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| Section | Objectives |
|---|---|
| Design and implement an MLOps infrastructure | - Configure source control, CI/CD pipelines, and automation for ML workflows - Set up Azure Machine Learning workspace and compute targets - Manage environments, data stores, and model registries - Implement security, governance, and compliance for MLOps |
| Design and implement a GenAIOps infrastructure | - Set up Microsoft Foundry and Azure AI services for generative AI workloads - Manage API keys, rate limits, and responsible AI guardrails - Configure prompt orchestration, prompt flows, and agent frameworks - Implement RAG (Retrieval-Augmented Generation) pipelines and vector search |
| Implement generative AI quality assurance and observability | - Conduct red teaming, adversarial testing, and content filtering - Monitor latency, token usage, cost, and error rates - Evaluate generative AI outputs for quality, safety, and grounding - Implement logging, tracing, and telemetry for GenAI applications |
| Optimize generative AI systems and model performance | - Fine-tune and distill models for specific use cases - Optimize inference performance, caching, and throughput - Tune prompts, system messages, and grounding strategies - Implement cost management and scaling strategies for GenAI workloads |
| Implement machine learning model lifecycle and operations | - Retrain, update, and manage model versions in production - Deploy models to real-time and batch endpoints - Monitor model performance, data drift, and operational health - Train, register, and version models using Azure Machine Learning |
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NEW QUESTION # 93
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.
You manage an Azure Machine Learning workspace. The Python script named script.py reads an argument named training_data. The training_data argument specifies the path to the training data in a file named dataset1.csv.
You plan to run the script.py Python script as a command job that trains a machine learning model.
You need to provide the command to pass the path for the dataset as a parameter value when you submit the script as a training job.
Solution: python script.py dataset1.csv
Does the solution meet the goal?
Answer: A
Explanation:
Correct:
* python script.py --training_data ${{inputs.training_data}}
The scipt is named script.py.
For the parameter use ${{inputs.training_data}}
Incorrect:
* python script.py --training_data dataset1.csv
* python script.py dataset1.csv
* python train.py --training_data training_data
Note: Read a TabularDataset, Example
In the Input object, specify the type as AssetTypes.MLTABLE, and mode as InputOutputModes.DIRECT:
* Details omitted*
job = command(
code="./src", # Local path where the code is stored
*-> command="python train.py --inputs ${{inputs.input_data}}",
inputs=my_job_inputs,
environment="<environment_name>:<version>",
compute="cpu-cluster",
)
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-read-write-data-v2
NEW QUESTION # 94
An organization maintains separate Azure Machine Learning workspaces for development and production.
Both environments must use the same validated assets without duplicating them.
Assets must be shared across workspaces while maintaining centralized governance and version control.
You need to enable reuse of assets across workspaces without copying them.
What should you do?
Answer: A
NEW QUESTION # 95
You create a workspace by using Azure Machine Learning Studio.
You must run a Python SDK v2 notebook in the workspace by using Azure Machine Learning Studio. You must preserve the current values of variables set in the notebook for the current instance.
You need to maintain the state of the notebook.
What should you do?
Answer: C
NEW QUESTION # 96
A team is experimenting with traditional models for a classification workflow in Azure Machine Learning.
The team requires a consistent way to manage assets that are created during experimentation.
You need to ensure that artifacts can be reused and governed across projects.
Which asset should you register?
Answer: A
Explanation:
In an Azure Machine Learning classification workflow, you should register Models.
Registration creates a versioned asset in your workspace or a centralized registry, which is essential for ensuring that artifacts are reusable, governed, and trackable across different projects and environments.
Key Assets for Reuse and Governance
To maintain a consistent and governed workflow, you should focus on registering these specific assets:
Models: The primary artifact. Registering a model allows you to track its lineage (which experiment created it), version it, and deploy it consistently across environments.
Components: These are self-contained pieces of code that perform specific steps in a pipeline (e.g., data cleaning, training). Registering them allows different teams to reuse the same
"traditional" classification logic without rewriting code.
Environments: Encapsulates the software dependencies (Python packages, Docker images) required for your model to run. Registering these ensures reproducibility across different compute targets.
Data Assets: Registering your training and testing datasets as versioned assets ensures that you can always audit exactly what data was used to train a specific model version.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/concept-azure-machine-learning-v2
NEW QUESTION # 97
You create a multi-class image classification model with automated machine learning in Azure Machine Learning.
You need to prepare labeled image data as input for model training in the form of an Azure Machine Learning tabular dataset.
Which data format should you use?
Answer: C
Explanation:
Azure Machine Learning, you should use the JSON Lines (.jsonl) format.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-prepare-datasets-for-automl-images?view=azureml-api-2
NEW QUESTION # 98
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