Free Operationalizing Machine Learning and Generative AI Solutions Practice Questions

Termination policy type: Bandit

Termination policy parameter: slack_factor

The correct early termination policy is Bandit because the requirement explicitly compares each training run against the best-performing run . Microsoft defines BanditPolicy as an early termination mechanism that stops runs that fall outside a permitted performance margin relative to the best run observed so far.

The correct parameter is slack_factor because the threshold is expressed as a percentage , not as an absolute difference in the metric. Microsoft defines slack_factor as the ratio representing the allowable distance from the best-performing run. Therefore, a 25% tolerance would be configured conceptually as:

slack_factor = 0.25

A trial whose primary metric falls outside that permitted 25% band is eligible for early termination at the configured evaluation interval. Microsoft provides the same pattern in SDK v2 examples, such as BanditPolicy(slack_factor=0.15, ...) for a 15% relative tolerance.

Median stopping compares runs with the median performance across completed or running trials rather than strictly against the current best run. Truncation selection terminates a specified percentage of the worst-performing trials, so neither matches this requirement.

Correct sequence:

Create a data asset.

Create a Foundry Search resource.

Create a vector index.

Create a flow.

First, create a data asset so that the locally stored text files are available within the project as a managed data source. The content must then be made searchable for Retrieval-Augmented Generation.

Next, create a Foundry Search resource to provide the search infrastructure that will store and serve the indexed document content. Microsoft documents Azure AI Search as a supported vector store for Foundry and Azure Machine Learning RAG workloads.

Third, create a vector index from the source documents. During vector-index creation, the content is processed into chunks and embeddings, which enables semantic similarity retrieval. Microsoft documents that vector indexes can be created from local files, folders, or registered data assets and then consumed by an Index Lookup operation.

Finally, create a flow and configure it to query the vector index and supply the retrieved context to the connected Azure OpenAI model. Microsoft specifically documents adding an existing vector index to a prompt flow through the Index Lookup tool.

Create a connection is the unused action in this question because the scenario already specifies a connected Azure OpenAI Service model.

Study Guide Reference: Design and implement a GenAIOps infrastructure — RAG architecture, project data, vector indexing, Azure AI Search, prompt flow, and grounding with enterprise content.

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

Correct sequence:

From the terminal window in the Azure Machine Learning interface, run the ssh-keygen command.

From the terminal window in the Azure Machine Learning interface, run the cat ~/.ssh/id_rsa.pub command.

Add a public key to the GitHub account.

From the terminal window in the Azure Machine Learning interface, run the git clone command.

Azure Machine Learning supports cloning Git repositories directly into the workspace file system from a compute instance terminal . For an SSH-based GitHub connection, the required workflow is to generate an SSH key pair, obtain the public-key value, associate that public key with the Git account, and then clone the repository using its SSH URL. Microsoft documents this exact logical sequence for Git integration with Azure Machine Learning.

First, ssh-keygen creates the private/public SSH key pair on the Azure Machine Learning compute instance. Next, the cat ~/.ssh/id_rsa.pub command displays the public-key contents so they can be copied. The public key is then added to the GitHub account, enabling GitHub to authenticate connections originating from the compute instance. The private key must remain on the compute instance and must never be uploaded to GitHub.

Finally, execute git clone with the repository ' s SSH clone URL. Azure Machine Learning documentation confirms that repositories can be cloned directly into its shared workspace file system and recommends performing Git operations from the compute-instance terminal.

Add a private key to the GitHub account is therefore the unused and incorrect action.

Study Guide Reference: Design and implement an MLOps infrastructure — source control integration, Azure Machine Learning workspace files, SSH authentication, Git repositories, and secure development workflows.