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| Section | Weight | Objectives |
|---|---|---|
| Design and implement a GenAIOps infrastructure | 20–25% | - Set up Microsoft Foundry environment
|
| Optimize generative AI systems and model performance | 15–20% | - Optimize model selection and configuration
|
| Implement machine learning model lifecycle and operations | 25–30% | - Deploy models to production
|
| Design and implement an MLOps infrastructure | 15–20% | - Create and manage Machine Learning workspace resources and assets
|
| Implement generative AI quality assurance and observability | 10–15% | - Evaluate and test generative AI applications
|
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NEW QUESTION # 17
A team is developing a generative AI assistant. The team is experimenting with multiple prompt variants to improve the user experience.
When comparing prompt variants, the team plans to assess whether the generated responses are grammatically correct.
You need to evaluate the quality of the language from the generated responses.
Which evaluator should you use?
Answer: C
NEW QUESTION # 18
Drag and Drop Question
A team performs interactive experimentation during development. The team also runs scalable jobs for model training.
The team must minimize costs while ensuring compute resources scale when needed. Different workloads require different compute behaviors within the same workspace.
You need to configure compute targets that support each workload.
Which compute targets should you use? To answer, move the appropriate compute targets to the correct workload types. You may use each compute target 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 # 19
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 need to reset the state of the notebook.
Which three actions should you use? Each correct answer presents a complete solution.
Answer: C,D,E
Explanation:
Azure Machine Learning Studio explicitly distinguishes actions that merely interrupt execution from actions that reset notebook state and clear variables . Microsoft documents three relevant operations that reset notebook state: changing the kernel, switching compute, and resetting compute .
C). Reset the compute restarts the notebook execution environment. When another cell is subsequently executed, the compute environment starts again and the previous in-memory notebook state is no longer retained.
D). Change the current kernel causes the notebook to use a different kernel. Because variables, imported objects, and other runtime state belong to the active kernel process, changing the kernel resets that execution state.
E). Change the compute switches the notebook to another compute resource. Microsoft states that switching compute automatically connects the notebook to the selected compute and resets the notebook state and variables.
By contrast, B. Stop the current kernel only stops a currently running cell; executing another cell automatically restarts the kernel while the notebook-state behavior documented for this action does not constitute a reset. Likewise, A. navigating to another workspace section stops running cells but does not reset variables or notebook state.
Study Guide Reference: Implement machine learning model lifecycle and operations - Azure Machine Learning notebooks, compute instances, kernel management, execution environments, and interactive development.
NEW QUESTION # 20
Drag and Drop Question
A company is standardizing generative AI development across multiple teams.
Each team requires an isolated workspace. Governance and shared connections must be centrally managed.
You need to implement a Microsoft Foundry environment structure that supports centralized governance and team isolation.
Which type of configuration should you use for each requirement? To answer, move the appropriate configurations to the correct requirements. You may use each configuration 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 # 21
Hotspot Question
You manage an Azure Machine Learning workspace by using the Python SDK v2.
You must create an automated machine learning job to generate a classification model by using data files stored in Parquet format. You must configure an autoscaling compute target and a data asset for the job.
You need to configure the resources for the job.
Which resource configuration should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Azure Databricks
Autoscaling: Out of the provided choices, Azure Databricks is the only compute target that natively supports the automated scaling up and down of worker nodes required to efficiently match the computation demands of your specific job.
Compatibility: Azure HDInsight and Azure Data Lake Analytics are legacy analytics platforms that do not offer the same direct, optimized, and auto-scaling compute integration within Azure ML SDK v2 pipelines for AutoML training.
Box 2: uri_folder
Directory access: Since your data consists of multiple data files stored in the Parquet format, pointing your asset type to a uri_folder allows the training job to automatically read and ingest all individual Parquet partition files stored inside that directory.
Reference:
https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/azure-databricks/automl/automl-databricks-local-01.ipynb
NEW QUESTION # 22
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