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| Section | Weight | Objectives |
|---|---|---|
| Optimize generative AI systems and model performance | 15–20% | - Optimize model selection and configuration
|
| Design and implement a GenAIOps infrastructure | 20–25% | - Set up Microsoft Foundry environment
|
| Implement generative AI quality assurance and observability | 10–15% | - Evaluate and test generative AI applications
|
| Design and implement an MLOps infrastructure | 15–20% | - Implement infrastructure as code for Machine Learning
|
| Implement machine learning model lifecycle and operations | 25–30% | - Register, version, and package models
|
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NEW QUESTION # 19
Hotspot Question
You are using hyperparameter tuning in Azure Machine Learning Python SDK v2 to train a model.
You configure the hyperparameter tuning experiment by running the following code:
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:
Explanation:
Box 1: No
No - By defining sampling in this manner, every possible combination of the parameters will be tested.
No, every possible combination will not be tested.
Continuous Spaces: Normal and Uniform distributions define continuous search spaces, creating an infinite number of possible decimal values.
Sampling Limits: The sweep job will only try a finite number of combinations based on your configuration.
Termination Rules: The total number of trials evaluated is strictly determined by the max_total_trials parameter and the chosen sampling_algorithm (e.g., Random, Grid, or Bayesian) that you apply to the sweep.
Box 2: Yes
Yes - Random values of the learning_rate parameter will be selected from a normal distribution with a mean of 10 and a standard deviation of 3.
The Normal(mu, sigma) distribution sampler in the Azure Machine Learning Python SDK v2 selects random values from a normal distribution defined by a mean (mu) and a standard deviation (sigma). In the code, Normal(10, 3) explicitly sets the mean to 10 and the standard deviation to 3.
Box 3: No
No - The keep_probabilily parameter value will always be either 0.05 or 0.1.
Because it is defined using Uniform(0.05, 0.1), Azure Machine Learning treats it as a continuous hyperparameter. This means the sweep job will sample any real number (floating-point value) distributed uniformly between 0.05 and 0.1 inclusive (for example, 0.063, 0.087, or 0.091), rather than choosing strictly between the two boundaries.
Box 4: No
No - Random values for the number_of_hidden_layers parameter will be selected from a normal distribution with a mean of 3 and standard deviation of 5.
The values for number_of_hidden_layers will be selected uniformly from a discrete list of choices, not from a normal distribution.
The Choice function: The Choice(range(3,5)) expression creates a discrete set of options: [3, 4].
Reference:
https://learn.microsoft.com/en-us/Azure/machine-Learning/how-to-tune-hyperparameters
https://github.com/Azure/azureml-examples/blob/main/sdk/python/jobs/single-step/lightgbm/iris/lightgbm-iris-sweep.ipynb
NEW QUESTION # 20
You manage an Azure Machine Learning workspace named workspace1 by using the Python SDK v2.
The default datastore of workspace1 contains a folder named sample_data.
The folder structure contains the following content:
You write Python SDK v2 code to materialize the data from the files in the sample_data folder into a Pandas data frame.
You need to complete the Python SDK v2 code to use the MLTable folder as the materialization blueprint.
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:
The MLTable format in Azure Machine Learning Python SDK v2 is a structured abstraction over tabular data sources. It stores a YAML file alongside data files, describing how to read and transform the data. To materialize this into a Pandas DataFrame, you use the mltable library: first call mltable.load with the path to the MLTable folder to load the definition, then call to_pandas_dataframe() on the returned object. The path parameter should point to the folder containing the MLTable YAML file on the default datastore. MLTable abstracts the storage location and transformation steps, making the code storage-agnostic - the same code works whether data lives in Blob Storage, ADLS Gen2, or a local path. The SDK v2 approach with MLTable is the recommended pattern for governed, reusable data access in Azure Machine Learning.
Microsoft Learn Reference Topic: Create and use MLTable data assets in Azure Machine Learning Python SDK v2
NEW QUESTION # 21
Drag and Drop Question
You have an Azure Machine Learning workspace named WS1 and a GitHub account named account1 that hosts a private repository named repo1.
You need to clone repo1 to make it available directly from WS1. The configuration must maximize the performance of the repo1 clone.
Which four actions should you perform in sequence?
Answer:
Explanation:
Explanation:
Step 1: Create a compute instance
Git integration for Azure Machine Learning
Clone Git repositories into your workspace file system
Azure Machine Learning provides a shared file system for all users in the workspace. To clone a Git repository into this file share, we recommend that you create a compute instance (Step 1) & open a terminal (Step 2). Once the terminal is opened, you have access to a full Git client and can clone and work with Git via the Git CLI experience.
We recommend that you clone the repository into your user directory so that others will not make collisions directly on your working branch.
Step 2: Open a terminal window
Step 3: Generate a Secure Shell (SSH) key pair
Authenticate your Git Account with SSH
Generate a new SSH key
1. Open the terminal window in the Azure Machine Learning Notebook Tab.
2. Paste the text below, substituting in your email address.
ssh-keygen -t rsa -b 4096 -C your_email@example.com
This creates a new ssh key, using the provided email as a label.
3. When you're prompted to "Enter a file in which to save the key" press Enter. This accepts the default file location.
4. Verify that the default location is '/home/azureuser/.ssh' and press enter. Otherwise specify the location '/home/azureuser/.ssh'.
5. At the prompt, type a secure passphrase. We recommend you add a passphrase to your SSH key for added security Step 4: Add a public key to account1.
Add the public key to Git Account
1. In your terminal window, copy the contents of your public key file. If you renamed the key, replace id_rsa.pub with the public key file name.
cat ~/.ssh/id_rsa.pub
2. Select and copy the SSH key output to your clipboard.
3. Next, follow the steps to add the SSH key to your preferred account type:
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/concept-train-model-git-integration
NEW QUESTION # 22
You use Azure Machine Learning to train models across multiple experiments by using the same workspace.
You must record training runs in a centralized location to compare results from different jobs.
During training, performance values must be captured so they appear in the experiment run history.
You need to configure experiment tracking.
What should you configure for each requirement? To answer, select the appropriate options in the answer area
. NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Azure Machine Learning ' s experiment tracking is built around two complementary concepts. First, experiments are named containers that group related runs. By calling mlflow.set_experiment with an experiment name at the start of your training code, all subsequent runs are grouped under that experiment name in the AML workspace, creating the centralized record required. Second, metrics are scalar values such as accuracy, loss, or AUC that represent model performance. Calling mlflow.log_metric with a metric name and value during training persists these values to the run ' s record in the experiment history. These values appear on the Azure ML Studio run detail page and can be compared across runs using the experiment comparison view. Without set_experiment, runs fall into a default experiment. Without log_metric, the run history has no performance data to display or compare.
Microsoft Learn Reference Topic: Track machine learning experiments with MLflow in Azure Machine Learning
NEW QUESTION # 23
An organization maintains separate Azure Machine Learning workspaces for development and production.
Both environments must use the same validated assets without duplicating them.
Assets must be shared across workspaces while maintaining centralized governance and version control.
You need to enable reuse of assets across workspaces without copying them.
What should you do?
Answer: D
NEW QUESTION # 24
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