Databricks-ML-Assoc ML Workflows Practice Test 2 — 15 Questions

Question 1

An ML engineer is using MLflow Tracking to log experiments for a model. They want to ensure that the experiment can be reproduced later and that the best model can be registered to the Databricks Model Registry. Which TWO actions should they take? (Choose two.)

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Log the model using mlflow.log_model with a signature that defines input and output schema, and register it to the Model Registry using the registered_model_name parameter.. To reproduce an experiment, you need to log the code version, hyperparameters, metrics, and the model artifact with its signature. Registering the model to the Model Registry requires logging it with a registered model name. Logging the Git commit hash and parameters ensures you can recreate the exact run, while the signature enables proper model serving and validation.

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Store the training dataset in DBFS and log its path as a tag, but do not log the model itself to avoid large artifacts.

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Use mlflow.autolog() to automatically log all metrics and parameters, and rely on it to register the model automatically.

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Create a separate MLflow experiment for each run to isolate metrics and avoid confusion when comparing runs.

Question 2

A machine learning team is using Databricks Feature Store to build a training dataset for a fraud detection model. They want to ensure that the same feature computation logic is used during both training and online inference. Which TWO practices should they follow? (Choose two.)

Accepted Answer

Log the model with mlflow.pyfunc.log_model and include the feature_lookups parameter to automatically retrieve features at inference time.. Registering the feature table with proper keys and logging the model with feature_lookups are the two practices that ensure consistent feature computation between training and inference. The first centralizes feature definitions; the second embeds retrieval logic into the model so that online serving uses the same features and keys.

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Configure the online store to automatically generate features on the fly from raw data using SQL expressions defined in the model.

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Use a separate feature engineering notebook for online inference that reimplements the same transformations independently.

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Manually copy the feature values into a CSV file and include it as an artifact with the model.

Question 3

An ML engineer is using MLflow Tracking to log experiments for a model. They want to ensure that the experiment can be reproduced later and that the best model can be registered to the Databricks Model Registry. Which TWO actions should they take? (Choose two.)

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Set the MLFLOW_TRACKING_URI environment variable to databricks.. For MLflow on Databricks to log runs to the managed tracking server, the tracking URI must be set to 'databricks'. This can be done via the MLFLOW_TRACKING_URI environment variable or programmatically with mlflow.set_tracking_uri. Without it, MLflow defaults to a local store, and runs won't appear in the workspace experiment UI. The other options address different aspects but not the core connectivity.

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Enable autologging by calling mlflow.autolog() before starting the run.

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Install the mlflow-skinny package instead of the full mlflow library.

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Create an MLflow experiment in the workspace and set it as the active experiment using mlflow.set_experiment().

Question 4

An ML engineer is deploying a model to production using Databricks Model Serving. The model was trained on a feature table in Databricks Feature Store. The engineer wants to ensure that the same feature transformations are applied during online inference as were used during training. Which approach correctly ensures consistency between training and inference?

Accepted Answer

Use the Feature Store's training_set() method to create a training set and then log the model with the feature table metadata using mlflow.log_model().. When a model is trained using Feature Store's training_set(), the resulting training set includes feature metadata. Logging the model with mlflow.log_model() stores this metadata, enabling Model Serving to automatically retrieve features from the online store and apply the same transformations. This guarantees consistency between training and inference.

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Export the feature table to a CSV file and include it in the model artifact for the serving endpoint to read.

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Log the feature table with the model using mlflow.log_artifact() and manually apply transformations in the serving endpoint.

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Create a separate serving endpoint for each feature and use API calls to combine them at inference time.

Question 5

An ML engineer is evaluating a model with a custom Python function that logs a single numeric metric and a text summary. They want to compare this evaluation across multiple runs in the MLflow experiment UI. Which MLflow tracking function should they use to record both values in one call?

Accepted Answer

mlflow.log_metrics({"f1_score": 0.87}); mlflow.set_tag("summary", "good"). To record a numeric metric and a text summary for cross-run comparison, use mlflow.log_metrics for the number and mlflow.set_tag for the string. mlflow.log_metrics ensures the value appears in the metrics column for sorting and charting, while mlflow.set_tag stores the summary as searchable metadata. Other functions either reject strings or store data as artifacts, which are not directly comparable in the experiment UI.

Answer

mlflow.log_dict({"f1_score": 0.87, "summary": "good"}, "eval.json")

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mlflow.log_metrics({"f1_score": 0.87})

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mlflow.log_params({"f1_score": 0.87, "summary": "good"})

Question 6

A team is using MLflow Projects to package their machine learning code. They want to run the project on a Databricks cluster with a specific environment and parameters. Which method should they use to execute the project?

Accepted Answer

Use the MLflow CLI command mlflow run with the --backend databricks option and specify the project URI and parameters.. MLflow Projects are designed to be run with the mlflow run command, and specifying --backend databricks executes the project on a Databricks cluster. This leverages the MLproject file to set up the environment and pass parameters, ensuring consistent and reproducible execution. Other methods do not directly interpret the MLproject file.

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Use the Databricks Repos feature to import the project and run it as a Python script.

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Use the Databricks CLI command databricks runs submit with the MLflow project URI and parameters.

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Create a Databricks Job that points to the MLproject file and runs it as a notebook.

Question 7

A data scientist has trained a scikit-learn model and wants to register it in the Databricks Model Registry with a signature that defines the expected input and output schema. Which MLflow method should they call to create a model signature from a sample input and the model's predictions?

Accepted Answer

mlflow.models.signature.infer_signature(X_sample, model.predict(X_sample)). The correct method is mlflow.models.signature.infer_signature, which takes sample input and output to automatically generate a ModelSignature. This signature is then passed to the model logging function to enforce schema validation. The other options either do not exist, require manual schema objects, or represent a full logging call rather than the signature creation method itself.

Answer

mlflow.models.signature.create_signature(X_sample, model.predict(X_sample))

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mlflow.models.ModelSignature.from_schema(X_sample, model.predict(X_sample))

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mlflow.sklearn.log_model(model, "model", signature=mlflow.models.signature.infer_signature(X_sample, model.predict(X_sample)))

Question 8

A data scientist is using MLflow Model Registry in Databricks to manage model versions. They want to automate the promotion of models from Staging to Production based on evaluation metrics. Which TWO of the following steps are required to implement this automation? (Choose two.)

Accepted Answer

Set up a Databricks job that runs a script to evaluate metrics and call the Model Registry API.. Automating model promotion requires programmatically transitioning stages based on metrics. The MLflow Model Registry API enables stage transitions, and a Databricks job can schedule a script to evaluate metrics and invoke that API. Together, these steps allow conditional promotion without manual intervention. Other options like webhooks or experiment settings do not provide the necessary automation capabilities.

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Configure a webhook in the MLflow Model Registry to trigger stage transitions.

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Use the Databricks REST API to update the model version stage directly.

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Enable automatic stage transitions in the MLflow experiment settings.

Question 9

A junior data scientist is using Databricks Repos to manage code for an ML project. They want to collaborate with teammates and track changes to notebooks and Python files. Which Git operation should they use to create a personal copy of the repository where they can make changes without affecting the main branch?

Accepted Answer

Clone the repository into a new Databricks Repo and create a new branch.. To collaborate on an ML project using Databricks Repos, the data scientist should clone the repository into a Databricks Repo and create a new branch. This provides a personal working copy linked to Git, enabling isolated changes and later merging via pull requests. Other options like forking or exporting do not align with the integrated Git workflow in Databricks Repos.

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Fork the repository on the Git provider and then add it as a Databricks Repo.

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Export the notebooks as .dbc files and import them into a new workspace folder.

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Use the Databricks Repos UI to create a new branch directly from the main branch.

Question 10

A data scientist is using MLflow to track experiments for a model that predicts customer churn. They want to log the model's hyperparameters, evaluation metrics, and the trained model artifact for each run. Which TWO of the following MLflow operations should they perform to achieve this? (Choose two.)

Accepted Answer

Use mlflow.log_param to record each hyperparameter.. To track hyperparameters, evaluation metrics, and the model artifact in MLflow, the data scientist should log parameters with mlflow.log_param, metrics with mlflow.log_metric, and the model with mlflow.sklearn.log_model. These operations populate the run's metadata and artifacts, enabling comparison and reproducibility. The other options either log the model as a generic artifact without flavor information or attempt to register a model without logging it first.

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Use mlflow.log_artifact to save the trained model file.

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Use mlflow.register_model to automatically log the model artifact.

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Use mlflow.sklearn.log_model to log the trained scikit-learn model.

Question 11

An ML engineer is using MLflow Projects to package a training script. The project must run on a Databricks cluster with a specific Python library version that is not installed by default. Which file in the MLflow Project should the engineer modify to specify the required library dependency?

Accepted Answer

conda.yaml. In an MLflow Project, the conda.yaml file defines the conda environment, including Python and library dependencies. By adding the required library with its version to the dependencies list, the engineer ensures the correct environment is created when the project runs. The MLproject file references this environment file, but the actual dependencies are listed in conda.yaml.

Answer

python_env.yaml

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MLproject

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requirements.txt

Question 12

A data scientist is training a model using MLflow on Databricks. They need to ensure that the model artifacts and metrics are logged automatically without adding manual logging code to the training script. Which approach should they use?

Accepted Answer

Execute mlflow.autolog() at the start of the notebook cell before running the model training code.. MLflow provides autologging capabilities that automatically capture parameters, metrics, and models when using popular machine learning libraries like Scikit-learn, TensorFlow, or PyTorch. By calling mlflow.autolog() before the training code, the framework instruments the library calls to record telemetry automatically. This approach minimizes boilerplate code and ensures consistency across experiments, which is essential for auditability and model reproducibility in collaborative Databricks environments, as it prevents human error in manual logging.

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Call mlflow.set_tracking_uri() inside the training function to redirect logs to the workspace.

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Wrap the training logic within an mlflow.start_run() block without any additional configuration.

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Configure the Databricks cluster environment variable MLFLOW_TRACKING_ENABLED to true.

Question 13

Refer to the exhibit. A Databricks job failed to start, returning the error shown. The job depends on MLflow for tracking. What is the most likely cause of this failure?

Accepted Answer

The cluster library configuration is missing the required MLflow package.. The ClassNotFoundException indicates that the MLflow library is missing from the runtime environment of the job cluster. In Databricks, if a job is configured to use a custom or minimal environment, essential libraries must be explicitly installed. This error highlights the importance of dependency management in production ML workflows, ensuring that all necessary packages are present in the cluster environment to prevent execution failures during automated deployment or batch scoring jobs.

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The Databricks Runtime version is too recent and contains a breaking change.

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The MLflow tracking server is currently unreachable due to network security policies.

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The job cluster has insufficient memory to load the MLflow dependency.

Question 14

When designing an ML workflow, what is the primary benefit of using MLflow Projects over executing raw scripts?

Accepted Answer

They facilitate consistent execution by encapsulating environment and dependency definitions.. MLflow Projects provide a standard format for packaging reusable data science code. By using a YAML-based environment definition, they ensure that the code runs in the exact same environment across different machines. This is critical for ML workflows to guarantee reproducibility, as it eliminates the 'it works on my machine' problem by explicitly defining the dependencies and environment setup required for the project execution in a portable, platform-agnostic manner.

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They automatically scale the underlying compute cluster size based on the task.

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They allow for the automatic versioning and tracking of data snapshots.

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They replace the need for unit testing individual functions within the pipeline.

Question 15

Which THREE actions are best practice when deploying a machine learning model using Databricks Model Serving?

Accepted Answer

Ensure the model signature is defined to enable input validation.. Databricks Model Serving provides a managed endpoint for low-latency inference. Adopting best practices for deployment—such as using environment variables, ensuring proper logging, and testing in staging—is essential for maintaining reliable, scalable, and secure production services. These practices ensure that the serving infrastructure is decoupled from the training environment, adheres to security policies, and provides observability into model performance in real-world conditions, effectively bridging the gap between model development and operational deployment.

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Hardcode API credentials directly into the model inference script.

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Perform inference on the same cluster used for training to save costs.