Databricks-ML-Assoc Practice Test 15 — 15 Questions

Question 1

A machine learning engineer is using MLflow to log a model trained with XGBoost. They want to ensure that the model can be loaded and used for inference in a different environment without requiring the original training environment. Which MLflow feature allows the model to capture its dependencies and environment?

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

Accepted Answer

MLmodel file with conda_env and requirements.txt. The MLmodel file, along with conda_env and requirements.txt, captures the dependencies required to load and run the model. When logging a model, MLflow automatically generates these files based on the current environment. This enables the model to be deployed in a different environment by recreating the necessary dependencies, ensuring consistent behavior.

Answer

Run ID

Answer

Model signature

Answer

Model version

Question 2

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.

Answer

runs:/<run_id>/model

Answer

models:/main.ml.churn_model/Production

Answer

models:/main.ml.churn_model/3

Question 3

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.

Answer

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

Answer

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

Answer

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

Question 4

A data scientist is training a linear regression model using scikit-learn on Databricks. They want to track the model's hyperparameters, such as fit_intercept and normalize, in MLflow. Which MLflow API call should they use to log these hyperparameters?

Accepted Answer

mlflow.log_param. To track hyperparameters in MLflow, the appropriate API is mlflow.log_param, which records key-value pairs that define the model's configuration. This allows the parameters to be visible in the MLflow UI and searchable via the API. Logging metrics, artifacts, or tags would not correctly capture hyperparameters and would hinder experiment comparison.

Answer

mlflow.log_artifact

Answer

mlflow.log_metric

Answer

mlflow.set_tag

Question 5

A machine learning engineer is using MLflow to log a model trained with XGBoost. They want to ensure that the model can be loaded and used for inference in a different environment without requiring the original training environment. Which MLflow feature allows the model to capture its dependencies and environment?

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

Accepted Answer

Model signature. The model signature is the MLflow component that explicitly defines the input and output schema of a logged model. Without it, MLflow does not store metadata about column names, data types, or tensor shapes, which complicates downstream validation and deployment. Logging a signature ensures that consumers of the model understand its expected interface, reducing integration errors.

Answer

Run ID

Answer

Model version

Answer

Input example

Question 6

A machine learning engineer is using MLflow to track experiments in Databricks. They want to record the hyperparameters used for each run so that they can compare runs later. Which MLflow method should they use to log a single hyperparameter?

Accepted Answer

`mlflow.log_param()`. `mlflow.log_param()` is the correct method to record hyperparameters for an MLflow run. It stores key-value pairs that are displayed in the experiment UI and can be used to filter and compare runs, which is essential for hyperparameter tuning.

Answer

`mlflow.log_metric()`

Answer

`mlflow.set_tag()`

Answer

`mlflow.log_artifact()`

Question 7

A data scientist is training a scikit-learn model on a Databricks cluster using MLflow. To enable automatic logging of parameters, metrics, and models, they call mlflow.sklearn.autolog() before fitting the model. After the run completes, they notice that the model artifact is stored in the run's artifact location but is not registered in the MLflow Model Registry. What is the most likely reason for the model not being registered?

Accepted Answer

The autolog() function logs the model but does not register it; registration requires either specifying registered_model_name in autolog() or manually registering the model.. Autologging in MLflow captures parameters, metrics, and models but does not automatically register them in the Model Registry. To register, you must specify a registered model name either when calling autolog or when logging the model. Without that, the model remains only as an artifact in the run. This is a common point of confusion for practitioners expecting end-to-end automation.

Answer

The model registration failed because the cluster does not have the necessary permissions to write to the Model Registry.

Answer

The autolog() function does not log models for scikit-learn; it only logs parameters and metrics.

Answer

The model was not registered because the MLflow run was not associated with a registered model name.

Question 8

A data scientist has registered a scikit-learn model in the Databricks Model Registry as `prod.churn_model`. The production endpoint serving this model must automatically roll back to the previously served version if the newly deployed version's error rate exceeds a threshold within one hour of deployment. Which Databricks feature should the data scientist configure to meet this requirement?

Accepted Answer

Set up a Databricks Lakehouse Monitoring quality monitor on the endpoint's inference table with a custom metric and an alert that triggers a rollback via a job.. Automatic rollback based on runtime error rate is not a built-in Model Serving feature. The viable path is to monitor the endpoint's inference table with Lakehouse Monitoring, define a custom metric for error rate, and use an alert to trigger a job that reconfigures the endpoint to the previous version. This leverages Databricks-native monitoring and orchestration to meet the requirement.

Answer

Use MLflow Model Registry webhooks to trigger a rollback when the endpoint's error rate exceeds the threshold.

Answer

Configure the endpoint to serve the new version with a small percentage of traffic and manually promote it after validating the error rate.

Answer

Enable automatic model version rollback on the serving endpoint by defining a rollback policy with the error-rate threshold.

Question 9

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.

Answer

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

Answer

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

Answer

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

Question 10

A data scientist is training a scikit-learn model on a Databricks cluster with autoscaling enabled. They observe that each epoch takes significantly longer than expected, and the Spark UI shows very low CPU utilization across the worker nodes. The dataset is small enough to fit in memory on a single node. What is the most likely cause of the slow training?

Accepted Answer

The model is being trained on the driver node only, and the worker nodes are idle because the training code does not distribute the workload.. The correct answer is that the model is being trained on the driver node only, leaving worker nodes idle. In Databricks, standard scikit-learn training runs on the driver unless you explicitly distribute it. The low CPU utilization on workers and slow epochs confirm that the workload is not parallelized. To speed up training, you could use Spark ML, Horovod, or parallelize hyperparameter tuning with Hyperopt.

Answer

The cluster is using a runtime version that does not support scikit-learn, forcing a fallback to single-threaded execution.

Answer

The cluster's autoscaling policy is aggressively terminating worker nodes, causing the training to restart.

Answer

The data is stored in DBFS, and reading it for each epoch introduces network latency that throttles training.

Question 11

A machine learning engineer is deploying a scikit-learn model to a Databricks Model Serving endpoint. The model was logged with MLflow using the default signature and input example. After deployment, the engineer notices that the endpoint's REST API expects a JSON payload in a specific format. Which MLflow artifact is used by Model Serving to determine the expected input format for the endpoint?

Accepted Answer

The MLmodel file's signature field, which contains the input schema.. Model Serving relies on the MLflow model's signature, stored in the MLmodel file, to understand the expected input schema. This signature includes column names, data types, and shapes, allowing the endpoint to validate and parse JSON requests correctly. Without a signature, the endpoint may not enforce input validation, leading to errors. The signature is essential for seamless deployment.

Answer

The conda.yaml file, which lists the Python dependencies.

Answer

The requirements.txt file, which pins package versions.

Answer

The input_example.json file, which provides a sample input.

Question 12

A machine learning engineer is using MLflow to log a model trained with XGBoost. They want to ensure that the model can be loaded and used for inference in a different environment without requiring the original training environment. Which MLflow feature allows the model to capture its dependencies and environment?

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

Accepted Answer

The metric was logged, but the MLflow UI runs table does not automatically display all metrics; the engineer must manually add the 'weighted_f1' column to the table.. The MLflow UI runs table does not show all logged metrics by default; it shows a default set. Custom metrics must be explicitly added as columns to be visible and sortable. The metric is likely logged correctly, but the UI configuration hides it. This is a frequent source of confusion when comparing runs with custom evaluation metrics.

Answer

The metric name 'weighted_f1' contains an underscore, which is not allowed in MLflow metric names.

Answer

The metric was not logged because the run was not active; mlflow.log_metric() only works within an active run context.

Answer

The metric was logged with a step value, causing it to be treated as a time-series metric and not displayed in the runs table by default.

Question 13

A data scientist is using MLflow to track experiments. They notice that all runs from a particular notebook are being logged to the default experiment instead of the experiment they intended to use. They have already called mlflow.start_run() without specifying an experiment ID. What is the most likely cause?

Accepted Answer

The active MLflow experiment was not set using mlflow.set_experiment before starting the run.. MLflow logs runs to the active experiment, which is set via mlflow.set_experiment. If not set, runs go to the default experiment (ID 0). The start_run function does not automatically infer the intended experiment from the notebook context. To fix this, the data scientist should call mlflow.set_experiment with the desired experiment name or pass experiment_id to start_run.

Answer

The MLflow tracking server is misconfigured, causing runs to be redirected to the default experiment.

Answer

The MLflow client library version is outdated and does not support custom experiments.

Answer

The run was started with nested=True, which forces logging to the default experiment.

Question 14

A team maintains a Databricks Model Serving endpoint for a fraud model. Compliance requires that every request and response be logged to a Delta table for auditing and later analysis. The endpoint is already configured and serving traffic. What should the team do to capture this data with the least additional infrastructure?

Accepted Answer

Enable inference tables on the endpoint so requests and responses are automatically logged to a Delta table. Inference tables are the native Model Serving mechanism that records request and response payloads into a Delta table, giving complete server-side auditing without extra infrastructure. Client-side logging, dashboard metrics, and event logs each capture only partial or operational data, so they cannot satisfy the requirement to log every request and response for the fraud endpoint.

Answer

Configure the endpoint to emit metrics to a monitoring dashboard and export the dashboard data nightly

Answer

Write a client wrapper that logs each request and response to a Delta table before returning the prediction

Answer

Attach an event log delivery to the serving endpoint and parse the logs into a Delta table with a scheduled job

Question 15

A data scientist is developing a model on Databricks and wants to use MLflow to track experiments. They create a new experiment using mlflow.create_experiment('my_experiment') and then run mlflow.start_run(). However, when they log parameters and metrics, they notice that the run is not associated with 'my_experiment' but with the default experiment. What is the most likely reason?

Accepted Answer

The mlflow.create_experiment() function only creates the experiment but does not set it as the active experiment; they must call mlflow.set_experiment() to use it.. Creating an experiment with mlflow.create_experiment() does not automatically set it as the active experiment for subsequent runs. You must explicitly call mlflow.set_experiment() to designate the experiment for logging. Otherwise, runs default to the notebook's experiment or the default experiment. This separation allows flexible experiment management but requires an extra step.

Answer

The experiment was created in a different workspace, so the run cannot be associated with it.

Answer

The mlflow.start_run() function requires an experiment_id argument to associate the run with a specific experiment; without it, the run goes to the default experiment.

Answer

The experiment name 'my_experiment' is invalid because it contains an underscore; MLflow experiment names must be alphanumeric.