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| Section | Objectives |
|---|---|
| Topic 1: Optimize generative AI systems and model performance | - Optimize inference performance, caching, and throughput - Tune prompts, system messages, and grounding strategies - Fine-tune and distill models for specific use cases - Implement cost management and scaling strategies for GenAI workloads |
| Topic 2: Design and implement a GenAIOps infrastructure | - Configure prompt orchestration, prompt flows, and agent frameworks - Set up Microsoft Foundry and Azure AI services for generative AI workloads - Implement RAG (Retrieval-Augmented Generation) pipelines and vector search - Manage API keys, rate limits, and responsible AI guardrails |
| Topic 3: 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 4: Design and implement an MLOps infrastructure | - Manage environments, data stores, and model registries - Implement security, governance, and compliance for MLOps - Set up Azure Machine Learning workspace and compute targets - Configure source control, CI/CD pipelines, and automation for ML workflows |
| Topic 5: Implement machine learning model lifecycle and operations | - Monitor model performance, data drift, and operational health - Retrain, update, and manage model versions in production - Train, register, and version models using Azure Machine Learning - Deploy models to real-time and batch endpoints |
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NEW QUESTION # 48
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 # 49
A data science team plans to evaluate multiple hyperparameter values automatically while training a model in Azure Machine Learning.
The tuning process must run multiple training trials without manually modifying the training script for each run.
You need to automate hyperparameter tuning for the training job.
What should you do?
Answer: A
Explanation:
Correct:
* Create a tuning job that runs multiple trials with different parameter values To best automate hyperparameter tuning in Azure Machine Learning, you should create a Sweep Job (in SDK v2) or a HyperDrive experiment (in SDK v1).
This creates a tuning job that automatically launches multiple training trials (child runs) using your single base training script without requiring manual code modifications for each run.
Key Components to Automate the Job
To set up this job successfully using the Azure Machine Learning Python SDK v2, you will define:
Parameterized Training Script: Write your code to accept hyperparameters as command-line arguments (e.g., using Python's argparse), allowing the tuning job to pass different values to each trial.
Search Space: Define the range or specific discrete/continuous choices for the values you want to test (e.g., learning rates, batch sizes).Sampling Algorithm: Choose how Azure ML should navigate your search space. Options include Random sampling, Grid sampling (testing every possible combination), or Bayesian sampling (using previous trial results to pick the next best values).Primary Metric: Specify the performance metric your script logs (like accuracy or loss) so Azure ML knows which target to optimize.
Early Termination Policy: Optional policy (like a Bandit Policy) to automatically cancel poorly performing trials early, saving you compute time and cost.
Incorrect:
* Adjust hyperparameters after model deployment.
* Duplicate the training script for each parameter combination.
* Manually change hyperparameter values between training runs.
* Run a single training job with fixed hyperparameters.
* Select hyperparameters based only on default model settings.
Reference:
https://learn.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.hyperdrive
NEW QUESTION # 50
Hotspot Question
You create an Azure Machine Learning workspace.
You are developing a Python SDK v2 notebook to perform custom model training in the workspace. The notebook code imports all required packages.
You need to complete the Python SDK v2 code to include a training script, environment, and compute information.
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: MLClient
Box 2: command
Example, Submit a script run, SDK v2
#connect to the workspace
ml_client = MLClient.from_config(DefaultAzureCredential())
# set up pytorch environment
env = Environment(
image="mcr.microsoft.com/azureml/openmpi3.1.2-ubuntu18.04",
conda_file="pytorch-env.yml",
name="pytorch-env"
)
# define the command
command_job = command(
code="./src",
command="train.py",
environment=env,
compute="cpu-cluster",
)
returned_job = ml_client.jobs.create_or_update(command_job)
returned_job
Note: create_or_update
Creates or updates an Azure ML resource.
create_or_update(entity: T, **kwargs) -> T
Parameters
entity
Union[Job , Model, Environment, Component , Datastore]
Required
The resource to create or update.
Returns
The created or updated resource.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/migrate-to-v2-command-job
https://learn.microsoft.com/en-us/python/api/azure-ai-ml/azure.ai.ml.mlclien
NEW QUESTION # 51
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 --trainingdata ${{inputs.training_data}}
Does the solution meet the goal?
Answer: B
Explanation:
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
NEW QUESTION # 52
Hotspot Question
You manage an Azure Machine Learning workspace by using the Python SDK v2.
You must create an automated machine learning job to generate a classification model by using data files stored in Parquet format. You must configure an autoscaling compute target and a data asset for the job.
You need to configure the resources for the job.
Which resource configuration should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Azure Databricks
Autoscaling: Out of the provided choices, Azure Databricks is the only compute target that natively supports the automated scaling up and down of worker nodes required to efficiently match the computation demands of your specific job.
Compatibility: Azure HDInsight and Azure Data Lake Analytics are legacy analytics platforms that do not offer the same direct, optimized, and auto-scaling compute integration within Azure ML SDK v2 pipelines for AutoML training.
Box 2: uri_folder
Directory access: Since your data consists of multiple data files stored in the Parquet format, pointing your asset type to a uri_folder allows the training job to automatically read and ingest all individual Parquet partition files stored inside that directory.
Reference:
https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/azure-databricks/automl/automl-databricks-local-01.ipynb
NEW QUESTION # 53
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