Databricks-ML-Assoc Practice Test 2 — 10 Questions

Question 1

A data scientist is using Databricks to train a deep learning model. They need to monitor training loss and accuracy in real-time. Which tool is best suited for this task?

Accepted Answer

MLflow log_metric API.. MLflow's logging API allows users to log metrics at each step of the training loop. By calling `mlflow.log_metric()` during the epochs of a deep learning model, the scientist can visualize the training progress in the MLflow UI. This real-time feedback loop is essential for detecting issues like vanishing gradients or overfitting early in the development lifecycle, preventing wasted compute hours on non-converging or poor-performing models.

Answer

Databricks File System (DBFS) logs.

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Spark UI metrics tab.

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Unity Catalog lineage.

Question 2

A data scientist needs to track parameters, metrics, and model artifacts during training on Databricks. Which component is the primary tool for managing the entire lifecycle of these ML experiments?

Accepted Answer

MLflow Tracking. MLflow Tracking is the dedicated component within Databricks for recording experiments. By logging parameters, metrics, and artifacts, data scientists can reproduce results and compare different model versions effectively. This is crucial for maintaining model lineage and ensuring reproducibility across distributed training jobs in production environments. MLflow is integrated natively into the Databricks platform, providing a seamless experience for tracking machine learning workflows from experimentation to final deployment.

Answer

Databricks Feature Store

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Databricks Model Serving

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Unity Catalog

Question 3

A data scientist is training a model using MLflow on Databricks. They need to ensure that the model artifacts and metrics are logged automatically without adding manual logging code to the training script. Which approach should they use?

Accepted Answer

Execute mlflow.autolog() at the start of the notebook cell before running the model training code.. MLflow provides autologging capabilities that automatically capture parameters, metrics, and models when using popular machine learning libraries like Scikit-learn, TensorFlow, or PyTorch. By calling mlflow.autolog() before the training code, the framework instruments the library calls to record telemetry automatically. This approach minimizes boilerplate code and ensures consistency across experiments, which is essential for auditability and model reproducibility in collaborative Databricks environments, as it prevents human error in manual logging.

Answer

Call mlflow.set_tracking_uri() inside the training function to redirect logs to the workspace.

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Wrap the training logic within an mlflow.start_run() block without any additional configuration.

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Configure the Databricks cluster environment variable MLFLOW_TRACKING_ENABLED to true.

Question 4

A data scientist is comparing multiple hyperparameter configurations for a model and wants to view the resulting metrics side by side in a single interface, sort runs by accuracy, and drill into individual run details. Which MLflow component provides this capability?

Accepted Answer

MLflow Tracking UI, which lists runs within an experiment and supports sorting and filtering by metrics.. The MLflow Tracking UI is designed for experiment comparison. It presents runs in a sortable table with metrics and parameters, supports filtering, and allows drilling into individual runs. The Model Registry UI, Projects, and Jobs run history serve different purposes and do not offer the same run comparison and metric sorting features.

Answer

MLflow Model Registry UI, which shows model versions and their stage transitions.

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MLflow Projects dashboard, which lists available projects and their entry points.

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Databricks Jobs run history, which shows task durations and statuses for scheduled workflows.

Question 5

Refer to the exhibit. A data scientist is attempting to deploy a model using the MLflow client. The error above occurs during the deployment script. What is the most likely cause of this failure?

Accepted Answer

The model has not been registered in the Model Registry with the specified name and version.. This error indicates that the specified model version does not exist in the Registry, either because it was never registered, was deleted, or the name/version number was referenced incorrectly. In a Databricks context, verify that the model has been successfully logged and registered using mlflow.register_model. This is critical for deployment pipelines as it validates that the artifact exists before the serving infrastructure attempts to load the model file.

Answer

The cluster does not have the necessary MLflow libraries installed.

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The user lacks permissions to read the model version from the Registry.

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The model version is still in the 'Archived' state and cannot be accessed.

Question 6

A machine learning engineer is using MLflow to track experiments. They want to compare multiple runs and identify the run that produced the best model based on a custom metric called 'weighted_f1'. They have logged this metric using mlflow.log_metric. Which MLflow UI feature allows them to sort and filter runs by this metric to quickly find the best run?

Accepted Answer

The runs table with column sorting and the filter box using metric.weighted_f1.. The MLflow UI runs table provides a sortable and filterable view of all runs in an experiment. By sorting on the 'weighted_f1' metric column or using a filter expression, the engineer can instantly locate the top-performing run. This is the standard way to compare runs and select the best model.

Answer

The experiment notes field where you can manually record the best run ID.

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The model registry's stage transitions, which automatically promote the run with the highest metric.

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The artifact viewer, which displays metric plots and allows sorting by value.

Question 7

A data scientist is training a deep learning model on Databricks using Horovod for distributed training. They find that the model is converging slowly. What is the most likely cause related to the distributed configuration?

Accepted Answer

The learning rate was not adjusted for the distributed batch size.. In Horovod distributed training, the learning rate must often be scaled relative to the number of workers, because each worker processes a batch of data. A common mistake is failing to adjust the learning rate or optimizer settings to compensate for the aggregate batch size across the cluster. This results in ineffective gradient updates, slowing down convergence significantly compared to a single-node setup.

Answer

The cluster has too much memory per worker.

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The storage throughput of the DBFS mount is too high.

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The number of epochs is too low for the current model size.

Question 8

A data scientist registered a model in Unity Catalog and now wants to serve it as a low-latency REST endpoint for an application. They need automatic scaling, a secure endpoint URL, and the ability to update the served model version without redeploying infrastructure. Which Databricks capability should they use?

Accepted Answer

Model Serving with a served entity pointing at the registered model version.. Model Serving provides managed, autoscaling REST endpoints backed by registered model versions. Configuring a served entity that references a Unity Catalog model and version gives a secure URL and lets the team change the served version without rebuilding the endpoint, satisfying low-latency, secure, and updatable serving needs in one capability.

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A SQL warehouse with a user-defined function wrapping the model.

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An all-purpose cluster running an MLflow model server manually.

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A Databricks job that runs a notebook on a schedule to score requests.

Question 9

A data scientist is using Spark MLlib and wants to perform feature scaling on a large dataset. Which transformer should they use within a Pipeline to ensure that the scaling logic is correctly applied during both training and inference?

Accepted Answer

StandardScaler from pyspark.ml.feature.. Using a Pipeline with a scaler like StandardScaler or MinMaxScaler ensures that the scaling parameters (like mean and standard deviation) are captured as part of the model object. This is critical because the exact same scaling must be applied to new, unseen data during inference. Without this, the model will receive improperly scaled inputs, leading to incorrect predictions, as the model expects data to follow the same distribution used during training.

Answer

Manually calculate the mean and divide the DataFrame columns.

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Pandas transformation functions via a UDF.

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The VectorAssembler utility to normalize the input vector.

Question 10

A data scientist is using Databricks Feature Store to create a feature table for a machine learning model. They want to ensure that the features used during training are consistent with those used during inference. Which Databricks Feature Store capability should they use?

Accepted Answer

Feature Store's integration with MLflow to log the model with feature specifications.. The correct answer is Feature Store's integration with MLflow to log the model with feature specifications. When a model is trained and logged using Databricks Feature Store, the feature specifications are stored with the model. During inference, the model automatically retrieves features from the Feature Store using those specifications, ensuring that the same features and transformations are applied. This eliminates training-serving skew. Other capabilities like lineage, point-in-time lookups, and online stores are valuable but do not directly enforce consistency.

Answer

Feature Store's point-in-time lookups to avoid data leakage during training.

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Feature Store's ability to materialize features to an online store for low-latency serving.

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Feature Store's automatic lineage tracking to trace feature transformations.