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| Section | Objectives |
|---|---|
| Design and implement generative AI solutions | - RAG (Retrieval Augmented Generation) solutions
|
| Operationalizing machine learning solutions | - ML lifecycle management
|
| Implement secure and scalable AI systems | - Scalability and performance optimization
|
| Plan and design AI solutions using Azure AI services | - Requirements gathering and solution architecture
|
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NEW QUESTION # 67
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: B,D
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 # 68
A team is working in Microsoft Foundry to test and compare large language model (LLM) prompt variants in a development environment.
The team requires consistent inputs to evaluate prompt variants without relying on live user traffic.
You need to create a controlled evaluation of input data.
Which action should you perform first?
Answer: A
NEW QUESTION # 69
You have an Azure Machine Learning workspace. You are running an experiment on your local computer.
You need to ensure that you can use MLflow Tracking with Azure Machine Learning Python SDK v2 to store metrics and artifacts from your local experiment runs in the workspace.
In which order should you perform the actions? To answer, move all actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation:
NEW QUESTION # 70
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 in the review screen.
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: Create an environment.
Does the solution meet the goal?
Answer: A
Explanation:
Correct:
* Create an environment.
Incorrect:
* Delete the Python 3.6 - AzureML kernel.
* Delete the Python 3.8 - AzureML kernel.
Note:
Before you can add a new Jupyter kernel on an Azure Machine Learning compute instance terminal, you must create a Conda environment.
Required Workflow
To officially provision and expose the new kernel to your Azure Machine Learning studio Notebooks, you need to execute the following full process from your terminal session:
Create the environment: Provision a new isolated environment (e.g., using conda create -n newenv python=3.10).
Activate the environment: Run conda activate newenv.
Install dependencies: Add the required ipykernel package using conda install ipykernel or pip install ipykernel.
Register the kernel: Bind the new environment configuration to the global Jupyter directory by running:
python -m ipykernel install --user --name newenv --display-name "My New Kernel" Reference:
https://docs.azure.cn/en-us/machine-learning/how-to-access-terminal
NEW QUESTION # 71
An organization validates generative AI applications during CI/CD Microsoft Foundry.
Evaluation must run automatically and block releases when quality thresholds are NOT met. Manual evaluation is no longer acceptable.
Evaluation must use both predefined quality metrics and custom safety checks.
You need to implement an automated evaluation workflow that supports both built-in and custom metrics .
What should you do?
Answer: D
Explanation:
Define the evaluation dataset with input prompts and expected outputs covering both quality and safety scenarios. Configure built-in evaluators such as Coherence, Fluency, Groundedness, and Content Safety alongside custom Python evaluator functions for domain-specific safety checks. Create a GitHub Actions workflow that triggers on pull requests or merges to the main branch, calling the Azure AI evaluation SDK to run the evaluation job automatically. Add a quality gate by parsing the evaluation results and using a conditional step to fail the workflow when thresholds are not met. Configure GitHub branch protection rules to require the evaluation workflow to pass, preventing merging or deploying code that fails the quality gate.
Enabling application tracing (option A) and monitoring latency (option C) are runtime monitoring tools, not release gates. Reviewing results manually after deployment (option B) directly contradicts the requirement to eliminate manual evaluation.
Microsoft Learn Reference Topic: Automate AI evaluation in CI/CD with GitHub Actions - Microsoft Foundry evaluation integration
NEW QUESTION # 72
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