Databricks-ML-Assoc ML Workflows Practice Test 7 — 15 Questions

Question 1

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 2

A data scientist is using MLflow Tracking to log experiments. They want to compare multiple runs of a scikit-learn model and identify the run with the lowest RMSE. Which MLflow feature should they use?

Accepted Answer

The MLflow Tracking UI, which allows sorting and filtering runs by metrics.. The MLflow Tracking UI is designed for visualizing and comparing runs within an experiment. It allows sorting by metrics such as RMSE, filtering, and generating charts. Projects, Model Registry, and Recipes serve different purposes: packaging, lifecycle management, and automation, respectively. Therefore, the Tracking UI is the correct tool for this comparison.

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The MLflow Projects feature, which packages code for reproducible runs.

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The MLflow Model Registry, which stores model versions and their stages.

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The MLflow Recipes (formerly Pipelines) framework, which automates model training.

Question 3

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 4

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.

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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 5

A data scientist is using Databricks AutoML to train a classification model on a dataset with a binary target. They want to understand which features contributed most to the model's predictions and need a human-readable summary. Which AutoML output should they examine?

Accepted Answer

The AutoML-generated notebook that includes a section on model interpretability with SHAP values.. AutoML generates a notebook for each trial that includes model interpretability using SHAP. This notebook contains summary plots and feature importance rankings that are human-readable and explain the model's predictions. Examining this notebook is the correct way to understand feature contributions.

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The MLflow run's artifact directory, specifically the 'feature_importance.json' file.

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The Databricks Feature Store UI, which shows feature lineage and importance scores.

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The Model Registry's version description, which is automatically populated with feature importance by AutoML.

Question 6

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 7

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.

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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.

Question 8

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.

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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.

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

Question 9

An ML engineer is configuring a Databricks Job to retrain a model daily. The job must run a notebook that reads from a feature table, trains a model, and registers it to the Model Registry. The engineer wants to ensure that the job fails immediately if the model's accuracy drops below a threshold. Which approach should they use?

Accepted Answer

Add a task that runs a Python script to query the MLflow API and raise an exception if accuracy is below threshold.. To make the job fail when accuracy is below a threshold, the engineer should add a task that evaluates the metric and raises an exception. This task can run after training and before registration, ensuring the job stops and no subpar model is registered. This is a common pattern for implementing quality gates in ML pipelines.

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Configure the job to send an email alert if accuracy is below threshold, but allow the job to succeed.

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Use Databricks Model Serving to monitor the model's accuracy and automatically roll back if it drops.

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Set a timeout on the training task so that it fails if accuracy is not reached within a time limit.

Question 10

A machine learning team is using Databricks Feature Store to serve features for a real-time model. They have a feature table that is updated daily with new data. To ensure the online store always has the latest feature values for low-latency inference, which approach should they take?

Accepted Answer

Publish the feature table to an online store using the Databricks Feature Store UI or API, and schedule daily updates.. To serve features in real time with low latency, the feature table must be published to an online store, which is a key-value store optimized for fast lookups. Scheduling daily updates ensures the online store reflects the latest data. Other approaches like querying the offline table or using materialized views introduce latency and are not designed for online serving. Thus, publishing and scheduling updates is the correct strategy.

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Use the Feature Store's automatic online store synchronization by enabling a Delta Live Tables pipeline.

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Configure the model to query the offline feature table directly during inference, caching results in memory.

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Create a materialized view of the feature table in Databricks SQL and use that for online serving.

Question 11

A machine learning team is using Databricks Jobs to orchestrate a multi-step ML pipeline: data ingestion, feature engineering, model training, and batch inference. They need to ensure that if the model training step fails, the batch inference step does not run, and that the entire pipeline can be retried from the failed step. Which Databricks Jobs feature should they use to achieve this?

Accepted Answer

Configure each task with a dependency on the previous task and set the job to repair runs on failure.. Databricks Jobs allows defining tasks with dependencies, so the inference task will only run if the training task succeeds. The repair run feature enables retrying a failed run from the failed task, reusing successful task results. This combination provides both conditional execution and efficient recovery.

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Use a single notebook with all steps and rely on the notebook's built-in error handling to skip inference on failure.

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Set up a continuous job that always runs all tasks, and use conditional logic in the inference task to check if training succeeded.

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Use Databricks Jobs with a cluster that has autoscaling enabled and set the inference task to run only if the training task logs a success metric.

Question 12

A team registers a model in Unity Catalog as main.ml.churn_model and wants production scoring jobs to always load the newest approved version without editing job code when a new version is promoted. The team uses the MLflow Python client inside a Databricks job. Which model URI should the scoring code use?

Accepted Answer

models:/main.ml.churn_model@champion. Unity Catalog registered models support aliases such as champion, which act as movable pointers to a version. Referencing the model with models:/catalog.schema.model@alias lets consumers load whatever version currently holds the alias, so promotion is a metadata operation rather than a code change. Numeric versions and run IDs are immutable, and stage-based URIs belong to the legacy workspace registry rather than Unity Catalog.

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runs:/<run_id>/model

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models:/main.ml.churn_model/Production

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models:/main.ml.churn_model/3

Question 13

A data scientist is comparing multiple hyperparameter configurations for a model and wants to view the resulting metrics side by side in a single interface, sort runs by accuracy, and drill into individual run details. Which MLflow component provides this capability?

Accepted Answer

MLflow Tracking UI, which lists runs within an experiment and supports sorting and filtering by metrics.. The MLflow Tracking UI is designed for experiment comparison. It presents runs in a sortable table with metrics and parameters, supports filtering, and allows drilling into individual runs. The Model Registry UI, Projects, and Jobs run history serve different purposes and do not offer the same run comparison and metric sorting features.

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MLflow Model Registry UI, which shows model versions and their stage transitions.

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MLflow Projects dashboard, which lists available projects and their entry points.

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Databricks Jobs run history, which shows task durations and statuses for scheduled workflows.

Question 14

A data scientist is tuning a scikit-learn random forest with Hyperopt in a Databricks notebook. Each trial trains on a 40 GB Delta table, and the scientist notices that every trial re-reads the full table from cloud storage, making the search slow. Which change best accelerates the hyperparameter search while preserving correctness?

Accepted Answer

Load the Delta table into a Spark DataFrame once, cache it, and have each trial convert the cached data to pandas.. Hyperopt runs many independent trials, and when each trial re-reads a large Delta table, storage I/O dominates runtime. Caching the Spark DataFrame once outside the objective function materializes the data so trials reuse it, while converting to pandas inside each trial keeps scikit-learn training identical. This preserves data and model semantics and removes the repeated read that made the search slow.

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Switch the search algorithm from the default TPE to Random Search to reduce per-trial overhead.

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Enable adaptive query execution and rely on Spark to skip re-reading unchanged files automatically.

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Increase max_evals so more trials run and the overhead is amortized.

Question 15

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.