Databricks-ML-Assoc Practice Test 14 — 15 Questions

Question 1

An ML engineer registers a model to the Databricks Model Registry and moves it to the 'Production' stage. A downstream batch scoring job references the model as models:/churn_model/Production. A data scientist then registers a new model version and transitions it to 'Production'. What happens to the downstream batch scoring job the next time it runs?

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

Accepted Answer

The job loads the newly promoted Production version automatically, because the models:/churn_model/Production URI resolves to whichever version currently holds that stage.. Stage-based model URIs like models:/churn_model/Production resolve dynamically at load time to the version currently assigned to that stage. When a new version is transitioned into Production, subsequent scoring runs automatically use it, allowing controlled promotion without editing job code. The URI is not pinned or cached at job creation.

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The job fails because a model URI cannot reference a stage once more than one version has occupied that stage.

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The job continues to load the previously promoted version, because model URIs are cached and pinned at the time the job was first created.

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The job loads the newest registered version regardless of stage, because the models:/ URI ignores stage names and always selects the highest version number.

Question 2

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.

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

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

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

Question 3

An ML engineer is using Databricks Jobs to orchestrate a machine learning pipeline that includes data ingestion, feature engineering, model training, and batch scoring. The engineer wants to ensure that the pipeline is reproducible, handles failures gracefully, and allows for easy debugging of individual tasks. Which TWO features of Databricks Jobs should the engineer leverage to meet these requirements? (Choose two.)

Accepted Answer

Repair runs to re-execute only failed or skipped tasks without rerunning successful ones.. Task dependencies define execution order and conditional branching, ensuring that downstream tasks like training run only after upstream tasks succeed and enabling failure handling. Repair runs allow re-executing only failed or skipped tasks, which is critical for debugging and recovering from failures without rerunning the entire pipeline. Together, these Databricks Jobs features provide orchestration, graceful failure handling, and efficient debugging.

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Notebook parameters to pass dynamic values between tasks.

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Git integration to version-control notebooks and reference specific commits.

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Job clusters that are created for each run and terminated upon completion.

Question 4

A data scientist has trained a model and wants to deploy it for real-time inference with automatic scaling and without managing infrastructure. Which Databricks feature should they use?

Accepted Answer

Databricks Model Serving. Databricks Model Serving is the managed solution for deploying MLflow models as scalable REST endpoints. It handles provisioning, scaling, and monitoring, so the data scientist can focus on the model. It integrates with the Model Registry and supports real-time inference without infrastructure overhead.

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Databricks SQL endpoint

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A standalone Flask app on a Databricks cluster

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MLflow Model Registry

Question 5

A data scientist is working in a Databricks notebook and wants to view the results of their MLflow runs, including metrics and parameters, directly within the notebook. Which MLflow function should they use?

Accepted Answer

mlflow.search_runs(). mlflow.search_runs() is designed to query runs within an experiment and return a pandas DataFrame with columns for run ID, parameters, metrics, and other metadata. This makes it straightforward to display and analyze multiple runs in a notebook. Other functions either retrieve single runs or list experiments without providing run-level details in a tabular format.

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mlflow.get_experiment_by_name()

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mlflow.get_run()

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mlflow.list_experiments()

Question 6

A machine learning engineer is building a training pipeline in Databricks. They want each run to record the exact Git commit hash, the versions of scikit-learn and MLflow used, and the input data path so the run can be reproduced later. Which MLflow tracking capability should they use to capture this information with the least custom code?

Accepted Answer

mlflow.set_tags() combined with MLflow's automatic logging of the source Git commit and environment.. MLflow Tracking automatically records source information, including the Git commit when the run executes from a repository, and the environment details. Tags provide a searchable place for custom metadata such as the input data path. Together they deliver reproducibility context without custom parsing code, unlike parameters, artifacts, or registry entries that are meant for different purposes.

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mlflow.register_model() to create a model version that stores the Git commit hash and data path in its description.

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mlflow.log_artifact() to upload a text file containing the Git commit hash, library versions, and data path.

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mlflow.log_params() to log the Git commit hash, library versions, and data path as run parameters.

Question 7

A team is using Databricks Feature Store to create a feature table that will be used for both batch training and online inference. They need to ensure the feature table supports point-in-time lookups for training and low-latency reads for serving. Which TWO of the following statements are correct about meeting these requirements? (Choose two.)

Accepted Answer

The feature table must be published to an online store to support low-latency online inference.. To support both batch training and online inference, the feature table must be published to an online store for low-latency reads, and point-in-time lookups are handled using the primary key and timestamp key during training set creation. Parquet storage and automatic online store creation are not requirements, and partitioning is optional.

Answer

Point-in-time lookups require the feature table to be partitioned by the timestamp key.

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Online stores are automatically created when a feature table is registered, requiring no additional configuration.

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The feature table must be stored in Parquet format to enable low-latency reads.

Question 8

A data scientist wants to compare the accuracy, F1 score, and training duration of several model training runs side by side in a single table, and visually inspect how a hyperparameter affected the metric across runs. Which MLflow capability should be used?

Accepted Answer

The MLflow Experiment page, where runs can be selected and compared using the metrics table and chart views.. The MLflow Experiment page aggregates all runs with their parameters and metrics, supports selecting multiple runs for a side-by-side table, and offers chart views that plot metrics against parameters. This is the native way to compare accuracy, F1, and training duration and to visualize hyperparameter effects without writing custom code.

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The Databricks Jobs UI, where each task run records the metrics emitted by the notebook and displays them in a comparison chart.

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The MLflow Tracking Server's REST API, which must be queried programmatically to retrieve and compare run metrics.

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The Databricks Model Registry, where each model version lists its metrics and can be compared against other versions in a table.

Question 9

A machine learning engineer is building a scikit-learn model with hyperparameter tuning on Databricks. They want each trial to be tracked as a nested run under a single parent run in MLflow so that all trials are grouped together and the best parameters can be compared easily. Which MLflow API call should they use to start each trial run so that it is nested under the currently active run?

Accepted Answer

mlflow.start_run(nested=True). Nested runs are created by calling mlflow.start_run with nested=True while a parent run is active. This groups all trial runs under one parent, making it easy to compare metrics and select the best hyperparameters. Other MLflow APIs either create separate experiments or log artifacts, and manually setting parentRunId is not the supported nesting mechanism.

Answer

mlflow.set_tag("mlflow.parentRunId", parent_id)

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mlflow.log_artifact()

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mlflow.create_experiment()

Question 10

An ML platform team is standardizing how models move from experimentation to production on Databricks. They want promotion decisions to be auditable and to prevent unvalidated models from serving live traffic. Which TWO practices align with Databricks Model Registry and Unity Catalog governance? (Choose two.)

Accepted Answer

Store models in Unity Catalog with a three-level name and control access using catalog, schema, and model privileges.. Auditable promotion in Unity Catalog rests on governed model objects and movable aliases. Three-level names plus catalog, schema, and model privileges control who can read, write, and serve models, while champion and challenger aliases record which version is approved for production or evaluation. Together they let consumers reference a stable name that promotion workflows update, with every change visible in the registry and subject to access control.

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Embed promotion logic entirely in notebook widgets so reviewers approve versions from the notebook UI.

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Allow any team member with workspace access to register and promote models to reduce bottlenecks.

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Rely on MLflow stages like Staging and Production to gate which model version receives live traffic.

Question 11

An ML engineer needs to deploy a model for real-time inference with automatic scaling and a REST endpoint that requires token-based authentication. The model artifacts are already registered in the Databricks Model Registry. Which Databricks capability should be used?

Accepted Answer

Databricks Model Serving, which creates a scalable REST endpoint for registered model versions with built-in authentication and autoscaling.. Databricks Model Serving provides managed, autoscaling REST endpoints for registered model versions, with authentication handled through Databricks tokens or service principals. It is the native capability for real-time inference and requires no custom Flask hosting, cluster networking, or manual scaling logic, unlike the other options.

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MLflow's built-in model serving command, which deploys the model to a local REST server on the cluster and exposes it through the workspace URL.

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A Databricks Job with a continuous trigger that runs a notebook hosting a Flask application on the driver node.

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Databricks SQL endpoint with an AI function that calls the registered model for each row in a query.

Question 12

An ML engineer trains a scikit-learn model on a Spark DataFrame in a Databricks notebook using MLflow autologging. The run logs parameters and metrics, but the engineer later opens the MLflow run and cannot find any input dataset lineage. Which action should the engineer take to ensure the training dataset is recorded with the run in the MLflow UI?

Accepted Answer

Wrap the training data with mlflow.data.from_spark() and pass it to mlflow.log_input().. MLflow records input dataset lineage only when a Dataset object is created and logged to the active run. The mlflow.data.from_spark() constructor builds that object from a Spark DataFrame, and mlflow.log_input() attaches it, after which the run UI shows dataset source, digest, and schema. Autologging captures params, metrics, and the model, but not input data lineage, so an explicit call is required.

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Set the MLFLOW_TRACKING_URI environment variable to the Unity Catalog metastore and rerun training.

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Call mlflow.log_artifact() on the Spark DataFrame object directly after training.

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Enable Delta table time travel on the source table and reference the table path in the run tags.

Question 13

A machine learning engineer is using MLflow to log a model built with XGBoost. They want to ensure that the model's input schema is captured for validation during deployment. Which MLflow feature should they use?

Accepted Answer

Model signature. The model signature is the MLflow feature that captures the input and output schema of a model. It is logged alongside the model and is used by deployment tools to validate incoming data. For XGBoost, you can infer the signature from training data or define it manually. This ensures that the model receives data in the expected format, reducing runtime errors.

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Model version

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Run ID

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Input example

Question 14

A machine learning engineer is using MLflow to track experiments. They call mlflow.start_run() and then log a model with mlflow.sklearn.log_model(). After the run completes, they notice that the model artifact is stored in the run's artifact location, but the run's source version and git commit are not captured. They are running from a Databricks notebook with Git integration enabled. Which action will ensure that the Git commit hash and source version are automatically logged to the MLflow run?

Accepted Answer

Ensure that the notebook is attached to a Git repository and that the Git integration is properly configured so MLflow can detect the commit hash.. MLflow automatically logs Git metadata when the code runs from a Git repository. In Databricks, if the notebook is in a Git folder with integration enabled, MLflow detects the repository and records the commit hash and source version in the run. This is the standard behavior; manual environment variables or tags are unnecessary and not the automatic mechanism.

Answer

Set the environment variable MLFLOW_GIT_COMMIT to the current commit hash before starting the run.

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Call mlflow.set_tag('mlflow.source.git.commit', commit_hash) after starting the run.

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Use mlflow.log_artifact() to upload the .git directory to the run's artifacts.

Question 15

An ML engineer is using Databricks Jobs to orchestrate a pipeline that includes a notebook for feature engineering and a notebook for model training. The training notebook must run only after the feature engineering notebook completes successfully, and both must run on a schedule. Which configuration in Databricks Jobs achieves this dependency?

Accepted Answer

Create a multi-task job where the training task has a depends_on condition referencing the feature engineering task.. Databricks Jobs multi-task workflows allow defining tasks with dependencies using depends_on. This ensures the training task starts only after the feature engineering task succeeds, and the entire job can be scheduled. Separate jobs with triggers, %run, or time-based scheduling do not provide the same reliable dependency semantics.

Answer

Use a single notebook that calls the feature engineering notebook and then the training notebook using %run.

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Set the training notebook to run on a schedule that starts after the expected completion time of the feature engineering notebook.

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Create two separate jobs and configure the training job to be triggered by the completion of the feature engineering job using a job run trigger.