Online Access Free AI-300 Practice Test

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?

• A.Yes
• B.No

You are implementing hyperparameter tuning by using Bayesian sampling for an Azure ML Python SDK v2-based model training from a notebook. The notebook is in an Azure Machine Learning workspace. The notebook uses a training script that runs on a compute cluster with 20 nodes.
The code implements Bandit termination policy with slackjactor set to 0.2 and a sweep job with max_concurrent_trials set to 10.
You must increase effectiveness of the tuning process by improving sampling convergence.
You need to select which sampling convergence to use.
What should you select?

• A.Set the value of slack_factor of early_termination policy to 0.9.
• B.Set the value of max_concurrent_trials to 20.
• C.Set the value of slack_factor of early_termination policy to 0.1.
• D.Set the value of max_concurrent_trials to 4.

Drag and Drop Question
A team is deploying a new version of a customer scoring model to a production online endpoint in Azure Machine Learning.
The team must minimize risk by gradually introducing the new model version and ensuring that traffic can be reverted immediately if issues occur.
You need to roll out the new model according to the requirements.
Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

Correct Answer:

Explanation:
To safely deploy your new customer scoring model with minimal risk, you should use Azure Machine Learning managed online endpoints with a blue-green deployment strategy. This allows you to test the new model (green) alongside the old model (blue) and route traffic gradually.
Here is the step-by-step process to execute this rollout.
Step 1: Create a New Deployment for the updated model version.
1. Create a New Deployment under the Existing Endpoint
Do not overwrite your current production deployment. Instead, deploy the new model version as a separate deployment within the same managed online endpoint.
Keep the existing deployment (e.g., blue-deployment) running at 100% traffic.
Create the new deployment (e.g., green-deployment) with the new model version.
Set the initial traffic allocation for the new deployment to 0%.
2. Test the New Deployment in Isolation [Step not in this question]
Before exposing real customers to the new model, test its stability and performance in the production environment.
Send test scoring requests directly to the green-deployment.
Use the X-MS-Routing-Override: green-deployment HTTP header in your SDK or REST requests to bypass the traffic split and target the new model directly.
Verify that the data schema, latency, and prediction outputs match your expectations.
Step 2: Configure traffic split to route a small percentage to the new deployment.
3. Gradually Shift Traffic (Canary Testing)
Once internal testing passes, begin introducing real production traffic to the new model in phases.
3a. Update the endpoint's traffic configuration to route a small percentage (e.g., 10%) to green- deployment. [Step 2] Step 3: Monitor endpoint metrics and error rates.
3b) Monitor system metrics closely, looking for spikes in HTTP 4xx/5xx errors, timeouts, or unusual scoring distributions. [Step 3] Step 4: Increase traffic to the new deployment after validation
3c) If performance remains stable over a designated observation period, incrementally increase the traffic (e.g., to 30%, 50%, then 100%). [Step 4] Further future actions outside the context of the question:
4. Monitor App Insights and MetricsAzure ML integrates natively with Azure Monitor and Application Insights. Track these critical metrics during the shift:CPU/Memory Utilization: Ensure the new model does not exhaust container resources.
Model Latency: Compare the response times of the new deployment against the old one.
Tracking Token/Feature Drift: Ensure the live customer data matches the model's expected input feature distribution.
5. Instant Rollback Plan
If the new model causes errors, triggers high latency, or skews customer scores, you can revert traffic immediately.
Update the endpoint traffic settings to route 100% of traffic back to the original blue-deployment.
Set the green-deployment traffic to 0%.This configuration change takes effect almost instantly, isolating the faulty model without any downtime for your users.
Keep the green-deployment alive at 0% traffic while you pull the logs to investigate the root cause, then delete it once fixed.
Incorrect:
[Remove the previous deployment]
A step further in the future.
Reference:
https://chandras4bigdata.medium.com/mlops-interview-and-study-guide-489d13ff82fa

Drag and Drop Question
You complete the fine-tuning of a generative model in Microsoft Foundry. The fine-tuned model now appears as a new model variant in your development environment.
The deployment process must ensure that proper validation and control is maintained.
You need to promote the fine-tuned model from development to production.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

Correct Answer:

Drag and Drop Question
A team operates a generative AI-powered customer support assistant built on Microsoft Foundry.
The application serves users globally and supports both real-time chat interactions and batch summarization jobs.
The team must ensure that the application continues to meet defined service-level objectives (SLO) as usage increases.
The team requires visibility into runtime behavior to identify performance regressions that affect the user experience and system capacity.
You need to select the performance metrics that meet the requirements.
Which performance metric should you monitor for each requirement? To answer, move the appropriate performance metrics to the correct requirements. You may use each performance metric 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.

Correct Answer: