Medium AI-300 practice questions

You manage several registered models in an Azure Machine Learning workspace. A fraud-detection model has 12 versions, and versions 1 through 8 are obsolete and should no longer appear in default listings or be selectable for new deployments. However, compliance requires that these older versions remain retrievable for audit purposes and are not permanently deleted. What should you do?

Your team's security policy prohibits storing storage account access keys or SAS tokens anywhere in the Azure Machine Learning workspace. Data scientists must still be able to read training data from an existing Azure Data Lake Storage Gen2 container through a registered datastore. Users each have their own Microsoft Entra ID identity. What should you configure when registering the datastore so that data access is authorized using the user's own identity rather than stored credentials?

Your team provisions a new Azure Machine Learning workspace inside a resource group named 'rg-ml-prod' that also contains the associated storage account, key vault, and container registry. A group of ML engineers needs Contributor-level permissions on the workspace and every dependent resource, and you want to minimize the number of role assignments you have to manage as more supporting resources are added to the group over time. Which approach should you take?

Your team runs Azure Machine Learning training jobs that read from an Azure Blob Storage account. Security policy prohibits storing storage account keys or SAS tokens in the workspace. You register the storage as a datastore and need the compute cluster to authenticate to the blob container without embedded credentials. What should you do?

Your team stores large training datasets in an Azure Data Lake Storage Gen2 account that has hierarchical namespace enabled. You need to register this storage as a datastore in your Azure Machine Learning workspace using the Azure CLI (v2) so data scientists can reference paths in their jobs. Which datastore type should you specify in the YAML definition?

Your team has an existing Azure Blob Storage account that already contains curated training data in a container named 'training-data'. You need to make this data accessible from your Azure Machine Learning workspace so data scientists can reference it by name in jobs, without moving or copying the data. Credentials must be stored securely in the workspace and reused across jobs. What should you do?

You are training a classification model in Azure Machine Learning using a command job that logs a validation metric called 'val_auc' with MLflow. You want to automate hyperparameter tuning to maximize this metric across combinations of learning rate and batch size, sampling values randomly and stopping poorly performing trials early. Which approach should you use to configure the tuning job?

Your team maintains Azure Machine Learning infrastructure using Bicep templates stored in a GitHub repository. You need to configure a GitHub Actions workflow that deploys the workspace and its resources to Azure automatically on every merge to the main branch. Company security policy prohibits storing long-lived service principal secrets in GitHub. What should you configure to allow the workflow to authenticate to Azure?

Your team has an existing Python training script that trains a scikit-learn regression model and logs metrics with MLflow. You need to run this script on an Azure Machine Learning compute cluster as a reproducible job, passing a registered data asset and a learning-rate argument, while ensuring the run appears in a specific experiment for later comparison. Which Azure Machine Learning job type should you configure?

You are running a hyperparameter tuning sweep job in Azure Machine Learning to optimize a deep learning model. The sweep uses random sampling over a large search space, and each trial is expensive to run on GPU compute. You want to automatically stop poorly performing trials early to reduce cost, while still allowing promising trials to complete. Which configuration should you add to the sweep job?

You are an ML engineer at a logistics company. A data scientist has trained a model and logged it with MLflow, and you now need to deploy it to a managed online endpoint for real-time scoring. The endpoint must serve two model versions simultaneously so you can compare responses before committing. You want to minimize the amount of custom code required for the deployment. What should you do?

Your team has a registered MLflow model that must score a nightly file drop of roughly 5 million records stored in a Blob datastore. Latency is not important, but the job must finish within a maintenance window and produce a single consolidated output file. You need to create the most cost-effective managed inference solution in Azure Machine Learning. What should you do?

You deploy an MLflow model to an Azure Machine Learning batch endpoint to score a folder of 500,000 small CSV files. The initial scoring job runs very slowly, and monitoring shows the compute cluster nodes are underutilized with frequent overhead between individual file reads. You need to improve throughput without changing the scoring logic. Which batch deployment setting should you adjust?

You are deploying a scikit-learn model to an Azure Machine Learning batch endpoint using a custom scoring script. During execution, the endpoint must process thousands of CSV files, and each mini-batch should return one row of predictions per input row. Which function in your scoring script is responsible for processing each mini-batch of files and returning the results?

Your team trained a demand-forecasting model that must score a 40 GB CSV file dropped into Azure Blob Storage every night. The job has no latency requirement and results are written back to a data lake for downstream reporting. You want to minimize compute cost by only provisioning resources when the scoring job runs. Which Azure Machine Learning deployment approach best fits this workload?

Your team built a generative AI summarization assistant in Azure AI Foundry. Reviewers complain that some generated summaries are grammatically awkward and read poorly, while others jump between ideas without logical flow. You want to set up an automated evaluation run that specifically quantifies (1) how grammatically correct and natural the language is, and (2) how well sentences connect and follow a logical structure. Which two built-in AI quality metrics should you configure?

You are training a classification model in Azure Machine Learning using several experiment jobs, each logging accuracy, F1 score, and AUC through MLflow autologging. Your team lead asks you to identify which job produced the best-performing model and to visually compare the logged metrics side by side across all runs within the same experiment. Which approach should you use in the Azure Machine Learning studio?

You maintain an Azure Machine Learning training pipeline that trains a model and then registers it. The business requires that the model is only registered when its validation accuracy exceeds 0.85; otherwise the pipeline should skip registration and finish successfully without failing. You want to implement this within the pipeline itself using components. What is the most appropriate approach?

Your team runs intermittent training jobs on an Azure Machine Learning workspace. Costs are high because a dedicated compute cluster stays allocated even when no jobs are queued. You must minimize cost while ensuring capacity is available automatically when jobs are submitted. Which compute cluster configuration should you apply?

Your team runs training jobs in an Azure Machine Learning workspace using the Python SDK v2 from a compute instance. Compliance requires that every submitted job be traceable to the exact commit of the training code. The code lives in an Azure DevOps Git repository that is already cloned onto the compute instance. What is the simplest way to ensure the Git commit hash and repository details are automatically captured with each job?

Your data science team needs a reproducible training environment in Azure Machine Learning that includes scikit-learn plus two internal Python packages hosted on a private Azure Artifacts feed. The environment must be versioned as a reusable asset so that all training jobs reference the exact same dependency set. Which approach should you use to create and register this environment?

Your team runs an Azure AI Foundry customer-support agent that must always include a legal disclaimer phrase in responses that mention refund policies. The built-in AI-assisted quality metrics (groundedness, relevance, coherence, fluency) do not capture this business rule. You need the automated batch evaluation workflow to flag every response missing the required phrase, in addition to the built-in metrics. What should you do?

Your team runs automated evaluation on a customer-support agent in Azure AI Foundry. The built-in groundedness, relevance, and coherence evaluators are already configured, but the business requires a domain-specific check: each response must include a valid support ticket ID in a fixed format, and the evaluation must report the percentage of responses that pass this check across the test dataset. You need to add this measurement to the existing automated evaluation workflow with the least ongoing maintenance. What should you do?

Your team wants to standardize a data preprocessing step so it can be reused across multiple Azure Machine Learning pipelines built by different data scientists. You need to define this step as a versioned, shareable asset in the workspace using the Azure ML CLI (v2). Which asset type should you create and register for the preprocessing step?

You operate a fraud-detection model on an Azure Machine Learning managed online endpoint. Confirmed fraud labels arrive weeks after scoring, so ground-truth accuracy cannot be evaluated in near real time. Leadership wants an early warning when the model's behavior shifts, without waiting for labels. Which monitoring signal should you configure in the Azure ML model monitor?