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| Section | Objectives |
|---|---|
| Topic 1: Design and implement generative AI solutions | - RAG (Retrieval Augmented Generation) solutions
|
| Topic 2: Operationalizing machine learning solutions | - Deployment and monitoring
|
| Topic 3: Implement secure and scalable AI systems | - Security and governance
|
| Topic 4: Plan and design AI solutions using Azure AI services | - Responsible AI design
|
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NEW QUESTION # 172
You have an Azure Machine Learning workspace named Workspace 1 Workspace! has a registered Mlflow model named model 1 with PyFunc flavor You plan to deploy model1 to an online endpoint named endpointl without egress connectivity by using Azure Machine learning Python SDK vl You have the following code:
You need to add a parameter to the ManagedOnllneDeployment object to ensure the model deploys successfully Solution: Add the with_package parameter.
Does the solution meet the goal?
Answer: B
NEW QUESTION # 173
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 # 174
Hotspot Question
A team retrains a machine learning model on a weekly basis by using updated training data.
The team must be able to rerun any previous experiment by using the exact data that was available at the time it was originally run. The solution must preserve historical versions of the data without duplicating training scripts.
You need to manage the data so that experiments can be reproduced reliably.
Which workspace actions should you perform? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Create a versioned dataset
Track data changes over time.
Creating a versioned dataset in your machine learning workspace configuration is the correct and industry-standard way to solve this.
Versioned datasets allow you to decouple your training scripts from your data state. This approach satisfies all your constraints without duplicating code or creating messy infrastructure.
Enables Reproducibility: Every time you run a training job, you log the specific dataset version used (e.g., v1, v2, v3). To rerun a historical experiment, you simply call that specific version identifier.
Prevents Code Duplication: Your training scripts remain identical. They accept a dataset version as an input parameter rather than pointing to hardcoded file paths.
Tracks Weekly Updates: When the weekly data refresh occurs, you register the new data as v+1 in the workspace registry, keeping a clean chronological ledger.
Box 2: Use the data asset in the experiment
Associate a specific dataset version with a run.
To best associate a specific dataset version with a run in the workspace, you should use the data asset in the experiment.
Automatic Lineage: Registering data as a versioned data asset automatically tracks which exact version was used in which specific run.
Exact Reproducibility: You can look back at any historical run and instantly identify or retrieve the precise data state used at that moment.
No Script Duplication: Your training scripts remain decoupled from the data, satisfying your requirement to preserve history without changing code.
Incorrect:
[Pass the storage key at runtime]
Passing a storage key at runtime only connects the run to a storage location, not a specific, immutable version of the data at a frozen point in time.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-version-track-datasets
NEW QUESTION # 175
You need to isolate training workloads while remaining cost-aware to address Fabrikam Inc.'s issues, constraints, and technical requirements.
What should you implement?
Answer: C
Explanation:
Imagine a hospital analytics firm with data scientists who kick off dozens of training jobs throughout the week. During peak hours, five jobs compete for the same GPU cluster and fail or queue for hours. On quiet nights, that cluster sits completely idle, burning money. Managed compute targets with autoscaling solve both problems: the cluster scales out automatically when multiple jobs arrive simultaneously and scales back to zero when idle. Option A (single shared cluster) is exactly the resource-contention problem Fabrikam already has. Option B (fixed-size cluster) wastes money during off-peak hours. Option C (dedicated per-experiment clusters) eliminates contention but is prohibitively expensive for a cost-conscious healthcare firm.
Autoscaling managed compute is the cloud-native answer to variable workload demand.
Microsoft Learn Reference Topic: Azure Machine Learning compute targets - Configure autoscale for compute clusters
NEW QUESTION # 176
A data science team trains a model that depends on features that are stored in a managed feature store.
The model is registered in Azure Machine Learning and will be deployed to a real-time endpoint.
After deployment, the model must:
* Retrieve feature values dynamically at inference time.
* Use the same feature definitions that were used during training.
* Run without manual configuration changes across environments.
You need to define feature store entities so that feature retrieval behaves as expected when the model is deployed.
Which feature store entity should you select for each requirement? To answer, move the appropriate feature store entities to the correct requirements. You may use each feature store entity 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:
Explanation:
Define how features are retrieved at inference: Feature retrieval specification Ensure feature consistency between training and inference: Feature retrieval specification Enable automated feature lookup in production: Feature retrieval specification The feature retrieval specification is the key model-level contract for all three requirements. Microsoft defines it as a portable specification containing the exact list of features associated with a model, including the relevant feature store, feature set, and feature-set version . The same specification participates in both training and inference, making it the connective artifact across the model lifecycle.
During training, the feature retrieval specification is used to obtain the required feature values and generate training data. Microsoft requires the specification to be packaged with the model artifact when the model depends on feature-store features. At online inference time, the scoring script loads this packaged specification, resolves the feature list, and initializes online feature lookup before calling the feature store to retrieve current values.
This also prevents training-serving inconsistencies because the deployed model carries the feature dependencies and specific feature-set versions established during training, rather than relying on manually reconstructed production configuration. Microsoft explicitly states that packaging the retrieval specification with the model minimizes changes between training and inference workflows.
A feature set specification defines feature sources and transformations, a feature set asset provides managed versioning, and materialization precomputes feature values. None of those alone defines the model ' s complete retrieval contract at inference.
Study Guide Reference: Implement machine learning model lifecycle and operations - package a feature retrieval specification with the model artifact and operationalize feature-store-backed models.
NEW QUESTION # 177
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