Databricks-ML-Assoc ML Workflows Practice Test 5 — 15 Questions

Question 1

Refer to the exhibit. A data scientist receives this error while trying to load a model. What is the most likely cause of this failure in the workflow?

Accepted Answer

The model version number is incorrectly referenced in the code.. This error indicates that the code is attempting to reference a specific version of a model that does not exist in the Model Registry. This often occurs when a script relies on hard-coded version numbers rather than dynamic lookups or stage-based references. In a production workflow, using stage aliases like 'Production' is preferred to ensure that the application always fetches the current valid version without needing manual updates when models are updated.

Answer

The model has been deleted from the registry.

Answer

The cluster does not have permission to access the MLflow registry.

Answer

The MLflow tracking URI is pointing to a different workspace.

Question 2

An ML engineer is orchestrating an end-to-end machine learning pipeline using Databricks Jobs. The pipeline consists of data preparation, distributed hyperparameter tuning with Hyperopt, and model registration. The engineer needs to pass the best-performing model's run ID from the Hyperopt task to the subsequent model registration task dynamically. Which mechanism should the engineer use in Databricks Jobs to achieve this?

Accepted Answer

Use dbutils.jobs.taskValues to set the run ID in the producer task and get the value in the consumer task.. Databricks Jobs task values allow tasks to pass small amounts of data, such as strings or numeric metrics, downstream to other tasks within the same workflow. Using dbutils.jobs.taskValues.set inside the Hyperopt notebook and dbutils.jobs.taskValues.get in the registration notebook enables seamless dynamic orchestration without relying on external storage solutions.

Answer

Store the run ID in a shared Delta table inside the workspace and query it from the downstream task.

Answer

Write the run ID to a text file in DBFS root and read it back in the downstream task.

Answer

Pass the run ID as a static parameter defined in the JSON job configuration before execution begins.

Question 3

An ML engineer is building a recurring batch inference pipeline in Databricks. The model is registered in Unity Catalog as a model version and must always use the version currently tagged as 'champion', which is reassigned after each retraining run. The engineer wants the inference notebook to resolve this alias at runtime rather than hard-coding a version number. Which approach should the engineer use in the notebook?

Accepted Answer

Load the model with mlflow.pyfunc.load_model("models:/catalog.schema.model_name@champion") to resolve the alias at runtime.. Using a model alias in the models:/ URI lets the pipeline resolve the promoted version at runtime, so retraining and promotion do not require editing the inference notebook. The alias is a mutable pointer managed in Unity Catalog, and mlflow.pyfunc.load_model understands the models:/ scheme with the @alias suffix, making it the intended mechanism for champion-based batch scoring.

Answer

Load the model with mlflow.pyfunc.load_model("models:/catalog.schema.model_name/Production") to resolve the stage at runtime.

Answer

Read the model version number from the MLflow Tracking API at runtime and pass it to mlflow.pyfunc.load_model with a numeric version URI.

Answer

Download the model artifacts from the Unity Catalog volume path with dbutils.fs.cp and load them with mlflow.pyfunc.load_model using the local path.

Question 4

An ML engineer is building a Databricks Job that trains a model with scikit-learn and needs to capture hyperparameters, evaluation metrics, and the resulting model artifact for each run. The team wants to compare runs visually in the workspace and later promote the best model to the Model Registry. Which MLflow capability should the engineer use to record these per-run details?

Accepted Answer

MLflow Tracking with autologging enabled for scikit-learn. Recording per-run parameters, metrics, and model artifacts is the job of MLflow Tracking, and the scikit-learn autologging integration captures these automatically for standard estimators. That gives the team comparable runs in the workspace experiment UI and a logged model artifact that can subsequently be registered and promoted through the Model Registry.

Answer

Databricks Feature Store with online store enabled

Answer

Databricks Jobs task dependencies with a condition task

Answer

MLflow Model Registry stage transitions

Question 5

A team trains a model with Databricks Feature Store features and logs the training set using feature_lookups. At inference time, they want the model to automatically retrieve the same feature values from the online store so the serving endpoint does not require the caller to supply those features. What must the team do when logging the model so this automatic lookup works?

Accepted Answer

Log the model with the feature engineering client's log_model and include the feature_lookups used during training. When a model is logged through the feature engineering client with the same feature lookups used during training, the model metadata carries the information needed to resolve those features from the online store at serving time. Callers then only supply primary keys, and the endpoint assembles the full feature vector automatically, keeping training and inference feature logic consistent.

Answer

Wrap the model in a PythonModel that calls the Feature Store client inside its predict method

Answer

Publish the feature table with a primary key and enable streaming on the source Delta table

Answer

Register the model in Unity Catalog and grant the serving endpoint EXECUTE on the registered model

Question 6

A data scientist trains a scikit-learn model in a Databricks notebook and logs it with MLflow. She now needs to promote the exact model artifact to the Databricks Model Registry so that it can be served. Which MLflow API call accomplishes this promotion?

Accepted Answer

mlflow.register_model(model_uri, name). Registering a logged model means creating a named entry in the Model Registry that points at a specific run artifact. The mlflow.register_model() function performs exactly that linkage by accepting the run-relative model URI and the desired registered model name, returning a version that can then be transitioned to a stage or alias for serving. Saving, creating an empty registered model, or logging an artifact each perform only one piece of the workflow and leave no registered version.

Answer

mlflow.tracking.MlflowClient().create_registered_model(name)

Answer

mlflow.sklearn.save_model(model, path)

Answer

mlflow.log_artifact(model, artifact_path='model')

Question 7

You are training a scikit-learn model with MLflow in a Databricks notebook. The model's preprocessing includes a custom Python function that you wrote in the notebook. You need to register the model to the Databricks Model Registry and later deploy it with Model Serving, ensuring the preprocessing is applied automatically at inference. Which approach should you use?

Accepted Answer

Wrap the preprocessing and model in an mlflow.pyfunc.PythonModel subclass, and log it with mlflow.pyfunc.log_model.. To ensure custom preprocessing runs at inference time, the logic must be packaged inside the logged model artifact. Subclassing mlflow.pyfunc.PythonModel and logging with mlflow.pyfunc.log_model bundles the preprocessing code with the trained model, so Model Serving executes it automatically. Other approaches either misuse parameters, store code without wiring it into scoring, or assume unsupported endpoint hooks.

Answer

Register the model with the sklearn flavor, and configure a pre-processing hook in the Model Serving endpoint configuration.

Answer

Log the preprocessing function as a separate artifact using mlflow.log_artifact, and reference it from the model's conda environment.

Answer

Log the model with mlflow.sklearn.log_model, passing the custom preprocessing function in the signature parameter.

Question 8

An ML engineer has a scikit-learn model trained locally and wants to log it to MLflow with a signature so that Databricks Model Serving can enforce input schema validation. The engineer calls mlflow.sklearn.log_model(model, 'model') but does not use infer_signature. What is the most accurate consequence when the model is later served on Databricks Model Serving?

Accepted Answer

The model will be served, but the endpoint cannot validate incoming request schemas against a stored signature, so malformed inputs may reach the model.. Logging a model without infer_signature leaves the MLflow model metadata without an input schema. Databricks Model Serving uses that stored signature to validate request payloads, so the endpoint will accept requests but cannot reject malformed ones. To gain schema enforcement, the engineer should call infer_signature on a sample DataFrame and pass it to log_model.

Answer

The model will be served with a default signature that expects a single double column named 'features'.

Answer

Databricks Model Serving will automatically infer the signature from the first request payload and cache it for later validation.

Answer

Databricks Model Serving will reject the model version because a signature is mandatory for all Unity Catalog registered models.

Question 9

An ML engineer is using MLflow Tracking to compare multiple runs of a hyperparameter tuning experiment. The engineer wants to quickly identify the run that achieved the best validation accuracy and then promote that run's model to the Model Registry. Which MLflow feature allows the engineer to view and compare runs in a centralized UI?

Accepted Answer

MLflow Tracking UI. The MLflow Tracking UI is designed to log and compare runs, displaying metrics, parameters, and artifacts in a sortable table. It allows the engineer to identify the best run and then register its model to the Model Registry with a few clicks, streamlining the workflow.

Answer

MLflow Model Registry

Answer

MLflow Projects

Answer

MLflow Models

Question 10

An ML engineer runs an automated hyperparameter sweep with MLflow on a Databricks cluster. The sweep launches 200 runs, and the engineer wants to retrieve, in a notebook, the run ID of the single run that achieved the highest validation accuracy so it can be registered. Which approach correctly identifies that run?

Accepted Answer

Use mlflow.search_runs() with an experiment ID, order by metrics.validation_accuracy descending, and take the first row's run_id.. To find the best run across many, the tracking API must be queried with a metric sort. mlflow.search_runs() supports order_by on metrics.<name> and returns a DataFrame whose first row, when sorted descending, is the top-scoring run. Its run_id can then be passed to registration. Fetching individual runs or reading raw artifact files cannot rank the sweep and relies on unsupported or incomplete data.

Answer

Call mlflow.get_run(run_id) for each run created by the sweep and compare the metrics in Python.

Answer

Use mlflow.list_experiments() and select the experiment with the most runs, then register its latest version.

Answer

Read the MLflow experiment's artifact location from DBFS and parse the meta.yaml files to find the highest metric.

Question 11

An ML engineer runs a hyperparameter tuning job on Databricks using Hyperopt with SparkTrials. The objective function trains a model and returns a validation metric. The engineer notices that each trial logs to the same MLflow run, making it impossible to compare trials. What should the engineer do to ensure each trial appears as a separate MLflow run?

Accepted Answer

Call mlflow.start_run(nested=True) at the beginning of the objective function.. With Hyperopt and SparkTrials on Databricks, the tuning job creates a parent MLflow run. To capture each trial separately, the objective function must open a nested run using mlflow.start_run(nested=True). This produces child runs per trial, each with its own parameters and metrics, which can be compared in the MLflow experiment UI. Tags, metric names, or experiment changes do not create distinct runs.

Answer

Use mlflow.set_tag to assign a unique trial ID tag before logging metrics.

Answer

Pass a unique run_name argument to mlflow.log_metric for each trial.

Answer

Set the environment variable MLFLOW_EXPERIMENT_ID to a unique value inside the objective function.

Question 12

A data scientist wants to package a training script with its Python dependencies and parameters so that the same code can be rerun on a different Databricks cluster or shared with a colleague and reproduced exactly. Which MLflow component is designed for packaging and reproducing project code in this way?

Accepted Answer

MLflow Projects. MLflow Projects are the packaging mechanism: an MLproject file names entry points, parameters, and the environment, and running the project recreates that environment to execute the code. This makes training scripts portable and reproducible across clusters and users, which is exactly the requirement described.

Answer

MLflow Tracking

Answer

MLflow Models

Answer

MLflow Model Registry

Question 13

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?

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.

Answer

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

Answer

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.

Answer

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 14

A data scientist trains a scikit-learn model with MLflow tracking in a Databricks notebook. They call mlflow.sklearn.log_model(model, 'model') but later find that the Registered Model's schema shows no input signature, preventing automatic schema enforcement during serving. What should they have done to capture the model signature?

Accepted Answer

Call mlflow.sklearn.log_model(model, 'model', signature=mlflow.models.infer_signature(X_train, model.predict(X_train))). The model signature is created and attached during logging, not afterward. mlflow.models.infer_signature derives input and output schema from sample data and predictions, and passing it to log_model stores it in the MLmodel file. This enables schema enforcement and automatic serving input validation. Other options either do not create a signature or attempt unsupported post-hoc edits.

Answer

Register the model in the workspace Model Registry and then edit the signature in the UI

Answer

Log the training DataFrame as an artifact and add a signature manually in the MLmodel file after training

Answer

Enable autologging with mlflow.sklearn.autolog() before training and logging

Question 15

A data scientist wants to compare the performance of three hyperparameter configurations for a Spark ML model. They need a central place to view metrics like RMSE and MAE across runs, and to filter runs by parameters. Which Databricks capability should they use?

Accepted Answer

MLflow Tracking with the Databricks-hosted experiment UI. MLflow Tracking, accessed through the Databricks experiment UI, is the component that logs and visualizes run metrics, parameters, and artifacts. It enables filtering and side-by-side comparison of runs, which is exactly what the data scientist needs for evaluating hyperparameter configurations using RMSE and MAE.

Answer

Databricks Jobs with task dependencies

Answer

Databricks Feature Store

Answer

Databricks Model Registry