Databricks-ML-Pro Practice Test 2 — 30 Questions

Question 1

Refer to the exhibit. Your automated CI/CD pipeline triggered a model deployment to production, but the job failed with the error shown. What is the most likely cause?

Accepted Answer

The deployment job is referencing a model version that hasn't been created yet.. The error indicates that the deployment pipeline is attempting to access a model version that does not exist in the Model Registry. This usually happens when the pipeline triggers before the registration process completes or when a race condition occurs between the training job and the deployment job. Validating the existence of the model version before initiating deployment is essential for pipeline reliability.

Answer

The model was registered, but the cluster lacks permissions to read it.

Answer

The model serving endpoint is already occupied by another model version.

Answer

The training cluster ran out of memory during the model artifact upload.

Question 2

What is the primary purpose of registering a model in the MLflow Model Registry?

Accepted Answer

To provide a structured workflow for versioning, stage management, and model governance.. The MLflow Model Registry provides a centralized hub for managing the model lifecycle, including versioning, stage transitions (e.g., Staging to Production), and lineage tracking. This is foundational for MLOps because it provides a single source of truth for all stakeholders, enabling controlled deployments, auditability, and the ability to easily revert to previous model versions if performance regressions are detected in production.

Answer

To increase the training speed of the machine learning model.

Answer

To automatically retrain the model when data drift is detected.

Answer

To convert Python code into high-performance C++ code.

Question 3

A fraud detection model is served on a Databricks Model Serving endpoint. You notice that predictions for the same input vector differ between two consecutive requests within seconds, and there is no feature store or external cache involved. The model was logged with a fixed random seed and deterministic inference code. Which action should you take first to diagnose the inconsistency?

Accepted Answer

Inspect the endpoint's request logs to confirm the payloads are identical and check whether the model version serving traffic changed between requests.. When a deterministic model returns different outputs for the same input, the fastest path to the cause is to verify the two requests were truly identical and to confirm which model version handled each. A version transition during a rollout, or a payload difference such as field ordering or missing values, explains the symptom without any model retraining. Retraining, scaling, or changing flavors are premature and do not produce diagnostic evidence.

Answer

Convert the model to a different flavor such as ONNX and re-register it, because the original flavor cannot guarantee deterministic inference.

Answer

Increase the endpoint's concurrency and enable autoscaling, since differing predictions indicate the replicas are running different code.

Answer

Retrain the model with a different random seed and redeploy, because seed instability is the likely cause of varying predictions.

Question 4

When auditing an ML pipeline in Databricks for compliance and governance, which THREE of the following should be verified?

Accepted Answer

The Git commit hash associated with the specific training run.. Auditing requires proof of data lineage, code versioning, and access control. Verifying these components ensures that the organization can prove how a model was built, what data was used, who approved it, and that the code was properly reviewed. This level of traceability is non-negotiable for regulated industries and essential for enterprise-grade MLOps maturity.

Answer

The personal credentials of the data scientist who manually ran the training.

Answer

The raw text of every email sent between the data scientists.

Question 5

Your organization is implementing an MLOps strategy that requires strict model governance. You need to ensure that no model is deployed to production unless it has been tagged with 'validated=true' in the MLflow Model Registry. How can you enforce this policy within your CI/CD workflow?

Accepted Answer

Add a validation script in the CI/CD pipeline that queries the model version tags.. Implementing a policy-based deployment gate in your CI/CD pipeline ensures that governance requirements are met before code or models hit production. By querying the model's tags using the MLflow client before attempting a deployment, you create a hard stop for non-compliant models. This is a critical security and compliance practice, preventing unauthorized or untested models from being served to end-users or critical business processes.

Answer

Use Databricks workspace permissions to prevent unauthorized users from deploying models.

Answer

Rely on the MLflow UI to visually verify the tag before clicking 'Deploy'.

Answer

Set the default stage of all new models to 'Production' via a global config.

Question 6

Which of the following describes the purpose of a 'Gold' table in the Medallion architecture within an MLOps pipeline?

Accepted Answer

It contains validated, business-level data ready for ML model training.. The Gold layer contains highly processed, business-level data that is ready for consumption by downstream ML models or analytical applications. By ensuring data is clean, aggregated, and validated at this stage, MLOps teams can rely on high-quality features for training, which directly improves model performance and reduces the complexity of the feature engineering step in the training pipeline.

Answer

It stores raw data ingested directly from external sources.

Answer

It serves as a transient staging area for schema evolution.

Answer

It stores model artifacts and hyperparameters for versioning.

Question 7

A data scientist wants to compare multiple runs within the same MLflow experiment to identify the best-performing model based on a custom metric. What is the most efficient way to do this in the MLflow UI?

Accepted Answer

Use the MLflow UI's experiment page to sort runs by the custom metric and compare them side by side.. The MLflow UI experiment page is designed for comparing runs: it displays all runs in a table, supports sorting by any metric, and allows selecting multiple runs for side-by-side comparison of parameters, metrics, and artifacts. Manual recording, CSV export, or custom API scripts are less efficient and unnecessary for this common task.

Answer

Export all runs to a CSV file using the MLflow CLI and then analyze the CSV in a separate tool.

Answer

Query the MLflow tracking server's REST API and write a custom script to parse and rank the runs.

Answer

Open each run individually and manually record the custom metric values in a spreadsheet.

Question 8

A team is using Databricks Feature Store to manage features for a real-time model served via Databricks Model Serving. They need to ensure that the online feature values used at inference time are consistent with the training data. Which TWO practices should they implement? (Choose two.)

Accepted Answer

Use point-in-time lookups when creating the training dataset to avoid leaking future data.. Consistency between online and offline features is achieved by using the same pipeline to publish to both stores and by using point-in-time lookups during training. These practices ensure that the transformations and temporal alignment match between training and inference. Manual copying or direct Delta queries do not provide the required consistency or performance.

Answer

Log the model with the feature store's training set specification to enable automatic feature lookup at serving time.

Answer

Store all features in a single Delta table and query it directly from the model serving endpoint.

Answer

Manually copy feature values from the offline store to the online store on a daily basis using a notebook.

Question 9

A data scientist is training a machine learning model on Databricks using MLflow. They need to track hyperparameter tuning experiments while ensuring that each iteration is uniquely identifiable and reproducible. Which feature should they use to group related runs within a single experiment?

Accepted Answer

MLflow Nested Runs via the 'parent_run_id' parameter.. MLflow nested runs are the industry-standard approach for hierarchical organization of hyperparameter tuning tasks. By creating a parent run for the overall optimization process and child runs for individual parameter sets, data scientists gain clear visibility into model performance metrics across the entire search space, simplifying comparative analysis and facilitating efficient model selection during the development lifecycle.

Answer

MLflow Tags to append metadata for each run iteration.

Answer

MLflow Experiment IDs for every parameter variation.

Answer

MLflow Model Registry versions to track iterative progress.

Question 10

Which component of Databricks ML is best suited for tracking model hyperparameters, metrics, and code versions during the experimentation phase of the ML lifecycle?

Accepted Answer

MLflow Tracking. MLflow Tracking is the dedicated component designed to record every experiment's parameters, metrics, and code state. By capturing these elements, data scientists can compare different iterations of a model to identify the best-performing configuration. This visibility is vital in MLOps, as it creates an audit trail of how a final model was derived, supporting reproducibility and informed decision-making before promoting a model to the registry.

Answer

Databricks SQL

Answer

Delta Lake

Answer

Unity Catalog

Question 11

When logging a model to the MLflow Model Registry, what is the primary benefit of using a registered model name rather than just the model URI?

Accepted Answer

It enables seamless staging and production management without code changes.. Registered model names provide a level of indirection that allows you to manage model lifecycle stages such as 'Staging', 'Production', and 'Archived'. By referencing a model name, you can update the underlying model version without requiring code changes in your inference applications. This promotes operational stability, as teams can transition models through an automated CI/CD pipeline while maintaining consistent endpoints for downstream consumers of the model.

Answer

It automatically increases the model training speed during the next iteration.

Answer

It forces the model to be saved in a specific proprietary cloud format.

Answer

It ensures that the model is automatically encrypted using hardware security modules.

Question 12

Refer to the exhibit. An administrator notices that the cost for this specific endpoint is higher than expected even when there is no traffic. Based on the exhibit, what is the most likely cause of the high idle cost?

Accepted Answer

The 'scale_to_zero_enabled' parameter is set to false, keeping an instance active at all times.. The configuration shows that 'scale_to_zero_enabled' is set to false. This means that at least one instance of the model is running 24/7, regardless of whether any requests are being made. While this ensures there is never a cold start, it leads to continuous billing for the compute resources even during nights and weekends.

Answer

The 'entity_version' is set to a legacy version that uses more expensive hardware.

Answer

The endpoint is using a GPU-accelerated instance by default for all Unity Catalog models.

Answer

Multiple versions of the model are being served simultaneously, doubling the cost.

Question 13

Which THREE of the following are primary responsibilities of an MLOps engineer when maintaining production ML models in Databricks?

Accepted Answer

Monitoring model performance and detecting data drift.. An MLOps engineer's role is to ensure stability, performance, and compliance. This includes monitoring model performance to detect drift, managing the CI/CD pipeline for automated deployments, and ensuring security via robust access controls. By balancing these tasks, they ensure that the machine learning system remains reliable and valuable to the business over time, effectively bridging the gap between development and operations.

Answer

Writing the core machine learning research papers for the organization.

Answer

Manually retraining every model daily regardless of performance metrics.

Question 14

Refer to the exhibit. If the 'train_model' task fails, what happens to the 'evaluate_model' task in this Databricks Job?

Accepted Answer

It is skipped because the dependency is not met.. In Databricks Jobs, a task that depends on another will only execute if the parent task finishes successfully. If 'train_model' fails, the dependency condition for 'evaluate_model' is not met, causing the job to stop or skip the downstream task. This behavior is intentional to prevent evaluating a non-existent or corrupted model, ensuring pipeline integrity and avoiding wasted computational resources on dependent tasks.

Answer

It executes regardless of the failure.

Answer

It attempts to run using the last successful model artifact.

Answer

It retries the 'train_model' task automatically until success.

Question 15

A data scientist wants to record the exact library dependencies and a code snapshot alongside a model so that a reviewer can later restore the same environment and reproduce the training run. They are logging with MLflow on Databricks. Which practice best satisfies this requirement?

Accepted Answer

Log the model with `mlflow.sklearn.log_model` and pass pinned `pip_requirements` plus attach the source notebook as an artifact.. Reproducibility requires capturing two things: the environment and the code. Pinning `pip_requirements` when logging the model embeds a version-exact dependency manifest that MLflow restoration consumes. Attaching the source notebook records the training logic as it existed at that moment. Storing artifacts elsewhere or relying on autolog alone leaves one of these gaps, so the run cannot be faithfully recreated later.

Answer

Call `mlflow.log_artifact` on a generated `requirements.txt` and let autolog capture parameters only.

Answer

Log the model with `mlflow.sklearn.log_model` and rely on the cluster's installed libraries at load time.

Answer

Use `mlflow.autolog()` and set the experiment's artifact location to a Unity Catalog volume.

Question 16

An ML engineer is deploying a scikit-learn model to a Databricks Model Serving endpoint. The model expects a single feature vector of 10 float values per request. The endpoint must return predictions in under 100 ms. Which approach should the engineer use to minimize per-request overhead?

Accepted Answer

Log the model using the native scikit-learn flavor and deploy it with a workload size that provides sufficient CPU.. For low-latency single-vector inference, minimizing framework overhead is critical. The native scikit-learn flavor avoids the extra Python layer that pyfunc introduces, and a suitable workload size provides the CPU needed to execute the model quickly. Autoscaling and request batching address throughput rather than per-request latency, so they do not help meet the strict latency target.

Answer

Log the model with a signature that specifies a tensor input and enable the endpoint's request batching.

Answer

Enable autoscaling on the endpoint and send requests with a batch size of 1.

Answer

Use MLflow's pyfunc flavor with a custom predict method that processes one row at a time.

Question 17

A data scientist is developing a model on Databricks and wants to use MLflow to compare multiple runs. They need to quickly identify the run with the lowest validation loss. Which MLflow UI feature allows them to sort and filter runs based on metrics?

Accepted Answer

The runs table in the MLflow experiment UI, where you can sort by metrics and apply filters.. The MLflow experiment UI includes a runs table that lists all runs with their metrics. You can sort by any metric, such as validation loss, to find the lowest value. Filters can also be applied to focus on specific runs. This is the standard way to compare runs visually.

Answer

The artifacts tab of a run, where you can view logged metric files and manually sort them.

Answer

The notebook's output cell, where you can print a DataFrame of runs and sort it.

Answer

The model registry page, where you can compare model versions by their metrics.

Question 18

Your ML pipeline requires a complex environment with specific C++ dependencies. What is the recommended way to manage this in Databricks?

Accepted Answer

Use Databricks Container Services with a custom image.. Databricks Container Services allows users to define custom Docker images as the execution environment. This is the only way to handle complex system-level dependencies like C++ libraries that cannot be installed via standard pip or conda commands. By using custom containers, teams ensure that the training and inference environments are identical and include all necessary system-level components.

Answer

Install dependencies in a setup script at runtime.

Answer

Include dependencies in the MLflow model artifact.

Answer

Create a shared library JAR file.

Question 19

Refer to the exhibit. A user wants to retrieve the 'accuracy' metric from this run programmatically. Which code snippet correctly accesses this value?

Accepted Answer

client = mlflow.tracking.MlflowClient(); client.get_run('550e8400-e29b-41d4-a716-446655440000').data.metrics['accuracy']. The MLflow tracking client provides the `get_run` method, which returns an object containing all run metadata, including metrics. Accessing the dictionary via `data.metrics` is the standard way to retrieve tracked values. This is critical for automated model comparison scripts, where developers must programmatically evaluate multiple runs to select the best-performing iteration for registration in the Model Registry.

Answer

mlflow.get_metric('accuracy', run_id='550e8400-e29b-41d4-a716-446655440000')

Answer

mlflow.search_runs(run_ids=['550e8400-e29b-41d4-a716-446655440000'])['accuracy']

Answer

mlflow.load_metric('accuracy', '550e8400-e29b-41d4-a716-446655440000')

Question 20

A financial institution deploys a credit scoring model using Databricks Model Serving. The model must log all incoming requests and outgoing responses to a Delta table for auditing. The ML engineer needs to enable this logging with minimal performance impact. Which solution should they implement?

Accepted Answer

Enable inference logging on the serving endpoint, specifying a Delta table location for logs.. Databricks Model Serving includes a built-in inference logging feature that asynchronously logs request and response data to a Delta table. This is the most efficient and least intrusive method. Custom wrappers or external systems add latency and complexity. Access logs do not contain payloads. Therefore, enabling inference logging directly is the correct solution.

Answer

Implement a custom logging wrapper in the model's predict method that writes each request to a Delta table.

Answer

Use a Databricks job to periodically query the endpoint's access logs and write them to a Delta table.

Answer

Configure the model to send logs to an external Kafka topic, then use a Databricks job to ingest from Kafka into Delta Lake.