Databricks-ML-Pro Practice Exam 3 — 20 Questions

Question 1

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 2

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 3

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.

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Writing the core machine learning research papers for the organization.

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Manually retraining every model daily regardless of performance metrics.

Question 4

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 5

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.

Question 6

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.

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

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

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

Question 7

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.

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

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

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

Question 8

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.

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Install dependencies in a setup script at runtime.

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Include dependencies in the MLflow model artifact.

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Create a shared library JAR file.

Question 9

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.

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

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

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

Question 10

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.

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

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

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

Question 11

You are designing a CI/CD pipeline for a machine learning model on Databricks. The pipeline must automatically retrain the model when new data arrives, validate it, and deploy it to a serving endpoint if it passes. Which two components are essential to achieve this? (Choose two.)

Accepted Answer

A Databricks Model Serving endpoint that can be updated with the new model version via the MLflow API.. A Databricks Job provides the orchestration to run training and validation when new data arrives, and a Model Serving endpoint is required to deploy the validated model for real-time predictions. Together, they form the backbone of an automated retraining and deployment pipeline. MLflow Tracking, feature stores, and Repos are valuable but not strictly essential for the pipeline to function.

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MLflow Tracking to log parameters, metrics, and artifacts for each run.

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A Databricks Repos integration to version control notebooks and manage code changes.

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A feature store that automatically ingests new data and updates features in real time.

Question 12

You are auditing a Databricks environment to ensure compliance. Which TWO actions ensure the highest level of model lineage and reproducibility for models registered in MLflow?

Accepted Answer

Include the Git hash in the log_model metadata.. Linking models to specific Git commits and tracking the environment dependencies via Conda or pip requirements files are essential for reproducibility. These steps ensure that when a model is redeployed, the exact code version and environment can be recreated, fulfilling the core MLOps requirement of auditability and consistent model behavior across different computing environments.

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Use random seeds for all ML libraries.

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Store all training data as CSV files in the workspace.

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Manually copy artifacts to a shared folder.

Question 13

A data scientist is using MLflow to track experiments on Databricks. They notice that some runs are missing the model artifact even though they called mlflow.sklearn.log_model(). What is the most likely cause?

Accepted Answer

The model artifact was logged outside of an active MLflow run.. MLflow only logs artifacts when there is an active run. Calling mlflow.sklearn.log_model() outside a run context results in the artifact not being associated with any run, so it appears missing. Other options like size limits or overwrites are less likely. Ensuring the logging call is inside a with mlflow.start_run() block or after mlflow.start_run() will correctly attach the model artifact to the run.

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The model artifact was overwritten by a subsequent run with the same name.

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The model artifact was too large and exceeded the maximum artifact size.

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The model artifact was logged to a different experiment than the one being viewed.

Question 14

You are retiring a real-time model endpoint on Databricks Model Serving. The endpoint has been serving production traffic for six months and you want to archive its request logs for compliance before deleting the endpoint. Which action should you take first?

Accepted Answer

Query the existing inference table that was configured for the endpoint and export its contents before deleting the endpoint.. Model Serving writes inference payloads to a Delta inference table when the feature is enabled, and that table survives endpoint deletion because it is a separate Unity Catalog object. Exporting the table before removing the endpoint preserves the historical request and response records required for compliance, whereas configuration snapshots or model artifacts do not contain traffic data.

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Download the endpoint's model artifact from the MLflow Model Registry and store it as the audit record.

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Use the Databricks REST API to list the endpoint's configuration and save the JSON response as the archive.

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Enable inference table logging on the endpoint and wait for the next inference requests to be persisted.

Question 15

Refer to the exhibit. An engineer is configuring a serving endpoint. Based on the configuration provided, what is the impact of the 'auto_scale' flag?

Accepted Answer

It allows the serving endpoint to adjust worker count based on request load.. The 'auto_scale' flag allows the Databricks Model Serving endpoint to dynamically adjust the number of active workers based on real-time traffic load. This ensures that the system maintains performance during peak request periods while minimizing costs during low-traffic periods. This balance of performance and efficiency is a critical aspect of managing production ML infrastructure in an enterprise Databricks environment.

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It scales the number of model versions deployed in the registry.

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It automatically re-trains the model when request volume drops.

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It forces the endpoint to only use 10 workers permanently.

Question 16

Which of the following is an advantage of using Databricks AutoML compared to building a custom Scikit-Learn training loop?

Accepted Answer

It generates the training notebook for the trial, allowing for further refinement and transparency.. Databricks AutoML significantly accelerates the development lifecycle by automating tedious tasks like data cleaning, feature engineering, and model selection. It provides a baseline of high-performance models while simultaneously producing the source code for the best-performing model. This allows teams to iterate rapidly, then customize the generated code, combining the speed of automation with the flexibility of manual tuning for complex production requirements.

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AutoML models are guaranteed to have higher accuracy than custom models.

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It prevents the need for any feature engineering by automatically creating all required variables.

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It eliminates the need for monitoring the model after deployment.

Question 17

Your team is experiencing 'training-serving skew' where model performance in production is significantly lower than during training. Which approach should you prioritize to mitigate this issue?

Accepted Answer

Use the Databricks Feature Store to unify feature engineering pipelines.. Training-serving skew often occurs because the data transformation logic used during model training differs from the logic applied during real-time inference. By utilizing the Databricks Feature Store, you can centralize your feature engineering code. This ensures that the exact same transformation code and look-up logic are utilized for both offline training and online inference, effectively eliminating the discrepancy between the two environments.

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Increase the amount of historical training data used in the model.

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Switch the model architecture to a deeper neural network.

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Retrain the model every hour to keep it fresh with recent data.

Question 18

Which feature in Databricks allows you to automatically track training code, parameters, metrics, and models during the development phase?

Accepted Answer

MLflow Tracking. MLflow Tracking is the core component of Databricks for experiment management. It provides a structured way to log and visualize the results of machine learning runs. Understanding this component is fundamental to MLOps because it allows teams to compare models, track performance over time, and ensure that every experiment is reproducible, which is essential for professional model development and auditing.

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Databricks Delta Lake

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Databricks Feature Store

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Databricks Workflows

Question 19

An ML engineer is deploying a model to Databricks Model Serving and wants to implement A/B testing between two model versions. The engineer needs to route a percentage of traffic to each version and collect performance metrics. Which feature of Databricks Model Serving should the engineer use?

Accepted Answer

Create a single endpoint with multiple model versions and configure traffic splitting between them.. To perform A/B testing between model versions, the engineer should create a single Model Serving endpoint that serves multiple model versions with traffic splitting. This allows a specified percentage of requests to be routed to each version, and Databricks provides metrics for each version, facilitating performance comparison. This native feature simplifies A/B testing.

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Deploy two separate endpoints and use an external load balancer to distribute traffic between them.

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Enable the 'Canary' deployment option in the endpoint configuration to automatically split traffic.

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Use MLflow's model registry to assign a stage to each model version and route traffic based on the stage.

Question 20

Refer to the exhibit. What happens to these logged metrics in MLflow when the training run completes?

Accepted Answer

MLflow keeps all values, enabling the visualization of metrics over time.. MLflow is designed to track metrics over time, which is crucial for monitoring model convergence during training. By logging the same metric key multiple times, MLflow stores each value as a point in a time series. This allows developers to visualize the training progress and identify when the model stops improving, which is critical for implementing early stopping and optimizing training efficiency in deep learning.

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MLflow overwrites the previous value with the latest value.

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The run will throw an error because the metric key is not unique.

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MLflow averages the values and stores the mean.