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| Section | Objectives |
|---|---|
| Implement secure and scalable AI systems | - Security and governance
|
| Operationalizing machine learning solutions | - Deployment and monitoring
|
| Design and implement generative AI solutions | - RAG (Retrieval Augmented Generation) solutions
|
| Plan and design AI solutions using Azure AI services | - Requirements gathering and solution architecture
|
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13. Frage
A team accidentally deploys an outdated model version due to incorrect tagging. You need to enforce strict deployment governance and version control. What should you implement?
Antwort: D
Begründung:
Version pinning ensures that only specific, approved model versions are deployed, while approval workflows enforce governance. This prevents accidental deployment of outdated or unverified models. Simple naming conventions or tagging are error-prone and do not provide sufficient control.
14. Frage
Hotspot Question
You manage an Azure Machine Learning workspace named workspace1.
You must register an Azure Blob storage datastore in workspace1 by using an access key. You develop Python SDK v2 code to import all modules required to register the datastore.
You need to complete the Python SDK v2 code to define the datastore.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Antwort:
Begründung:
Explanation:
How should you complete the code?
Box 1: container_name
container_name specifies the parameter name used in the AzureBlobDatastore constructor to identify your target blob storage container.
Box 2: wasbs
Correct Code Formats
Depending on your preference for the storage connection protocol, your completed line of code should look like one of the following variations:
Using the standard HTTPS protocol (Default).
-> Using the WASBS (Windows Azure Storage Blob Secure).
Reference:
https://stackoverflow.com/questions/75275875/create-an-azureblobdatastore-with-sdk-v2
15. Frage
Drag and Drop Question
A customer-facing web application uses a foundational model deployed through Microsoft Foundry.
A new model version must be introduced and validated without disrupting production traffic.
You need to deploy the new version by using a safe promotion strategy.
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.
Antwort:
Begründung:
16. Frage
Hotspot Question
You have an Azure Machine Learning workspace.
You plan to use Azure Machine Learning Python SDK v2 to register a component in the workspace. The component definition is stored in the local file ./components/train/train.yml.
You write code to connect to the workspace by using the ml_client object and import all required libraries.
You need to complete the remaining code.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Antwort:
Begründung:
Explanation:
Box 1: load_component
train_component
Example:
Create component: training (using yaml definition)
Create and register the component. Registering it allows you to re-use it in other pipelines. Also, anyone else with access to your workspace can use the registered component.
# importing the Component Package
from azure.ai.ml import load_component
# Loading the component from the yml file
train_component = load_component(source=os.path.join(train_src_dir, "train.yml"))
# Now we register the component to the workspace
train_component = ml_client.create_or_update(train_component)
# Create (register) the component in your workspace
print(
f"Component {train_component.name} with Version {train_component.version} is registered" ) Box 2: create_or_update ml_client Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/tutorial-pipeline-python-sdk
17. Frage
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?
Antwort: A
Begründung:
Correct:
* python script.py --training_data ${{inputs.training_data}}
The scipt is named script.py.
For the parameter use ${{inputs.training_data}}
Incorrect:
* python script.py --training_data dataset1.csv
* python script.py dataset1.csv
* python train.py --training_data training_data
Note: Read a TabularDataset, Example
In the Input object, specify the type as AssetTypes.MLTABLE, and mode as InputOutputModes.DIRECT:
* Details omitted*
job = command(
code="./src", # Local path where the code is stored
*-> command="python train.py --inputs ${{inputs.input_data}}",
inputs=my_job_inputs,
environment="<environment_name>:<version>",
compute="cpu-cluster",
)
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-read-write-data-v2
18. Frage
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