives a real-time HTTP request. It accepts the raw transaction payload, deserializes it, processes the features, passes them to the loaded model for prediction, and returns a JSON-serializable response.
Reference:
https://docs.azure.cn/en-us/machine-learning/how-to-deploy-online-endpoints?view=azureml-api-2
質問 # 24
Hotspot Question
A team deploys a generative AI application built by using Microsoft Foundry to production and receives variable traffic throughout the day.
The team requires uninterrupted insight into the application s health and model behavior to detect issues without relying on manual inspection.
You need to select the monitoring capabilities that provide real-time operational visibility into the application.
Which monitoring capability should you use for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation:
Box 1: Real-time tracing
Observe live request flow.
To best observe the live request flow in your generative AI application, you should use Real-time tracing.
Real-time tracing maps the end-to-end journey of a request as it flows through various microservices, APIs, and AI model endpoints. It provides the exact visibility needed to pinpoint bottlenecks, latencies, and failures in the live request path immediately.
Box 2: Aggregated metrics
Identify recurring failure patterns.
To best identify recurring failure patterns in a generative AI application with variable traffic, you should use aggregated metrics.
Aggregated metrics groups data points over specified time intervals to reveal long-term trends, error rates, and system anomalies. It allows you to track patterns (like a spike in 5xx errors or model timeouts during peak traffic) over hours or days without manually inspecting individual logs.
Reference:
https://docs.azure.cn/en-us/ai-services/metrics-advisor/glossary
質問 # 25
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