Databricks-ML-Assoc Practice Test 24 — 15 Questions

Question 1

When logging a PyTorch model to the MLflow Model Registry, which component must be explicitly defined to allow the model to be loaded in an environment where the original code structure might not exist?

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

Accepted Answer

The environment dependencies, often captured via a requirements.txt or conda.yaml file.. When saving a model, especially deep learning models like PyTorch, the 'conda_env' or 'pip_requirements' must be explicitly captured. This ensures that the environment (dependencies and versions) is portable. Without defining these, the model might fail to load in other environments due to version mismatches or missing packages, making the model unusable for inference in production or shared staging environments.

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The hardware specification of the training cluster.

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A list of all users who have access to the model.

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The full raw dataset used for training.

Question 2

A team observes that their Databricks Model Serving endpoint occasionally returns HTTP 429 responses during bursty traffic, even though the endpoint shows low average CPU utilization. They want to reduce these throttling errors without over-provisioning capacity. Which action is most appropriate?

Accepted Answer

Increase the endpoint's `max_instances` and tune the autoscaling concurrency target so replicas scale out faster during bursts.. Throttling at low average CPU points to concurrency saturation during short bursts. Allowing the endpoint to scale to more replicas and configuring autoscaling to react to concurrency sooner lets the endpoint absorb spikes before requests are rejected. This addresses the root cause without permanently over-provisioning capacity during steady-state periods.

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Switch the endpoint to a smaller workload size so individual replicas handle requests faster.

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Enable scale-to-zero and rely on autoscaling to handle bursts.

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Increase the endpoint's `min_instances` so that more replicas are always warm.

Question 3

Which workflow step is essential before promoting a model from 'Staging' to 'Production' in the MLflow Model Registry?

Accepted Answer

Validating the model performance on a hold-out test set.. Validation against a held-out test set is a mandatory step before any model promotion. This ensures that the model meets performance requirements and hasn't regressed compared to the currently deployed version. This gated approach, combined with the Model Registry's staging transitions, provides a controlled environment for testing, ensuring that only verified, high-quality models enter the production ecosystem, thus mitigating business risk and maintaining model predictive accuracy in critical applications.

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Deleting the original training dataset to save storage space.

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Manually updating the production database credentials in the script.

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Restarting the cluster to clear the memory of the training environment.

Question 4

Refer to the exhibit. Why is the 'signature' parameter included in the log_model call?

Accepted Answer

To enforce input data schema validation during model inference.. The model signature defines the expected schema for inputs and outputs. Providing a signature allows Databricks to perform runtime validation of data types, ensuring that the model receives data in the format it expects. This prevents runtime errors during inference and allows for automated documentation and improved user experience within the MLflow Model Registry, which is crucial for scalable model deployment in production.

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To encrypt the model artifacts before saving them to DBFS.

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To specify the hyperparameters used to train the model.

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To compress the model into a smaller binary file for faster deployment.

Question 5

A data scientist is training a scikit-learn model in a Databricks notebook and wants to automatically log parameters, metrics, and the model artifact to an MLflow experiment without writing explicit log calls. They have already installed the required libraries. Which approach should they use?

Accepted Answer

Call mlflow.sklearn.autolog() before starting the training run.. Autologging in MLflow, when invoked for a specific library like scikit-learn, automatically records parameters, metrics, and the model artifact for each run. It requires calling the appropriate autolog function before training. This is the intended method in Databricks to reduce boilerplate and ensure consistent tracking. The other options either involve manual steps or non-existent configurations.

Answer

Register the model in the MLflow Model Registry immediately after training, which will retroactively populate the experiment with all training details.

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Use the MLflow UI to create an experiment and then manually log each parameter and metric using mlflow.log_param and mlflow.log_metric.

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Set the Spark configuration spark.databricks.mlflow.autolog to true in the cluster settings.

Question 6

A machine learning engineer is preparing to deploy a registered model to a Databricks Model Serving endpoint. Before creating the endpoint, the engineer wants to confirm the deployment prerequisites are satisfied. Which two conditions are required for a successful endpoint creation? (Choose two.)

Accepted Answer

The model version has a logged MLflow model artifact with a valid model signature. A successful endpoint deployment depends on a loadable MLflow model artifact with a defined signature and a reconstructable dependency environment, because the service must build a container and validate request schemas. Registry stage, attached clusters, and name matching are not technical prerequisites and do not affect whether the endpoint can be created and brought to a ready state.

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The model version is transitioned to the Production stage in the Model Registry

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The endpoint name matches the registered model name exactly

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A dedicated all-purpose cluster is running and attached to the endpoint

Question 7

A team wants to route production traffic to a new model version while keeping risk low. They configure a Databricks Model Serving endpoint with two served entities: `champion` (entity_version 5) and `challenger` (entity_version 6). They want 95% of requests to hit `champion` and 5% to hit `challenger`. Which configuration accomplishes this?

Accepted Answer

Set `traffic_config` with routes assigning `traffic_percentage` 95 to `champion` and 5 to `challenger`.. Databricks Model Serving uses `traffic_config` with a `routes` list, where each route references a served entity by name and assigns an integer `traffic_percentage`. Summing route percentages to 100 achieves the desired 95/5 split. Other configuration fields like workload size and scale-to-zero govern resources and scaling behavior, not request distribution.

Answer

Deploy two separate endpoints and use a client-side load balancer to send 95% of calls to the champion endpoint.

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Set `scale_to_zero_enabled` to true only on `challenger` so it receives proportionally fewer requests.

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Set `workload_size` to `Small` on `champion` and `Large` on `challenger` to bias traffic toward the larger entity.

Question 8

A machine learning engineer registers a model in the Databricks Model Registry and wants to serve it with low-latency online inference. The model's Python dependencies include a custom private library that is not publicly available. Which deployment approach should the engineer use to ensure the private library is available at inference time?

Accepted Answer

Package the private library as a wheel file, include it in the model's conda environment or requirements, and log the model with that dependency so the serving endpoint installs it.. To serve a model with a private dependency, the dependency must be included in the model's environment specification. Packaging the private library as a wheel file and referencing it in the conda environment or requirements ensures the serving endpoint installs it during environment construction, so the model can import it at inference time.

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Deploy the model to a Databricks model serving endpoint and specify the private library as an environment variable in the endpoint configuration.

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Register the model with mlflow.pyfunc.log_model and set the pip_requirements parameter to the name of the private library as published on PyPI.

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Upload the private library to DBFS and reference its path in the model's Python code using dbutils.fs, then rely on the serving endpoint to mount DBFS automatically.

Question 9

Which component in Databricks is used to manage the lineage of machine learning data, ensuring that you can trace a model back to the exact version of the data it was trained on?

Accepted Answer

Unity Catalog. Unity Catalog provides comprehensive lineage and governance for data and AI assets. It tracks how data is transformed and used, linking datasets to the models they trained. This lineage is vital for auditing, compliance, and debugging, as it allows teams to verify that models were trained on the correct, compliant, and current version of the data, maintaining high standards of data integrity in AI workflows.

Answer

MLflow Tracking

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Databricks Jobs

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Delta Lake

Question 10

A data scientist wants to package a training script with its Python dependencies and parameters so that the same code can be rerun on a different Databricks cluster or shared with a colleague and reproduced exactly. Which MLflow component is designed for packaging and reproducing project code in this way?

Accepted Answer

MLflow Projects. MLflow Projects are the packaging mechanism: an MLproject file names entry points, parameters, and the environment, and running the project recreates that environment to execute the code. This makes training scripts portable and reproducible across clusters and users, which is exactly the requirement described.

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MLflow Tracking

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MLflow Models

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MLflow Model Registry

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.

Answer

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

Which THREE of the following are supported methods for serving machine learning models in Databricks?

Accepted Answer

Real-time REST API endpoints. Databricks provides multiple pathways for model serving, catering to different latency and throughput requirements. Real-time serving provides low-latency REST endpoints for interactive applications. Batch inference allows for high-throughput processing on large datasets using Spark. Finally, user-defined functions (UDFs) allow for embedding model logic directly into SQL or DataFrame transformations, which is highly useful for integrating machine learning models within existing data engineering pipelines and analytics workflows.

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Manual model training in a local Excel file

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Streaming model updates to public web hosting

Question 13

You are building a pipeline where a feature table must be updated daily. Which Databricks construct is the most appropriate for orchestrating this periodic feature engineering job?

Accepted Answer

Databricks Jobs. Databricks Workflows (Jobs) are the recommended tool for orchestrating multi-step data processing tasks, including feature engineering pipelines. By scheduling a Job, you can automate the ingestion, transformation, and write operations to the Feature Store. This ensures that features are refreshed consistently, minimizing data drift and enabling reliable model training or batch inference processes that depend on the most recent feature values.

Answer

MLflow Experiments

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Unity Catalog

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MLflow Model Registry

Question 14

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 15

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.

Answer

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.