Databricks-ML-Pro Practice Test 2 — 25 Questions

Question 1

Refer to the exhibit. A machine learning team has updated their model serving endpoint configuration as shown in the JSON. Which deployment strategy is being implemented, and what is the primary risk associated with this specific configuration?

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

Accepted Answer

Canary deployment; the primary risk is increased 'cold start' latency for the v2 model due to low traffic volume.. The exhibit demonstrates a Canary deployment where a small fraction of traffic (10%) is routed to a new model version (v2) while the majority remains on the stable version (v1). This allows for real-world testing with minimal impact. However, since both versions are set to scale-to-zero, the 10% traffic might not be frequent enough to keep v2 warm, leading to high latency for those users.

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Blue/Green deployment; the primary risk is the high cost of running two identical clusters simultaneously.

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A/B testing; the primary risk is that the workload size 'Small' is insufficient for 90% of the traffic.

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Shadow deployment; the primary risk is that v2 will interfere with the predictions returned by v1.

Question 2

A machine learning engineer is using MLflow to track experiments on Databricks. They notice that when they run `mlflow.log_artifact` with a local file path inside a notebook, the artifact is stored in the run's artifact location, but when they run the same code in a job cluster, the artifact is missing. The job cluster uses the same MLflow tracking server and experiment. What is the most likely reason for the missing artifact?

Accepted Answer

The local file path used in the job cluster points to a location that is not accessible from the driver, such as a path on the local filesystem of a different node, so the file does not exist when `log_artifact` is called.. In a Databricks job cluster, code may execute on the driver, but local filesystem paths are not shared across nodes. If the artifact file is created on an executor or a different node, the driver cannot access it when `log_artifact` is called. To ensure artifacts are logged, write them to a distributed storage location such as DBFS or a Unity Catalog volume, then log from there.

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The MLflow tracking server rejects artifacts from job clusters because they lack an interactive user session, so the artifact is discarded.

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The artifact location is configured with a short-lived credential that expires before the job cluster completes, causing the artifact upload to fail silently.

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The job cluster does not have the MLflow library installed, so `log_artifact` silently fails without logging an error.

Question 3

Which practice is most effective for managing dependencies to ensure consistent model training and inference results across different Databricks clusters?

Accepted Answer

Include a requirements.txt file with the notebook or project and specify it in the MLflow model logging process.. Using environment management files (e.g., requirements.txt or conda.yaml) ensures that the exact library versions used during development are replicated in the training and serving environments. This consistency is critical for preventing 'works on my machine' issues, where discrepancies in library versions lead to different numerical outputs or runtime crashes during inference, thus maintaining the reliability and reproducibility of the machine learning pipeline.

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Manually install libraries on each cluster node before starting the job.

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Rely on the default Databricks Runtime library set for all projects.

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Use broad version constraints like '>=1.0' in the environment configuration.

Question 4

A machine learning engineer needs to track model experiments and ensure that all training parameters and metrics are captured in a reproducible way. What is the best practice for using MLflow within Databricks notebooks?

Accepted Answer

Use MLflow with 'mlflow.start_run()' and 'mlflow.autolog()' to ensure all runs are tracked automatically.. Using 'mlflow.autolog()' or explicit 'mlflow.log_params()' within a context manager ensures that every run is isolated and documented. This practice allows for easy comparison of different hyperparameter configurations, preventing data loss and providing a clear lineage from the raw data to the final model artifact, which is crucial for compliance and debugging in enterprise ML environments.

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Manually log metrics to a shared Excel file stored on DBFS after the run completes.

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Only log the final evaluation metric at the end of the training loop to minimize storage.

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Print the parameters to the notebook output cell and rely on the notebook's revision history.

Question 5

Your organization requires that all models deployed to production must be signed by a security officer. How can you enforce this requirement within the Databricks MLflow Model Registry?

Accepted Answer

Restrict 'CAN_MANAGE' permissions on the model, and use an automated CI/CD pipeline for promotion.. Enforcing approval workflows is crucial for compliance and risk management in MLOps. By using MLflow's permissions and stages, you can restrict who can promote models to 'Production'. Combining this with programmatic checks or external CI/CD gates ensures that a model cannot be deployed without the necessary authorization, preventing unauthorized or unvetted code from reaching production inference services.

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Delete all models in the Production stage and only re-upload the signed models.

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Grant 'CAN_MANAGE' permissions on the registry to everyone to ensure transparency.

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Set the model version description to 'Signed by Security' after manual inspection.

Question 6

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.

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

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

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

Question 7

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.

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To increase the training speed of the machine learning model.

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To automatically retrain the model when data drift is detected.

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

Question 8

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.

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

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

Question 9

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.

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

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

Question 10

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.

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

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

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

Question 11

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.

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It stores raw data ingested directly from external sources.

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It serves as a transient staging area for schema evolution.

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It stores model artifacts and hyperparameters for versioning.

Question 12

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.

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

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

Question 13

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.

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

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

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

Question 14

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.

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MLflow Tags to append metadata for each run iteration.

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MLflow Experiment IDs for every parameter variation.

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MLflow Model Registry versions to track iterative progress.

Question 15

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

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Delta Lake

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Unity Catalog

Question 16

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.

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

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

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

Question 17

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.

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

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

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

Question 18

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.

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Manually retraining every model daily regardless of performance metrics.

Question 19

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.

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It executes regardless of the failure.

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It attempts to run using the last successful model artifact.

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It retries the 'train_model' task automatically until success.

Question 20

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.

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

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

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