Databricks-ML-Assoc Databricks Machine Learning Practice Test 5 — 15 Questions

Question 1

What is the primary function of the 'Model Signatures' in MLflow?

Accepted Answer

To define the input/output schema for the model.. Model Signatures define the expected data types and structure for the inputs and outputs of a machine learning model. This metadata is crucial for data validation and automated infrastructure testing in Databricks. By enforcing this schema, you ensure that the serving layer can validate incoming requests against the model's expectations, which significantly reduces runtime errors and improves the overall reliability of production machine learning applications within the workspace.

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To act as a digital watermark for security.

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To encrypt the model artifacts.

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To identify the author of the model.

Question 2

A data scientist is training a machine learning model on Databricks and needs to ensure that every experiment run is automatically tracked, including parameters, metrics, and model artifacts. Which component should the scientist use to achieve this with minimal code changes?

Accepted Answer

MLflow Tracking with autologging. MLflow Tracking is the core component of the Databricks Machine Learning platform designed for logging experiments. By leveraging the autologging feature, the platform automatically captures parameters, metrics, and model signatures without requiring manual instrumentation for most popular frameworks like Scikit-learn or PyTorch. This ensures reproducibility and enables easy comparison of model performance across different runs, which is essential for managing model lifecycles in a production-oriented machine learning environment.

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Databricks Feature Store

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MLflow Model Registry

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Databricks SQL Warehouse

Question 3

A data scientist is using MLflow on Databricks to log a scikit-learn model. They call mlflow.sklearn.log_model(model, 'model') and then inspect the run. They notice the model artifact is stored, but the run does not appear in the Models page of the workspace. They did not call any model registration function. What is the most likely reason the model is not listed in the Models page?

Accepted Answer

The model must be registered in the Model Registry using mlflow.register_model() or the model_signing/registration API before it appears in the Models page.. The Models page in Databricks shows models that have been registered in the Model Registry. Logging a model with mlflow.sklearn.log_model() stores the artifact in the run but does not create a registered model. To make the model visible, the data scientist must register it, for example by using mlflow.register_model() or the model registry UI/API.

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The model must be logged using mlflow.pyfunc.log_model() instead of mlflow.sklearn.log_model() to be visible in the Models page.

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The model artifact is stored in the run but not in the workspace's default model store, so it cannot be displayed.

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MLflow only shows models in the Models page if the run is part of an experiment with a specific tag.

Question 4

A machine learning engineer is orchestrating a multi-step training workflow on Databricks using Databricks Jobs. The workflow includes data preprocessing, model training, and evaluation. The engineer needs to ensure that the evaluation step runs only if the training step completes successfully, and that the preprocessing step runs first. Which feature of Databricks Jobs should be used to define these dependencies?

Accepted Answer

Task dependencies using the depends_on field. Task dependencies via the depends_on field are the core mechanism in Databricks Jobs for orchestrating multi-task workflows. They allow you to specify that a task runs only after its upstream tasks complete successfully, enabling conditional execution. This directly satisfies the need to run preprocessing first, then training, and evaluation only if training succeeds. Other options address compute scaling, failure recovery, or version control, not workflow dependencies.

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Repair run with task-level retries

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Databricks Repos integration with Git

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Job clusters with autoscaling enabled

Question 5

A data scientist is using MLflow to log a custom PyTorch model in a Databricks notebook. They want to register the model in the Databricks Model Registry and later serve it with MLflow model serving. Which function should they call within their MLflow run to log the model with the necessary signature and dependencies?

Accepted Answer

mlflow.pytorch.log_model(). To log a PyTorch model, the correct function is mlflow.pytorch.log_model. It handles saving the model in MLflow's format, capturing dependencies and signatures, and making it available for registration and serving. Other functions either don't exist, serve different purposes, or are not flavor-specific.

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mlflow.save_model()

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mlflow.log_model()

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mlflow.register_model()

Question 6

A data scientist is using MLflow to track experiments on Databricks. They notice that the metrics logged during a run are not appearing in the MLflow UI. The run is part of an experiment with many runs. What is the most likely cause for the missing metrics?

Accepted Answer

The metrics were logged to a different experiment than the one currently selected in the MLflow UI.. Metrics are associated with a specific run, and runs belong to an experiment. If the run was created under a different experiment than the one selected in the MLflow UI, its metrics will not be visible. This often happens when the experiment is changed mid-session or when using a different tracking URI. Checking the experiment ID of the run resolves the issue.

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The metrics were logged with a step value that is not monotonically increasing, causing MLflow to ignore them.

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The metrics were logged as strings instead of numeric values, so MLflow silently drops them.

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The metrics were logged using mlflow.log_metric but the run was not properly ended, so they are still buffered.

Question 7

A data scientist is performing hyperparameter tuning for a scikit-learn model using MLflow on a Databricks cluster. They want to parallelize the tuning to reduce total runtime. Which Databricks-supported method allows them to run multiple trials concurrently with minimal code changes?

Accepted Answer

Use Hyperopt with `SparkTrials`.. Hyperopt's `SparkTrials` is the Databricks-recommended way to parallelize hyperparameter tuning. It distributes trials across Spark executors, integrates with MLflow for automatic logging, and requires only a small change to the tuning code. Other options either do not provide parallelism or require manual orchestration, making them less efficient.

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Use Databricks Jobs to schedule multiple notebook runs with different parameters.

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Use MLflow's `mlflow.sklearn.autolog()` with a single run.

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Use MLflow Projects to package the training code and run it via `mlflow run`.

Question 8

A machine learning engineer is using Databricks Feature Store to create a feature table for a model that predicts customer churn. The feature table includes customer demographics and transaction history. The engineer wants to ensure that the model can access the latest feature values during online inference. What should the engineer do?

Accepted Answer

Publish the feature table to an online store using the Databricks Feature Store API.. To serve features for online inference, the feature table must be published to an online store. Databricks Feature Store provides APIs to publish feature tables to low-latency databases, enabling the model to retrieve the latest feature values in real time. This is a key step when deploying models that require online features, ensuring consistency between training and serving.

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Enable change data feed on the feature table to capture updates.

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Set up a Databricks SQL warehouse to query the feature table.

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Use MLflow to log the feature table as a model artifact.

Question 9

A data scientist is using MLflow to track experiments on Databricks. They want to log a custom metric that is computed during model training and later compare it across runs using the MLflow UI. Which MLflow API call should they use?

Accepted Answer

mlflow.log_metric("custom_metric", value). The data scientist needs to track a numeric value computed during training and compare it across runs. MLflow provides specific APIs for different types of data: parameters for inputs, metrics for numeric outputs, tags for metadata, and artifacts for files. Using log_metric ensures the value is stored as a metric and can be visualized and compared in the MLflow UI. The other APIs serve different purposes and would not provide the desired functionality.

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mlflow.set_tag("custom_metric", value)

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mlflow.log_artifact("custom_metric", value)

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mlflow.log_param("custom_metric", value)

Question 10

A machine learning engineer is using Databricks Feature Store to create a training dataset for a model that predicts customer lifetime value. The feature table includes a timestamp key. Which TWO statements are true regarding point-in-time correctness when creating the training set? (Choose two.)

Accepted Answer

The training set creation process uses the timestamp key to join features as of the label event time, preventing data leakage.. Point-in-time correctness in Feature Store requires a timestamp key and uses it to join features as of the label event time. This prevents data leakage by ensuring only past data is used. The other options incorrectly describe behavior that would either ignore timestamps or introduce future data, which are not how Feature Store operates.

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The training set will include feature values that were recorded after the label event time if they are available.

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If multiple feature values exist for an entity at the same timestamp, the training set will randomly select one.

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The training set will automatically include the latest feature values for each entity, regardless of the timestamp.

Question 11

A machine learning engineer is using MLflow on Databricks to track an experiment. They want to record the model's hyperparameters and evaluation metrics, but they do not want to save the trained model artifact. Which MLflow API calls should they use?

Accepted Answer

mlflow.log_param() and mlflow.log_metric(). The scenario requires logging hyperparameters and metrics without saving model artifacts. MLflow provides dedicated functions for this: log_param for individual parameters and log_metric for metrics. These write to the tracking server and do not create artifacts. Other functions either create artifacts or manage run lifecycle, neither of which meets the specific requirement.

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mlflow.start_run() and mlflow.end_run()

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mlflow.log_artifact() and mlflow.log_model()

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mlflow.set_tag() and mlflow.set_experiment()

Question 12

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 13

A data scientist is using MLflow on Databricks to track experiments. After running several training jobs, they notice that the run metrics are recorded but the model artifact is missing when they view the run details. They logged the model using the default MLflow API. What is the most likely cause?

Accepted Answer

The model artifact was logged outside of the active MLflow run context, so it was not associated with the run.. The model artifact is missing because log_model was called outside an active MLflow run, so it was not linked to the run. Metrics were logged within the run, explaining their presence. To ensure artifacts are associated, all logging calls must occur inside a run context created by mlflow.start_run(). This is a common oversight when refactoring code or using helper functions.

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The model was too large to be stored in the MLflow artifact store, so it was automatically skipped.

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The model was logged using mlflow.sklearn.log_model, but the run was not ended, so artifacts are not persisted.

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The MLflow tracking server was not configured to log artifacts, so only metrics were recorded.

Question 14

A machine learning engineer wants to register a model in the Databricks Model Registry using MLflow. They have already trained a model and logged it with MLflow. Which method should they use to register the model programmatically?

Accepted Answer

mlflow.register_model(model_uri, name). To register a model programmatically in the Databricks Model Registry, the engineer should use mlflow.register_model, which creates a new model version linked to the logged run. This is the direct method for registration. The other options either create a registered model without a version, log the model without registering, or save the model locally without registry integration. Thus, mlflow.register_model is the appropriate choice.

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mlflow.log_model(model, name)

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mlflow.create_registered_model(name)

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mlflow.save_model(model, path)

Question 15

A data scientist has trained a model using scikit-learn and wants to log it to MLflow for deployment. They need to ensure that the model can be served with the correct dependencies. Which MLflow function should they use to log the model?

Accepted Answer

`mlflow.sklearn.log_model()`. `mlflow.sklearn.log_model()` is specifically designed to log scikit-learn models, capturing the model flavor, dependencies, and signature. This allows the model to be served or deployed with the correct environment. Other functions either log artifacts, metrics, or register an already logged model, none of which fulfill the logging requirement.

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`mlflow.log_metric()`

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`mlflow.register_model()`

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`mlflow.log_artifact()`