Databricks-ML-Assoc Databricks Machine Learning Practice Test 2 — 15 Questions

Question 1

A machine learning engineer is using MLflow Tracking on Databricks to log experiments. They want to ensure that the experiments are reproducible and that all necessary information is captured. Which TWO of the following should they log to achieve this? (Choose two.)

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

Accepted Answer

Hyperparameters. To ensure reproducibility, logging hyperparameters and evaluation metrics is essential. Hyperparameters define the model configuration, and metrics quantify performance. Other items like commit hash or dataset are either automatically captured or impractical to log fully.

Answer

Source code version control commit hash

Answer

The model's learned weights

Answer

The entire training dataset

Question 2

A machine learning engineer is using Databricks AutoML to train a model on a dataset with a binary target. They want to ensure the model is optimized for recall. Which AutoML configuration should they use?

Accepted Answer

Set the primary metric to recall during AutoML experiment setup. To optimize for recall, you must set recall as the primary metric in Databricks AutoML. This directs the hyperparameter tuning and model selection to maximize recall. Other options either do not change the objective or are not available in AutoML's configuration, so they would not achieve the desired optimization.

Answer

Increase the number of trials in AutoML

Answer

Manually adjust class weights in the training code

Answer

Use the default primary metric (F1 score)

Question 3

A machine learning engineer is deploying a model to a Databricks model serving endpoint. They need to enable auto-scaling to handle variable traffic. Which configuration parameter should they set when creating the endpoint?

Accepted Answer

min_instances and max_instances. To enable auto-scaling for a model serving endpoint, the engineer must configure min_instances and max_instances. These parameters define the lower and upper bounds for the number of instances, allowing the endpoint to automatically scale based on traffic. Other parameters like scale_to_zero_enabled, instance_type, and workload_size do not provide dynamic scaling within a range. Therefore, setting min_instances and max_instances is the correct approach.

Answer

scale_to_zero_enabled

Answer

workload_size

Answer

instance_type

Question 4

A data scientist is building a Feature Store in Databricks. They need to ensure that features are available for both model training and online serving. They have created a feature table and want to publish it to an online store. Which TWO actions must they perform to make the features available for low-latency online inference? (Choose two.)

Accepted Answer

Publish the feature table to an online store using the Databricks Feature Store UI or API.. To serve features online with Databricks Feature Store, you must publish the feature table to an online store and enable Change Data Feed on the source Delta table. Publishing creates the online copy, and CDF allows Databricks to capture and propagate updates to keep the online store synchronized.

Answer

Configure a Databricks SQL warehouse to serve features via JDBC for online inference.

Answer

Register the feature table in the MLflow Model Registry to enable online serving.

Answer

Create a separate Delta table for online features and manually copy data using a scheduled job.

Question 5

A machine learning engineer is deploying an MLflow model to a Databricks model serving endpoint. The model was trained and logged with a custom Python function registered in the model's signature. When the endpoint is invoked, the request fails with an error indicating the custom function is not found. The engineer confirms the model artifact and its dependencies are correctly logged in the MLflow run. Which step should the engineer take to resolve this issue?

Accepted Answer

Package the custom function into a Python module, include it in the model's code directory when logging, and specify it in the model's requirements.. The error indicates the custom function is not found at serving time. MLflow models capture code dependencies only if they are included in the model's code directory or installed as packages. Packaging the function as a module and including it in the code directory ensures it is bundled with the model. Specifying it in requirements ensures installation. This resolves the missing function issue and allows the endpoint to invoke the model successfully.

Answer

Ensure the custom function is defined in the same notebook used for training and re-log the model with the function included as a code dependency.

Answer

Add the custom function to the model's conda environment YAML file and re-log the model.

Answer

Log the custom function as an artifact and update the model's signature to reference the artifact path.

Question 6

A data scientist is training a model using MLflow on Databricks and needs to ensure that all parameters and metrics are logged for every training run. Which approach ensures the most reliable logging of artifacts and metrics during model training?

Accepted Answer

Invoke mlflow.autolog() at the beginning of the notebook cell prior to training.. Using mlflow.autolog() is the recommended practice for capturing model metadata, hyperparameters, and metrics automatically in Databricks. This approach minimizes boilerplate code and ensures consistency across experiments, reducing human error. It is vital for reproducibility and model governance within the Databricks environment, as it captures the framework-specific details required for later model registration and deployment without requiring manual tracking of every individual metric.

Answer

Manually call mlflow.log_metric for every individual iteration inside the training loop.

Answer

Configure the MLflow tracking URI to point to an external database before initiating the training.

Answer

Use the model.save() method instead of MLflow tracking for better persistence.

Question 7

A machine learning engineer is troubleshooting a Model Registry issue where models are not being transitioned correctly. Which TWO actions should the engineer take to ensure proper governance and automated testing in the Registry?

Accepted Answer

Implement Webhooks to trigger external CI/CD validation pipelines upon status transitions.. Effective model governance relies on robust testing and clear transition protocols. Integrating CI/CD pipelines ensures that models undergo automated unit and integration tests before transitioning to Staging or Production. By utilizing Webhooks and automated transition triggers, teams can enforce validation checks, ensuring only high-quality models reach production. This systematic approach reduces risk and maintains a clear audit trail for compliance and operational reliability within the Databricks ML ecosystem.

Answer

Delete all older versions of the model to keep the registry clean and performant.

Answer

Manually update the model stage via the UI for every version to ensure maximum control.

Answer

Disable the model versioning feature to save storage space in the DBFS.

Question 8

Refer to the exhibit. A data scientist is attempting to deploy a model using the MLflow client. The error above occurs during the deployment script. What is the most likely cause of this failure?

Accepted Answer

The model has not been registered in the Model Registry with the specified name and version.. This error indicates that the specified model version does not exist in the Registry, either because it was never registered, was deleted, or the name/version number was referenced incorrectly. In a Databricks context, verify that the model has been successfully logged and registered using mlflow.register_model. This is critical for deployment pipelines as it validates that the artifact exists before the serving infrastructure attempts to load the model file.

Answer

The cluster does not have the necessary MLflow libraries installed.

Answer

The user lacks permissions to read the model version from the Registry.

Answer

The model version is still in the 'Archived' state and cannot be accessed.

Question 9

When preparing data for machine learning in Databricks, which feature of Delta Lake is most beneficial for managing large-scale datasets during the training process?

Accepted Answer

Time Travel (versioning) to access previous snapshots of training data.. Delta Lake's Time Travel (versioning) feature allows data scientists to query data as it existed at a specific point in time. This is essential for ensuring reproducibility in machine learning experiments, as it ensures that the training data remains consistent across different runs, even if the underlying table is updated or modified by other processes in the production pipeline.

Answer

Compaction (Auto Optimize) for improved query speed during data reading.

Answer

Schema enforcement to prevent corrupted data from entering the training set.

Answer

Z-Ordering to speed up filtering on specific columns in the dataset.

Question 10

Which Databricks feature should be used to provide a managed, secure, and scalable endpoint for real-time inference of models logged in the Model Registry?

Accepted Answer

Databricks Model Serving endpoints.. Databricks Model Serving provides a fully managed, low-latency API endpoint for model inference. It abstracts away the infrastructure requirements, enabling horizontal scaling and high availability. It is the standard service for deploying models in production, as it integrates directly with the Model Registry, allowing teams to push production-ready models to endpoints with minimal configuration and secure access controls.

Answer

Databricks Jobs running the model in a scheduled notebook.

Answer

A standard interactive cluster running a Flask server.

Answer

Delta Live Tables pipelines.

Question 11

A data scientist needs to perform hyperparameter tuning using Hyperopt. Which THREE components are essential to successfully implement an automated tuning run on a Databricks cluster?

Accepted Answer

A defined objective function that minimizes or maximizes a metric.. Successful hyperparameter tuning with Hyperopt requires a well-defined objective function, a search space, and an optimization algorithm. Integration with MLflow is also critical to track the performance of every trial. By combining these three elements, the scientist can efficiently navigate the search space to find optimal model parameters, while ensuring that the results are logged and manageable within the Databricks MLflow tracking workspace.

Answer

A pre-trained model checkpoint to initialize the search.

Answer

A dedicated GPU cluster for every single trial run.

Question 12

A team is building a Feature Store in Databricks. What is the primary advantage of using the Feature Store for training models compared to using raw Delta tables?

Accepted Answer

It prevents training-serving skew by using consistent feature engineering.. The Feature Store provides a centralized repository for features, ensuring that the same feature engineering logic is used for both training and real-time inference. This eliminates training-serving skew, a common problem where the logic used during training differs from the logic used in production. This consistency is essential for model accuracy and reliability in dynamic production environments.

Answer

It automatically scales the compute cluster based on the number of features.

Answer

It eliminates the need for data cleaning before feature creation.

Answer

It allows models to be deployed directly to external cloud storage.

Question 13

Which Databricks artifact should be used to encapsulate a model, its environment dependencies, and the required code to ensure consistent model behavior across different deployment environments?

Accepted Answer

An MLflow model with a captured Conda or pip environment.. An MLflow model flavor, specifically using the 'MLmodel' metadata file, provides a standard format for packaging models. By including the conda.yaml or requirements.txt file, MLflow captures all environment dependencies. This ensures that when the model is loaded in any environment (e.g., development, staging, production), the environment is reconstructed exactly, preventing version mismatches and runtime errors that frequently cause failed deployments in machine learning pipelines.

Answer

A raw Python script that loads the model weights.

Answer

A Delta Table containing the trained model weights.

Answer

A Git repository containing the training notebook and data.

Question 14

When evaluating a machine learning model, what is the main purpose of creating a separate evaluation dataset in Databricks?

Accepted Answer

To prevent overfitting and assess generalization capability.. The evaluation dataset (or validation set) is vital for assessing model performance on data it has not encountered during training. This prevents overfitting, where the model learns the training data by heart but fails to generalize. Using a separate dataset provides a realistic estimate of the model's performance on future, unseen data, which is essential for making informed decisions before deploying the model to production environments.

Answer

To increase the speed of training by reducing the data size.

Answer

To ensure that the model training pipeline satisfies storage requirements.

Answer

To provide the necessary training data for the model to converge.

Question 15

Which Databricks tool is primarily used for organizing and documenting experiments, tracking parameters, and versioning models during the machine learning lifecycle?

Accepted Answer

MLflow.. MLflow is the integrated platform in Databricks for managing the end-to-end machine learning lifecycle. It includes Tracking for logging parameters and metrics, Projects for packaging code for reproducibility, and the Model Registry for managing model versions and lifecycle transitions. It is the cornerstone of Databricks ML, providing the visibility and control necessary for professional-grade machine learning workflows across a team.

Answer

Databricks SQL.

Answer

Delta Live Tables (DLT).

Answer

Unity Catalog.