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| Section | Objectives |
|---|---|
| Topic 1: Implement secure and scalable AI systems | - Security and governance
|
| Topic 2: Design and implement generative AI solutions | - Large language model integration
|
| Topic 3: Plan and design AI solutions using Azure AI services | - Requirements gathering and solution architecture
|
| Topic 4: Operationalizing machine learning solutions | - Deployment and monitoring
|
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NEW QUESTION # 145
You train a model in Azure Machine Learning.
You plan to capture experiment details for later comparison. The training code must log parameters and metrics for each run.
You review the following training script.
You need to verify whether the training script meets the experiment tracking requirement.
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:
In Azure ML command jobs, MLflow automatically creates a run context, so explicit start_run is optional.
The training script must call mlflow.log_param to record each hyperparameter for the run. If the script calls this for each hyperparameter, the requirement for capturing parameters per run is met. The script must also call mlflow.log_metric to record numeric metrics such as accuracy or loss, optionally indexed by training step. Metrics and parameters logged inside an Azure ML command job are automatically associated with the correct experiment and run - no manual specification of experiment or run ID is required. If all three elements (run context, parameter logging, metric logging) are present in the script, all experiment tracking statements are True. If any element is absent, the corresponding statement is False and the tracking requirement is not met.
Microsoft Learn Reference Topic: Log metrics, parameters, and artifacts during Azure Machine Learning training runs with MLflow
NEW QUESTION # 146
Drag and Drop Question
A team deploys a machine learning model to production and monitors it continuously. Alerts are configured on performance and data quality metrics.
Multiple alerts are triggered during normal operation.
You need to perform the appropriate action for each model alert condition.
Which action should you perform for each alert condition? To answer, move the appropriate actions to the correct model alert conditions. You may use each action once, more than once, or not at all. You may need to move the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 147
Hotspot Question
A team is standardizing MLOps practices by using automated deployments.
The team requires infrastructure to be defined declaratively and deployed through automation pipelines.
You need to configure infrastructure deployment.
What should you configure for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Model Registry
Deploy resources from a pipeline.
To standardize MLOps practices with declarative infrastructure and automated pipelines, the best choice is to use a Model Registry via Infrastructure as Code (IaC) for your core setup, supplemented by Azure CLI inside your pipelines for operational tasks. Prompt Flow is an application development tool and should not be used to deploy infrastructure.
A Model Registry (like Azure Machine Learning registry or MLflow) is the standard architectural pattern for managing ML artifacts declaratively.
Declarative Tracking: It stores model versions, lineages, and environments as code-like configurations.
Pipeline Integration: CI/CD pipelines can natively fetch from or push to a registry using configuration files.
Environment Isolation: It allows you to promote the exact same model artifact across Dev, Staging, and Production environments without rebuilding.
Box 2: Bicep templates
Define Azure resources declaratively.
Bicep templates are an excellent choice for defining Azure resources declaratively in an automated MLOps pipeline.
Native Azure Integration: Bicep supports all Azure Machine Learning (Azure ML) resources immediately upon release.
Declarative Syntax: You define the desired end-state of your infrastructure without writing complex deployment scripts.
No State Management: Azure manages the state automatically, unlike Terraform which requires a remote state file.
Tooling Support: Deep integration with Azure Pipelines and GitHub Actions allows easy deployment validation.
Reference:
https://azure.microsoft.com/en-us/blog/new-azure-capabilities-to-simplify-deployment-and-management/
NEW QUESTION # 148
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: C
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 # 149
Drag and Drop Question
A team is developing a generative AI assistant. The team is experimenting with two prompt variants to improve performance before rolling out changes to production.
The team observes the following prompt results:
- PromptA variant generates longer responses and may be more expensive
to operate.
- PromptB may produce lower-quality answers.
The team must control operating costs while still selecting the better-performing prompt.
You need to identify cost drivers and compare the output quality of the two prompt variances to make an informed decision.
Which actions should you perform? To answer, move the appropriate actions to the correct requirements. You may use each action once, more than once, or not at all. You may need to move the split bar between panes or scroll to view content.
Answer:
Explanation:
NEW QUESTION # 150
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