Databricks-ML-Assoc ML Workflows Practice Test 6 — 15 Questions

Question 1

A data scientist is building a training pipeline where raw event data lands in a Delta table. They need to transform the data, train a model, and register it to the Databricks Model Registry. The pipeline must run daily on a schedule and send an email alert if training fails. Which Databricks construct should they use to orchestrate the entire workflow?

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

Accepted Answer

A Databricks Job with multiple tasks that include a notebook task for data transformation, a notebook task for training, and a task that registers the model, with email notifications configured on the Job.. Databricks Jobs with multiple tasks are the native way to orchestrate multi-step ML workflows. You can define task dependencies so transformation runs before training, and training before registration. Job-level notifications send email alerts on failure, and the built-in scheduler handles the daily cadence. This satisfies all requirements without external tooling.

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An MLflow Project that defines the transformation, training, and registration steps in its MLproject file and is run with mlflow run on a schedule.

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A Delta Live Tables pipeline that performs transformations and automatically trains and registers the model as part of the pipeline.

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A Databricks SQL dashboard that queries the raw Delta table, calls an external training service via a webhook, and emails the results.

Question 2

A data science team is using Databricks Jobs to orchestrate a machine learning pipeline. The pipeline includes a task that trains a model and a subsequent task that evaluates the model. The evaluation task must access the model version produced by the training task. Which mechanism should the team use to pass the model version between tasks?

Accepted Answer

Use task values to pass the model version as a string between tasks.. Task values are the native Databricks Jobs feature for passing small data between tasks. The training task can set a task value with the model version, and the evaluation task retrieves it using dbutils.jobs.taskValues.get(). This avoids ambiguity and external storage, ensuring the correct version is used.

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Write the model version to a Delta table and have the evaluation task read it.

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Store the model version in a file in DBFS and have the evaluation task read it.

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Use MLflow's tracking API to query the latest model version in the evaluation task.

Question 3

A data scientist needs to run the same feature-engineering notebook against three different parameter sets in a Databricks Job. She wants each parameter set to execute independently and in parallel, with separate logs. Which Job feature should she use?

Accepted Answer

A for-each task that iterates over a parameter array and runs the notebook once per element.. The for-each task is designed for iterating a nested task over an input array, producing one task run per element with its own logs and status. That gives the independent, parallel executions the scientist needs while keeping everything in one workflow. Passing a JSON string, creating separate jobs, or relying on autoscaling all fail to produce per-parameter task runs with isolated logs.

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A job cluster with autoscaling enabled and a single task.

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A single notebook task with the three parameter sets passed as a JSON string argument.

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Three separate jobs created from the same notebook, each with its own parameter.

Question 4

An ML engineer is setting up a Databricks Job to retrain a production model nightly. The job must run only when upstream data validation succeeds, notify the team on failure, and avoid retraining when the input data has not changed. Which TWO capabilities of Databricks Jobs directly support these requirements? (Choose two.)

Accepted Answer

Job-level and task-level notifications that alert on failure or duration thresholds. Condition tasks provide the branching needed to run training only when validation passes and data has changed, while task and job notifications deliver the failure alerts. Together they implement the conditional retraining and notification requirements, whereas autoscaling, repair runs, and Photon address performance or recovery rather than control flow and alerting.

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Cluster autoscaling to add workers when the training dataset grows

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Job compute configured with photon acceleration for the training tasks

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Repair runs to rerun only failed tasks after a partial failure

Question 5

A data scientist wants to track the progress of a training script that runs for several hours on a Databricks cluster. The script uses MLflow and needs to record metrics such as loss and accuracy at the end of each epoch so they can be visualized in real time. Which MLflow API call should be used inside the training loop?

Accepted Answer

mlflow.log_metric. To record metrics like loss and accuracy at each epoch for real-time visualization, the training loop should call mlflow.log_metric. This API accepts a metric name, a numeric value, and an optional step, and MLflow stores each call as a data point. The MLflow UI plots these points as a time-series chart, allowing progress monitoring during long training runs.

Answer

mlflow.log_param

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mlflow.set_tag

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mlflow.log_artifact

Question 6

An ML engineer is configuring a Databricks Job to automate nightly retraining of a model. The job must (1) run only after the upstream feature engineering job succeeds, and (2) notify the team via email if the training task fails. Which TWO configurations satisfy these requirements? (Choose two.)

Accepted Answer

Configure the training task with a depends_on entry referencing the feature engineering task, and add an email notification for the job's failure event.. Databricks Jobs provide first-class task dependencies via depends_on, which guarantees a downstream task runs only after its upstream task succeeds, and job-level notifications that can email the team on failure. These native features satisfy both the ordering and alerting requirements without custom code or fragile timing assumptions.

Answer

Use a schedule with a cron expression that triggers the training task five minutes after the feature engineering job is expected to finish, and enable email alerts on failure.

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Chain the notebooks by calling dbutils.notebook.run from the training notebook to invoke the feature engineering notebook, and rely on the notebook's exception handling to email the team.

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Set up an MLflow webhook that triggers the training notebook whenever a new model version is registered, and configure the webhook to send email on failure.

Question 7

An ML engineer registers a model in Unity Catalog and needs a stable pointer that always resolves to the version currently approved for production, without modifying calling code each time a new version is promoted. Which Model Registry feature should the engineer configure to provide this stable reference?

Accepted Answer

A model version alias such as 'champion' assigned to the approved version. Model version aliases provide exactly the stable, reassignable pointer described: consumers load by alias, and promotion is performed by moving the alias to the newly approved version. Tags, descriptions, and permission grants are metadata or access controls and cannot redirect consumers to a chosen version at load time.

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A model version tag recording the approval date

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A registered model description summarizing the production candidate

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A workspace-level permission grant on the registered model

Question 8

An ML engineer wants to package a training project so it can be run reproducibly from a Databricks Job across environments, with its Python dependencies and entry point defined. Which MLflow capability should be used?

Accepted Answer

MLflow Projects, using an MLproject file to declare the entry point and environment.. MLflow Projects are the packaging mechanism for reproducible runs: the MLproject file specifies the entry point and the environment via a conda or pip specification, and executing the project records source and parameters in a run. Models capture artifacts, Tracking records results, and Recipes impose a specific template, so none of those define a reusable training entry point with dependencies for a Databricks Job.

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MLflow Tracking, by logging parameters and metrics for each run.

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MLflow Recipes, by defining a step-based pipeline with profiles.

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MLflow Models, by saving the estimator with a signature.

Question 9

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?

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 10

You are using Databricks Feature Store to create a feature table that will be used for both batch training and online inference. The feature table must be refreshed daily with new data, and the online store must serve the latest feature values within minutes of the refresh. Which configuration should you use?

Accepted Answer

Create a feature table with online=True and use a Databricks Job to run a daily write that updates both the offline Delta table and the online store.. A feature table created with online=True is backed by both an offline Delta table and an online store. Writing to it via FeatureStoreClient.write_table publishes the new feature values to both stores. Running this write in a daily Databricks Job refreshes the online store with the latest data, enabling low-latency serving within minutes. Other options either omit the online store, use unsupported manual exports, or fail to meet freshness requirements.

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Create a feature table with online=False, and configure Model Serving to read from the offline table at inference time.

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Create a standard Delta table and manually export it to a key-value store each day using a notebook.

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Create a feature table with online=True, but only write to it weekly to reduce costs.

Question 11

A data scientist is using MLflow Tracking in Databricks to compare multiple runs of a hyperparameter tuning experiment. They want to quickly identify the run with the lowest validation loss and then register that model version in the MLflow Model Registry. Which MLflow UI feature allows sorting runs by a specific metric to find the best run?

Accepted Answer

The runs table, where you can click on the metric column header to sort runs ascending or descending.. The runs table in the MLflow UI provides a sortable list of all runs, with columns for parameters and metrics. Clicking a metric column header sorts runs by that metric, making it easy to spot the run with the lowest validation loss. This is the standard workflow for selecting the best run to register.

Answer

The experiment notes field, where you can manually record the best run ID.

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The model registry page, which automatically sorts models by their latest version's metrics.

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The run comparison view, which displays all runs side-by-side with their metrics.

Question 12

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 13

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 14

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 15

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.