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| Section | Objectives |
|---|---|
| Design and implement a GenAIOps infrastructure | - Manage API keys, rate limits, and responsible AI guardrails - Configure prompt orchestration, prompt flows, and agent frameworks - Set up Microsoft Foundry and Azure AI services for generative AI workloads - Implement RAG (Retrieval-Augmented Generation) pipelines and vector search |
| Design and implement an MLOps infrastructure | - Set up Azure Machine Learning workspace and compute targets - Implement security, governance, and compliance for MLOps - Manage environments, data stores, and model registries - Configure source control, CI/CD pipelines, and automation for ML workflows |
| Implement generative AI quality assurance and observability | - 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 - Conduct red teaming, adversarial testing, and content filtering |
| Implement machine learning model lifecycle and operations | - Train, register, and version models using Azure Machine Learning - Deploy models to real-time and batch endpoints - Monitor model performance, data drift, and operational health - Retrain, update, and manage model versions in production |
| Optimize generative AI systems and model performance | - Tune prompts, system messages, and grounding strategies - Optimize inference performance, caching, and throughput - Fine-tune and distill models for specific use cases - Implement cost management and scaling strategies for GenAI workloads |
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NEW QUESTION # 166
Hotspot Question
You have an Azure Machine Learning workspace.
You plan to use Azure Machine Learning Python SDK v2 to define a pipeline component that trains an image classification model. The execution logic of the component is contained in the train() function in the file named model_train.py.
You write code to import all required libraries and store it as train_component.py in the same folder that contains model_train.py.
You need to complete the remaining code in train_component.py.
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: command_component
The @command_component decorator transforms a standard Python function into a reusable pipeline component within Azure ML SDK v2.from model_ Box 2: model_train model_train import train: Because model_train.py resides in the same directory as train_component.py, you import the file directly by its module name (model_train) to access its execution logic inside the component function.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-create-component-pipeline-python
NEW QUESTION # 167
You have an Azure Machine Learning workspace.
You have the following code:
You plan to rely on serverless compute to train a model by using Azure Machine Learning Python SDK v2.
The serverless compute must use a designated number of nodes of a specific virtual machine type.
You need to modify the code to run the training job according to the plan.
How should you modify the command object? To answer, select the appropriate oations in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 168
Hotspot Question
You manage an Azure Machine Learning workspace. You configure an automated machine learning regression training job by using the Azure Machine Learning Python SDK v2.
You configure the regression job by using the following script:
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Yes
Yes, the automated machine learning (AutoML) training job will terminate early if the primary metric score stops improving after a specific number of iterations.
According to the official Microsoft Azure Machine Learning SDK v2 documentation, setting enable_early_termination = True activates the early stopping policy for the overall experiment.
How Early Termination Works in AutoML v2
The internal early stopping logic operates on a built-in schedule to avoid premature termination:
First 20 iterations: No early stopping takes place (these serve as landmarks).
From the 21st iteration onward: The early stopping window activates.
Termination trigger: The job will automatically stop if the primary evaluation score fails to improve across 10 consecutive iterations.
Box 2: Yes
Yes, a maximum of five trials can run at the same time.
The limit comes from the code settings.
The max_concurrent_trials = 5 line tells Azure to run up to five trials at once.
Box 3: No
No, a single AutoML trial cannot run for 60 minutes before it is terminated.
Parameter Breakdown
In the Azure Machine Learning Python SDK v2, the set_limits() method handles timeouts via two distinct parameters:timeout_minutes = 60: This sets the maximum duration for the entire AutoML job (the experiment as a whole), including data preparation, featurization, and all training trials combined.
trial_timeout_minutes: This parameter governs the maximum time allowed for an individual trial (a single model training run) before termination.
Conclusion
Because trial_timeout_minutes is omitted from your script, it defaults to its standard system value (which is typically 20 minutes for tabular datasets). Therefore, an individual trial will time out much earlier than 60 minutes. Additionally, since the entire job terminates at 60 minutes, it is physically impossible for a single trial to consume the full 60 minutes without forcing the termination of the remaining concurrent runs.
Box 4: No
No, the AutoML trial cannot take up to 1 month before it terminates.
Based on the script parameters, the job will terminate after a maximum of 60 minutes.
Reference:
https://learn.microsoft.com/en-us/python/api/azure-ai-ml/azure.ai.ml.automl.regressionjob?view=azure-python
NEW QUESTION # 169
A team uses a hosted Git repository to store training code and pipeline definitions of a machine learning experiment.
The team must ensure that access to the repository is granted without requiring each developer to store personal access tokens on their machines.
Repository access must be secure and centrally managed to reduce credential spread.
You need to enable secure access between an Azure Machine Learning workspace and the repository.
What should you do?
Answer: B
Explanation:
Configuring the workspace to use a managed identity (or a centralized service principal/workspace identity) is the correct approach to securely and centrally manage repository access without spreading personal access tokens.
No Developer Credentials: Developers do not need to generate, share, or store personal access tokens (PATs) on their local machines.
Centralized Management: Access permissions are bound to the cloud workspace resource rather than individual user accounts.
Reduced Credential Spreading: Eliminates the risk of leaked tokens when team members leave or compromise local workstations.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-identity-based-service-authentication
NEW QUESTION # 170
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: B
Explanation:
To share validated assets between development and production workspaces while maintaining centralized governance and version control, you should use Azure Machine Learning Registries.
Microsoft Learn
Unlike standard workspaces, registries are workspace-agnostic. They act as a central catalog that decouples assets from individual environments, allowing you to "promote" a model or environment from Dev to Prod without manual duplication or data drift.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/concept-machine-learning-registries- mlops
NEW QUESTION # 171
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