Databricks-ML-Assoc Multiple Select — 10 Questions

Question 1

Which TWO actions should be taken to ensure reproducibility of a Databricks ML model experiment?

Accepted Answer

Log the Git commit hash along with experiment metadata in MLflow.. Reproducibility in Databricks is achieved by capturing the state of the code, the environment, and the data. By utilizing Git integration and MLflow tracking, data scientists can point to the exact version of the code and environment used for an experiment. Using versioned Delta tables ensures that the data state can also be reconstructed, preventing 'data drift' from invalidating the results of historical experiments as underlying data tables change.

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Hardcode the data file paths to local desktop directories.

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Only log the model artifact and ignore the environment dependencies.

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Manually delete all previous MLflow runs to keep the tracking server clean.

Question 2

A data scientist is using Databricks Feature Store to create a feature table for a recommendation model. They want to ensure that the feature table can be used for both training and batch scoring, and that it supports point-in-time correctness. Which two actions must they take when creating the feature table? (Choose two.)

Accepted Answer

Define a primary key for the feature table.. To create a feature table in Databricks Feature Store that supports training, batch scoring, and point-in-time correctness, the data scientist must define a primary key and a timestamp key. The primary key identifies entities for joins, and the timestamp key enables time-aware joins to prevent leakage. Other actions like enabling time travel manually, partitioning, or using streaming tables are not required and may be unnecessary or counterproductive. Thus, the correct choices are defining a primary key and specifying a timestamp key.

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Set the table as a streaming table.

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Partition the feature table by the primary key.

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Enable time travel on the feature table.

Question 3

A data scientist is using MLflow to track experiments on Databricks. They want to compare multiple runs and identify the best performing model based on a custom metric. Which TWO features of MLflow can be used to achieve this? (Choose two.)

Accepted Answer

MLflow tracking API's search_runs method. The MLflow UI's compare runs feature and the tracking API's search_runs method both allow you to compare runs and sort by custom metrics. The UI provides a visual side-by-side comparison, while search_runs enables programmatic querying and sorting. Together, they enable identifying the best performing run based on a custom metric.

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MLflow Projects with a custom entry point

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MLflow Model Registry's stage transitions

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MLflow's automatic logging with autolog()

Question 4

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.

Question 5

Which TWO of the following are benefits of using the Databricks Feature Store for machine learning workflows?

Accepted Answer

It enables consistent feature definitions across training and inference pipelines.. The Databricks Feature Store facilitates feature reuse across different teams and projects, preventing redundant engineering work. Additionally, it ensures point-in-time correctness by preventing data leakage during training through time-travel capabilities. These features are critical for maintaining consistency between training and inference environments, reducing the likelihood of training-serving skew, and streamlining the overall MLOps lifecycle within the Databricks platform.

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It automatically scales the compute resources for training deep learning models.

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It automatically retrains models when feature data changes.

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It converts unstructured image data into structured feature vectors.

Question 6

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.

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A pre-trained model checkpoint to initialize the search.

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A dedicated GPU cluster for every single trial run.

Question 7

Which TWO of the following practices are recommended when performing feature engineering on Databricks using Feature Store to ensure consistency between training and inference?

Accepted Answer

Register feature tables in the Databricks Feature Store with unique primary keys.. Feature Store ensures that the exact same transformation logic used during model training is accessible during low-latency inference. By utilizing the Feature Store's unified interface, data scientists prevent training-serving skew, which is a common cause of poor model performance in production. This practice promotes reproducibility and streamlines the deployment pipeline by decoupling data preparation from model training code, ensuring that production features are calculated reliably using the same definitions as training features.

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Hardcode all feature transformation logic directly into the model training notebook.

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Calculate features in real-time within the model inference endpoint only.

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Avoid using time-based features to ensure the model remains simple.

Question 8

Which TWO factors should be considered when choosing between Batch Inference and Real-time Inference for a model in Databricks?

Accepted Answer

The required latency for the model predictions to reach the end user.. Choosing between batch and real-time inference depends on latency requirements and data availability. Batch is suited for high-volume, non-time-sensitive tasks, whereas real-time is necessary for low-latency, event-driven predictions. Understanding these trade-offs is essential for ML architects to design cost-effective and performant serving strategies that align with business needs, as they dictate the infrastructure configuration, monitoring complexity, and the overall deployment strategy for the model lifecycle.

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The memory limit of the driver node during the model training phase.

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The number of features used in the machine learning model.

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The programming language used to write the ML model training code.

Question 9

Which TWO of the following are primary benefits of using the Databricks Feature Store for machine learning workflows?

Accepted Answer

Eliminating training-serving skew by using the same feature definitions for both training and inference.. The Databricks Feature Store facilitates feature reuse across teams and prevents training-serving skew. By centralizing features, organizations ensure that the exact data used during model training is consistently applied during real-time inference. This eliminates the need for redundant feature engineering pipelines and provides a unified lineage, which is essential for auditability and ensuring that models perform reliably across different production environments.

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Providing a platform for distributed model training using Spark MLlib exclusively.

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Automating the deployment of machine learning models to production environments.

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Directly replacing the need for SQL-based data warehousing in Databricks.

Question 10

A data science team is preparing to deploy a custom machine learning model to Databricks Model Serving. Which TWO steps are required to ensure the model can successfully load and serve predictions using MLflow? Choose 2 answers.

Accepted Answer

Log the model using mlflow.sklearn.log_model() or equivalent flavor with a defined input example and model signature.. Deploying models to Databricks Model Serving requires logging the model with a valid signature and environment specification, and registering it to the Unity Catalog or Workspace Model Registry. These steps ensure the serving infrastructure can automatically provision the correct scoring environment, handle incoming JSON payloads, and validate data types against expected schemas.

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Manually install all Python dependencies on every serving endpoint worker node via a custom startup script.

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Export the model artifact into a raw Hadoop Distributed File System directory without using the MLflow format.

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Configure Apache Spark streaming jobs to continuously push payload batches into the model serving container endpoint.