Azure Data Scientist Free Sample Questions

A recursive character text splitter is ideal for documents with varied structure. It attempts to split along semantic boundaries (paragraphs, sections) first before falling back to smaller units. This preserves the context within chunks, which is crucial for accurate retrieval from both short policies and long guides. A moderate chunk size with overlap ensures that sentences or ideas are not awkwardly split between chunks.

Azure Machine Learning components can be configured with retry settings directly in their YAML definition. By adding a retry_settings block with properties like count, delay, and backoff, you instruct the pipeline orchestrator to automatically re-run the component if it fails. This is the correct approach for handling transient errors without altering the component's source code.

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.

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.

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.

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.

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.

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.

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.

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.