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| Section | Objectives |
|---|---|
| Topic 1: Plan and design AI solutions using Azure AI services | - Requirements gathering and solution architecture
|
| Topic 2: Implement secure and scalable AI systems | - Security and governance
|
| Topic 3: Operationalizing machine learning solutions | - Deployment and monitoring
|
| Topic 4: Design and implement generative AI solutions | - Large language model integration
|
>> Training AI-300 Solutions <<
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NEW QUESTION # 42
You have an Azure Machine Learning (ML) model deployed to an online endpoint.
You need to review container logs from the endpoint by using Azure Ml Python SDK v2. The logs must include the console log from the inference server with print/log statements from the models scoring script.
What should you do first?
Answer: A
NEW QUESTION # 43
You are designing a new machine learning solution to predict customer churn by using Azure Machine Learning. You have raw data in CSV format stored in Azure Data Lake.
You need to design the solution so that it can efficiently handle large-scale model training and iterative development.
Which two actions should you perform? Each correct answer presents part of the solution.
Choose two.
NOTE: Each correct selection is worth one point.
Answer: D,E
Explanation:
Here is the correct and optimized architectural design for the solution.
1. Data Engineering & Registration
Connect Data Lake: Create an Azure ML Datastore pointing to your Azure Data Lake Storage (ADLS) Gen2.
[D] -> Modern Data Asset: Register the CSV data as an MLtable (the modern replacement for Tabular Datasets).
Schema Enforcement: Define types (e.g., CustomerID as string, Churn as boolean) in the MLTable YAML file.
Lazy Loading: MLtable loads data lazily during training to prevent out-of-memory errors on massive datasets.
2. Compute Strategy
[C] -> Compute Cluster (Scale-Out): Use an Azure ML Compute Cluster (AmlCompute) for actual large-scale model training.
Auto-Scaling: Configure the cluster to scale from 0 to N nodes so you only pay for compute during active jobs.Spot VMs: Leverage Azure Spot Virtual Machines on the cluster to reduce training costs by up to 80%.
Incorrect:
[Not E] Compute Instance (Development): Use this only as your workstation for writing code, Jupyter notebooks, and light debugging.
Reference:
https://medium.com/henkel-data-and-analytics/how-to-use-azure-ml-studio-an-eye-opening-model-training-tutorial-for-beginners-from-henkels-data-5035ee10a6d2
NEW QUESTION # 44
Hotspot Question
You are monitoring a fine-tuned large language model deployed in Microsoft Foundry.
You evaluate the model before and after fine-tuning by using the same evaluation dataset.
You review the following evaluation results:
You need to determine whether the fine-tuned model shows improved performance without introducing regression. 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:
NEW QUESTION # 45
You use Azure Machine Learning to tram a model.
You must use Baylean sampling to Tune hyperparaters.
You need to select a learning_rate parameter distribution.
Which two distributions can you use? Each correct answer presents a complete solution.
NOTE Each correct selection is worth one point.
Answer: A,B
NEW QUESTION # 46
An organization operates a generative AI application in production by using Microsoft Foundry. The application serves live user traffic and is updated by a data scientist team regularly as prompts and models evolve.
The application intermittently times out during production use, which requires ongoing visibility into runtime behavior.
The team must also validate model quality and safety before releasing new updates to avoid introducing regressions.
You need to apply the correct mechanisms for continuous runtime monitoring and for release time validation.
Which mechanisms should you use for each requirement? To answer, move the appropriate mechanisms to the correct requirements. You may use each mechanism 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:
The two requirements target fundamentally different stages of the AI application lifecycle. Continuous runtime monitoring addresses production behavior - an application that intermittently times out needs real- time visibility into latency, error rates, and request volumes for every call in production. Tracing backed by Application Insights or OpenTelemetry exporters is correct here because it captures granular, per-request telemetry continuously without requiring manual triggering - it is always-on by design. Release-time validation addresses pre-release quality gates - before new prompts or model updates go live, you need to verify they have not introduced regressions. An evaluation pipeline in Microsoft Foundry ' s prompt flow is correct here: it runs the updated application against a predefined test dataset, scores outputs on quality and safety metrics, and passes or fails the release based on thresholds. Evaluation pipelines run on demand triggered by CI/CD, not continuously.
Microsoft Learn Reference Topic: Microsoft Foundry observability - Tracing for runtime monitoring vs.
Evaluation pipelines for release gates
NEW QUESTION # 47
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