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| Section | Objectives |
|---|---|
| Topic 1: Implement machine learning model lifecycle and operations | - Deploy models to real-time and batch endpoints - Train, register, and version models using Azure Machine Learning - Retrain, update, and manage model versions in production - Monitor model performance, data drift, and operational health |
| Topic 2: Design and implement a GenAIOps infrastructure | - Set up Microsoft Foundry and Azure AI services for generative AI workloads - Implement RAG (Retrieval-Augmented Generation) pipelines and vector search - Configure prompt orchestration, prompt flows, and agent frameworks - Manage API keys, rate limits, and responsible AI guardrails |
| Topic 3: Design and implement an MLOps infrastructure | - Manage environments, data stores, and model registries - Configure source control, CI/CD pipelines, and automation for ML workflows - Set up Azure Machine Learning workspace and compute targets - Implement security, governance, and compliance for MLOps |
| Topic 4: Implement generative AI quality assurance and observability | - Implement logging, tracing, and telemetry for GenAI applications - Monitor latency, token usage, cost, and error rates - Evaluate generative AI outputs for quality, safety, and grounding - Conduct red teaming, adversarial testing, and content filtering |
| Topic 5: Optimize generative AI systems and model performance | - Fine-tune and distill models for specific use cases - Optimize inference performance, caching, and throughput - Tune prompts, system messages, and grounding strategies - Implement cost management and scaling strategies for GenAI workloads |
With the help of AI-300 study materials, you can conduct targeted review on the topics which to be tested before the exam, and then you no longer have to worry about the problems that you may encounter a question that you are not familiar with during the exam. With AI-300 study materials, you will not need to purchase any other review materials. We have hired professional IT staff to maintain AI-300 Study Materials and our team of experts also constantly updates and renew the question bank according to changes in the syllabus.
NEW QUESTION # 105
You manage an Azure Machine Learning workspace
You build an Azure Machine Learning pipeline for image classification by using custom components. You need to define the interface, metadata, and code to execute components from a Python function. Which function should you use?
Answer: A
NEW QUESTION # 106
A company ' s platform engineers manage the resource settings and governance of Microsoft Foundry.
Developers must be able to create and update project assets but must not be able to change resource-level configurations.
You need to enforce least privilege access for the engineers and developers.
Which two actions should you perform? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point. Choose two .
Answer: B,D
Explanation:
Microsoft ' s Azure AI Foundry documentation defines built-in roles scoped to the resource hierarchy. The Azure AI Administrator role grants permissions to manage the Azure AI Hub resource itself - including network settings, shared connections, quota, and governance configuration - which is appropriate for platform engineers who configure resource-level settings. The Azure AI Developer role grants permissions to create, update, and manage project-level assets such as deployments, prompt flows, fine-tuning jobs, and evaluations, without access to resource-level configuration. This precisely matches the developer requirement and enforces least privilege. Disabling Entra ID authentication (option B) violates security policy and removes the identity-based access control that makes RBAC possible. Sharing a single API key (option D) violates the least-privilege principle - all users would have identical, undifferentiated access with no audit trail.
Microsoft Learn Reference Topic: Manage access to Azure AI Foundry - Built-in roles: Azure AI Administrator and Azure AI Developer
NEW QUESTION # 107
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 work in Microsoft Foundry with a prompt flow.
You must manually evaluate prompts and compare results across prompt variants.
You need to capture the inputs, outputs, token usage, and latencies for each flow run for the evaluation.
Solution: Create prompt variants and compare their outputs in the Evaluation experience.
Does the solution meet the goal?
Answer: A
Explanation:
The Evaluation experience in Microsoft Foundry ' s prompt flow editor shows aggregate quality scores across prompt variants - which variant produces more coherent answers, which scores higher on groundedness.
However, it does not expose per-run raw telemetry: individual token counts per call, per-request latency in milliseconds, or the exact input-output pairs for each execution. The Evaluation experience is designed for comparative quality scoring, not for detailed operational telemetry. To capture inputs, outputs, token usage, and latencies at the granular run level, Tracing must be enabled in Microsoft Foundry. Tracing records each LLM call as a structured span with timing, token consumption, and the complete input-output payload - a fundamentally different view than evaluation scores that directly satisfies all four capture requirements.
Microsoft Learn Reference Topic: Trace and debug prompt flows in Microsoft Foundry - Tracing vs.
Evaluation
NEW QUESTION # 108
Hotspot Question
You create an Azure Machine Learning workspace.
You must use the Python SDK v2 to implement an experiment from a Jupyter notebook in the workspace. The experiment must log a table in the following format:
You need to complete the Python code to log the table.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: dump
Log a table, SDK v2 with MLflow
# Add a metric for each column prefixed by metric name. Similar to log_row row1 = {"table.col1": 5, "table.col2": 10}
# To be done for each row in the table
mlflow.log_metrics(row1)
# Using mlflow.log_artifact
import json
with open("table.json", 'w') as f:
json.dump(table, f)
mlflow.log_artifact("table.json")
Box 2: mlflow.log_artifact
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/reference-migrate-sdk-v1-mlflow-tracking
NEW QUESTION # 109
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.
You have an Azure Machine Learning workspace. You connect to a terminal session from the Notebooks page in Azure Machine Learning studio.
You plan to add a new Jupyter kernel that will be accessible from the same terminal session.
You need to perform the task that must be completed before you can add the new kernel.
Solution: Delete the Python 3.8 - AzureML kernel.
Does the solution meet the goal?
Answer: A
Explanation:
No. Deleting an existing Azure Machine Learning kernel is neither required nor appropriate before adding another Jupyter kernel. Azure Machine Learning compute instances are explicitly designed to support multiple kernels simultaneously . Microsoft states that notebooks automatically discover the Jupyter kernels installed on the connected compute instance, allowing users to switch between them from the kernel selector.
To add a kernel, the supported process is to create a new Conda environment, activate it, install pip and ipykernel, and then register that environment by using python -m ipykernel install --user. Existing kernels do not need to be removed.
More importantly, Microsoft warns against deleting Conda environments or Jupyter kernels that you did not create , because doing so can damage Jupyter or JupyterLab functionality on the Azure Machine Learning compute instance. This makes removal of a built-in AzureML kernel particularly inappropriate as a prerequisite for creating another kernel.
The correct approach is therefore to leave the existing kernel intact and create a separate environment/kernel for the new workload.
Study Guide Reference: Design and implement an MLOps infrastructure - Azure Machine Learning notebook environments, kernel management, compute instances, and dependency isolation.
NEW QUESTION # 110
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