Free DP-100 sample questions

Correct answers: Separate Azure Machine Learning workspaces for each team.; A central Azure Machine Learning registry to store and share environments and models.

Creating separate workspaces provides the strongest isolation boundary. Each team can manage its own compute, data, and experiments independently, satisfying the requirement that they cannot access each other's resources.

An Azure Machine Learning registry is designed for sharing assets like models, environments, and components across multiple workspaces. This allows a central MLOps team to manage and distribute curated assets to all the individual team workspaces.

Correct answer: A Bandit policy with a `slack_factor` of 0.2 and an `evaluation_interval` of 2.

The Bandit policy is an aggressive early termination policy that terminates runs whose primary metric falls outside a specified slack factor/amount compared to the best-performing run. This is highly effective for efficiently exploring a large search space with a limited budget by quickly discarding unpromising trials.

Correct answer: Jinja templating in the prompt text

In Prompt flow, you use Jinja2 templating syntax to reference the outputs of previous nodes. For example, in the ad copy node's prompt, you would write `{{generate_audience.output}}` to insert the output from the `generate_audience` node. This creates the explicit data dependency and chains the steps together.

Correct answer: Gather more data for the underrepresented demographic group and use techniques like oversampling to rebalance the dataset before retraining.

Performance disparities often arise from imbalanced data where the model has insufficient examples for minority groups. The most effective initial step is to address this root cause by collecting more representative data or using sampling techniques (like SMOTE or random oversampling) to create a more balanced training dataset. This allows the model to learn the patterns for the minority group more effectively.

Correct answer: MLmodel

The `MLmodel` file is the primary metadata file for an MLflow model. It contains essential information, including the model's flavor, creation time, and most importantly, the `signature`. The signature explicitly defines the input and output schemas (data types, names, and tensor shapes), which is used by deployment tools to validate requests and format data correctly for inference.

Correct answer: True

This is true. Azure ML compute clusters can be configured with a minimum number of nodes (typically zero). The cluster automatically scales up when a job is submitted and scales down to the minimum count when idle. You are only charged for the nodes when they are allocated and running, not for the cluster resource itself when it's scaled to zero nodes.

Correct answer: az ml online-endpoint update

The `az ml online-endpoint update` command is used to modify the properties of an existing endpoint, including the traffic allocation. To route all traffic to the green deployment, you would use this command with the `--traffic "green=100"` parameter. This updates the endpoint's routing rules in place, ensuring a smooth transition with no downtime.

Correct answer: my_path = 'azureml://datastores/blob_datastore/paths/data/customers.csv' df = pd.read_csv(my_path)

The `azureml://` URI scheme is the standard way to reference data assets and paths within datastores in Azure ML SDK v2. By creating a data asset with a path pointing to this URI and then using `pd.read_csv(my_path)`, the SDK handles the authentication and data access seamlessly, loading the CSV into a Pandas DataFrame.

Correct answer: Fine-tune the smaller `Model-B` first. If its performance on a validation set meets the project requirements, select it. Otherwise, proceed to fine-tune `Model-A`.

This approach balances cost and performance. Starting with the smaller, cheaper model is a cost-effective experiment. If `Model-B` can be fine-tuned to meet the required performance benchmarks, there is no need to incur the higher costs associated with the larger `Model-A`. This iterative approach, starting with the simplest/cheapest viable option, is a common best practice in machine learning.

Correct answer: Pass the path to the environment YAML file to the `environment` parameter of the `CommandJob` constructor.

In the Azure ML SDK v2, you can directly reference a local YAML file that defines an environment. When you construct the `CommandJob`, you set the `environment` parameter to the path of this file (e.g., `environment='./my-env.yml'`). Azure ML will then build or retrieve the specified environment and use it to run the job.