Databricks-ML-Assoc Practice Test 4 — 10 Questions

Question 1

A data scientist trains a scikit-learn model in a Databricks notebook and calls mlflow.sklearn.log_model with the registered_model_name argument set to 'churn_model'. Later, a colleague needs to know which source notebook and Git commit produced run ID 3f8a1c. Where can this information be retrieved?

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

Accepted Answer

In the MLflow Experiment page, open the run and inspect the run's tags, including mlflow.source.name and mlflow.source.git.commit.. MLflow automatically attaches metadata tags to each run, including the source notebook path, Git commit, and user. These tags are visible on the run detail page in the experiment UI, making it the authoritative place to trace a run back to its originating notebook and revision. Registry entries and artifact files like conda.yaml do not carry this provenance.

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In the MLflow run's artifact directory, open the conda.yaml file, which records the source notebook path and Git commit used during training.

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In the Databricks Model Registry, open version 1 of churn_model and read the 'Source' field, which stores the originating notebook path and Git commit.

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In the Databricks Jobs UI, locate the task that ran the notebook and read its run history, which stores the MLflow run ID and its source metadata.

Question 2

A data scientist registers a scikit-learn model in the Unity Catalog model registry with the name `prod.ml_team.fraud_detector`. They now want to serve it with Databricks Model Serving. Which value should be supplied as the model identifier when creating the serving endpoint?

Accepted Answer

prod.ml_team.fraud_detector. Because the model was registered in Unity Catalog, the endpoint must be created from the three-level namespace catalog.schema.model_name, optionally pinned to a version. Stage-based URIs and raw storage or run paths do not identify a Unity Catalog registered model, so they cannot be used as the served entity. The fully qualified name is the correct identifier.

Answer

models:/prod.ml_team.fraud_detector/Production

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dbfs:/databricks/mlflow/prod/ml_team/fraud_detector

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runs:/<run_id>/model

Question 3

When performing hyperparameter tuning using Hyperopt on Databricks, which function is primarily used to distribute the training task across the cluster?

Accepted Answer

SparkTrials(). Hyperopt leverages the SparkTrials class to distribute tuning tasks. By using SparkTrials, Databricks orchestrates multiple training trials in parallel across the cluster nodes, significantly reducing the time required for grid or random search. This integration is vital for efficiently exploring complex hyperparameter spaces, allowing data scientists to iterate faster on model architectures without manual resource management or complex parallelization code.

Answer

fmin()

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parallel_task()

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distributed_fit()

Question 4

A data scientist registers a scikit-learn model in Unity Catalog as `prod.ml.churn_model`. They then create a Databricks Model Serving endpoint via the REST API using `served_entities` with `entity_name` set to `prod.ml.churn_model` and `entity_version` set to `"3"`. The endpoint creation fails. What is the most likely cause?

Accepted Answer

The serving endpoint lacks USE CATALOG, USE SCHEMA, and EXECUTE privileges on the Unity Catalog model, so the service principal cannot read the model artifacts.. Serving a Unity Catalog model requires the serving identity to hold USE CATALOG, USE SCHEMA, and EXECUTE privileges on the catalog, schema, and model. Without these grants, the endpoint cannot retrieve the model version, and creation fails. Referencing the model by three-level name and version string is otherwise correct, so permissions are the most likely root cause.

Answer

Model Serving only supports models registered in the workspace Model Registry, not models registered in Unity Catalog.

Answer

The model version must be referenced as a numeric integer, but the field only accepts a string, so the deployment cannot resolve the artifact path.

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The model must first be exported to DBFS as an MLflow artifact tarball before it can be referenced by a serving endpoint.

Question 5

Why should you use an inference table in Databricks Model Serving?

Accepted Answer

To provide visibility into production inputs and predictions. Inference tables allow you to capture every request and response processed by a model endpoint. This is vital for monitoring model performance over time, debugging issues by examining specific input-output pairs, and detecting data drift. Without this captured data, you essentially have no visibility into how the model is performing in the real world, which makes it impossible to maintain and improve the system effectively.

Answer

To increase the speed of model inference

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To automatically retrain the model on new data

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To secure the endpoint against malicious attacks

Question 6

An ML engineer has registered a model in the Databricks Model Registry. The model must be deployed to a REST endpoint that automatically scales with traffic and provides a stable serving environment. Which Databricks capability should they use?

Accepted Answer

Databricks Model Serving with the registered model version as the served entity.. Databricks Model Serving provides a managed, serverless endpoint for registered model versions. It automatically scales with request volume and offers a stable serving environment, eliminating the need to run and maintain a web server. The other options either require manual server management or are intended for interactive or batch use, not production REST serving.

Answer

Export the model as an MLflow artifact and load it in a Databricks notebook using mlflow.pyfunc.load_model for interactive scoring.

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MLflow Model Registry webhooks that trigger a deployment script when the model version transitions to Production.

Answer

A Databricks job that runs a Python web server on a cluster and exposes the model via a public URL.

Question 7

Which of the following is the recommended workflow for developing a scalable model on Databricks?

Accepted Answer

Perform EDA, feature engineering, distributed training, and register with MLflow.. The iterative cycle of data exploration, feature engineering, distributed training, and model registry management is the standard Databricks practice. By utilizing MLflow throughout, scientists ensure reproducibility. Scaling is handled by Spark, and the Feature Store ensures the same data definitions are used for both training and inference. This workflow bridges the gap between ad-hoc experimentation and production-ready machine learning, ensuring that the model is robust, documented, and easily deployable via standard enterprise pipelines.

Answer

Train a model on a local machine, then upload the final artifact to Databricks.

Answer

Use a single-node cluster for all stages to avoid network latency.

Answer

Skip logging metrics to save space and improve training speed.

Question 8

A machine learning engineer needs to track model experiments in Databricks and wants to ensure that model artifacts are versioned automatically. Which approach best leverages Databricks-native capabilities for this requirement?

Accepted Answer

Invoke mlflow.autolog() at the start of the training script to capture parameters, metrics, and model artifacts automatically.. MLflow tracking is the standard in Databricks for recording experiments. Using mlflow.autolog() automatically captures parameters, metrics, and artifacts during model training runs, ensuring consistency and reproducibility across team members. This is essential for managing the model lifecycle, as it eliminates manual logging errors and provides a clear lineage from source code to the registered model artifact within the Unity Catalog or Model Registry environment.

Answer

Manually save model weights to a DBFS mount point using a standard Python dictionary to track parameters.

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Write a custom function to append training results to a Delta table and store serialized model objects as binary columns.

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Use the standard Python logging module to output training metrics to a text file saved in the user home directory.

Question 9

A data scientist wants to package a training script so that it can be run repeatedly with different hyperparameters and shared with colleagues who use different Python library versions. The script must create a reproducible environment. Which MLflow component should they use to define the project and its dependencies?

Accepted Answer

MLflow Projects with an MLproject file and a conda.yaml environment specification.. MLflow Projects are designed to package code with a clear entry point and parameter definitions, and the conda.yaml file locks dependency versions for reproducibility. This lets colleagues run the same training script with different hyperparameters in an isolated environment. Tracking, Models, and the Model Registry serve different lifecycle stages and do not provide project packaging.

Answer

MLflow Tracking with a dedicated experiment and run tags for each library version.

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MLflow Models with a custom Python function flavor and a requirements.txt file.

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MLflow Model Registry with stage transitions and model version descriptions.

Question 10

What is the primary function of the 'Model Registry' in the Databricks ML ecosystem?

Accepted Answer

To provide a centralized platform for model lifecycle management. The Model Registry provides a centralized hub for managing the full lifecycle of a machine learning model. It allows teams to track versions, manage model stages (Staging, Production, Archived), and facilitate collaboration. By serving as a 'source of truth' for model status, it ensures that stakeholders can confidently transition models from development to production while maintaining complete audit trails and lineage of all changes applied to the model artifacts.

Answer

To store raw training datasets for feature engineering

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To execute distributed hyperparameter tuning experiments

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To monitor real-time inference drift in production