Databricks-ML-Assoc Practice Test 3 — 25 Questions

Question 1

Which approach is most efficient for tracking hyperparameter tuning metrics across thousands of runs on Databricks?

Accepted Answer

Using MLflow Experiments UI.. Using the MLflow Search API or the Experiments UI is the standard approach to manage and analyze large volumes of experiment results. These tools provide filtering, sorting, and visualization capabilities that allow data scientists to quickly identify the best performing models from thousands of candidates. This efficiency is necessary for iterative machine learning, where the ability to derive insights from vast amounts of experiment data drives faster and more accurate model development.

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Logging results to a text file.

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Printing metrics to the notebook logs.

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Manually updating a spreadsheet.

Question 2

A model has been trained on a dataset containing categorical features with high cardinality. Which technique is most effective for preparing these features for a linear model in a Databricks environment?

Accepted Answer

Use Target Encoding to map categories to their mean target value.. Linear models require numeric inputs and are sensitive to the scale and representation of categorical variables. One-hot encoding high-cardinality features results in sparse, high-dimensional matrices that degrade performance. Target encoding or using specialized embeddings is more efficient. Choosing the right encoding strategy is a critical model development decision that directly impacts training speed, convergence, and the overall predictive accuracy of the final model deployed into production.

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One-hot encode all categorical variables.

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Drop the categorical features entirely.

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Convert all features into a single string representation.

Question 3

A data scientist is working in a Databricks notebook and wants to use MLflow to log a trained scikit-learn model. They want to ensure that the model can be loaded later for inference. What is the correct MLflow function to log the model?

Accepted Answer

mlflow.sklearn.log_model. To log a scikit-learn model, use mlflow.sklearn.log_model. This function captures the model, its dependencies, and optionally a signature, making it easy to load later with mlflow.sklearn.load_model. It is the standard way to persist scikit-learn models in MLflow, ensuring reproducibility and compatibility with model serving.

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mlflow.log_artifact

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mlflow.log_model

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mlflow.register_model

Question 4

A data scientist is using Databricks Feature Store to create a feature table for a recommendation model. They want to ensure that the feature table can be used for both training and batch scoring, and that it supports point-in-time correctness. Which two actions must they take when creating the feature table? (Choose two.)

Accepted Answer

Define a primary key for the feature table.. To create a feature table in Databricks Feature Store that supports training, batch scoring, and point-in-time correctness, the data scientist must define a primary key and a timestamp key. The primary key identifies entities for joins, and the timestamp key enables time-aware joins to prevent leakage. Other actions like enabling time travel manually, partitioning, or using streaming tables are not required and may be unnecessary or counterproductive. Thus, the correct choices are defining a primary key and specifying a timestamp key.

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Set the table as a streaming table.

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Partition the feature table by the primary key.

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Enable time travel on the feature table.

Question 5

A data scientist is preparing a dataset for training a model on Databricks. They want to split the data into training and testing sets, and they need to ensure that the split is reproducible across different runs. Which PySpark method should they use to split the DataFrame with a fixed random seed?

Accepted Answer

df.randomSplit([0.8, 0.2], seed=42). randomSplit is the PySpark method designed to split a DataFrame into multiple parts based on weights, and it accepts a seed for reproducibility. This allows the data scientist to obtain consistent training and testing sets across runs. Other methods like sample, repartition, or cache do not provide a reproducible train-test split.

Answer

df.repartition(2)

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df.sample(fraction=0.8, seed=42)

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df.cache()

Question 6

A data scientist is using MLflow to track experiments on Databricks. They want to compare multiple runs and identify the best performing model based on a custom metric. Which TWO features of MLflow can be used to achieve this? (Choose two.)

Accepted Answer

MLflow tracking API's search_runs method. The MLflow UI's compare runs feature and the tracking API's search_runs method both allow you to compare runs and sort by custom metrics. The UI provides a visual side-by-side comparison, while search_runs enables programmatic querying and sorting. Together, they enable identifying the best performing run based on a custom metric.

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MLflow Projects with a custom entry point

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MLflow Model Registry's stage transitions

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MLflow's automatic logging with autolog()

Question 7

A machine learning engineer has a registered model and wants to expose it as a REST endpoint that their application can call for real-time predictions. They need the endpoint to be created and managed natively within Databricks, with the ability to enable scale-to-zero during idle periods. Which Databricks capability should they use?

Accepted Answer

Databricks Model Serving, creating an endpoint that serves the registered model and configuring the served entity with scale-to-zero enabled.. Databricks Model Serving is the native mechanism for deploying registered models as REST endpoints with managed lifecycle, authentication, and optional scale-to-zero. A Flask process on a job cluster, registry webhooks, or a SQL warehouse cannot provide a stable, secure, autoscaling prediction API, so they fail the scenario's requirements for native management and idle shutdown.

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MLflow Model Registry webhooks that notify an external service whenever a model version is registered, letting that service host the model.

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A Databricks job that runs a Python script loading the model and starting a Flask web server on a driver node, exposed via a public URL.

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A SQL warehouse with a user-defined function that loads the model artifact and returns predictions for incoming queries.

Question 8

A machine learning engineer is orchestrating a multi-step training workflow on Databricks using Databricks Jobs. The workflow includes data preprocessing, model training, and evaluation. The engineer needs to ensure that the evaluation step runs only if the training step completes successfully, and that the preprocessing step runs first. Which feature of Databricks Jobs should be used to define these dependencies?

Accepted Answer

Task dependencies using the depends_on field. Task dependencies via the depends_on field are the core mechanism in Databricks Jobs for orchestrating multi-task workflows. They allow you to specify that a task runs only after its upstream tasks complete successfully, enabling conditional execution. This directly satisfies the need to run preprocessing first, then training, and evaluation only if training succeeds. Other options address compute scaling, failure recovery, or version control, not workflow dependencies.

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Repair run with task-level retries

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Databricks Repos integration with Git

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Job clusters with autoscaling enabled

Question 9

Refer to the exhibit. A user attempts to update a model stage in the Model Registry and receives this error. What is the most appropriate action to resolve this?

Accepted Answer

Request the workspace admin to update the model ACLs. The error clearly indicates a lack of permission to modify the model's state. In Databricks, access control lists (ACLs) are strictly enforced on objects in the registry. To perform administrative actions like changing a model's stage, the user must be assigned the appropriate permission level (CAN_MANAGE) by an owner or admin. This enforces the principle of least privilege, preventing unauthorized users from promoting unvetted models to production.

Answer

Re-register the model under a new name

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Upgrade the model to a higher version

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Switch to a different Databricks cluster

Question 10

Refer to the exhibit. A data scientist is logging a model to MLflow. Why is including an 'input_example' highly recommended in this specific code snippet?

Accepted Answer

It allows MLflow to validate the schema and ensures the model is compatible with deployment endpoints.. The input_example acts as a validation mechanism that MLflow uses to verify that the model can successfully process incoming data. It also enables automatic schema inference, which is crucial for downstream services like Model Serving. By providing this example, developers ensure that the model registry has metadata that allows for schema validation at serving time, reducing the risk of runtime errors when the model is deployed to production endpoints.

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It forces the model to retrain every time it is loaded into production.

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It automatically encrypts the model artifacts for security compliance.

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It creates a dummy model version that cannot be used for inference.

Question 11

When preparing data for machine learning in Databricks, which feature of Delta Lake is most beneficial for managing large-scale datasets during the training process?

Accepted Answer

Time Travel (versioning) to access previous snapshots of training data.. Delta Lake's Time Travel (versioning) feature allows data scientists to query data as it existed at a specific point in time. This is essential for ensuring reproducibility in machine learning experiments, as it ensures that the training data remains consistent across different runs, even if the underlying table is updated or modified by other processes in the production pipeline.

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Compaction (Auto Optimize) for improved query speed during data reading.

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Schema enforcement to prevent corrupted data from entering the training set.

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Z-Ordering to speed up filtering on specific columns in the dataset.

Question 12

When developing a model, a data scientist uses the MLflow 'pyfunc' flavor to wrap their model. What is the primary benefit of using this approach?

Accepted Answer

It enables the model to be saved and loaded in a consistent, standardized format across different frameworks.. The 'pyfunc' flavor provides a generic, standardized wrapper for models. This is crucial because it allows any model—regardless of the library (scikit-learn, XGBoost, PyTorch)—to be deployed in the same way. By providing a common predict method, 'pyfunc' ensures that downstream serving infrastructure can interact with the model without knowing the underlying library's specific API, which is vital for standardized model deployment pipelines.

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It automatically converts the model into a Spark UDF for faster distributed batch inference.

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It optimizes the model size by removing unnecessary metadata during the serialization process.

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It provides built-in hyperparameter tuning capabilities for the wrapped model.

Question 13

An ML engineer registers a model in Unity Catalog and needs a stable pointer that always resolves to the version currently approved for production, without modifying calling code each time a new version is promoted. Which Model Registry feature should the engineer configure to provide this stable reference?

Accepted Answer

A model version alias such as 'champion' assigned to the approved version. Model version aliases provide exactly the stable, reassignable pointer described: consumers load by alias, and promotion is performed by moving the alias to the newly approved version. Tags, descriptions, and permission grants are metadata or access controls and cannot redirect consumers to a chosen version at load time.

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A model version tag recording the approval date

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A registered model description summarizing the production candidate

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A workspace-level permission grant on the registered model

Question 14

Which approach is most efficient for deploying a high-throughput, low-latency model in Databricks?

Accepted Answer

Using Databricks Model Serving. Databricks Model Serving is the recommended service for production environments requiring low latency and high scalability. It automatically manages scaling and infrastructure, ensuring that models are served in a highly available manner. By abstracting away the server management and load balancing, it allows developers to focus on model performance rather than infrastructure maintenance, which is essential for maintaining service level agreements in production-grade ML applications.

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Running a custom Flask app on a standard notebook

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Batch processing using Spark streaming

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Manual deployment to an external cloud VM

Question 15

Which Databricks feature provides a managed environment specifically optimized for machine learning libraries like TensorFlow, PyTorch, and XGBoost?

Accepted Answer

Databricks Runtime for Machine Learning. Databricks Runtime for Machine Learning (ML Runtime) is pre-configured with popular machine learning libraries and optimized versions of deep learning frameworks. It includes pre-installed dependencies and optimized binaries for distributed training, which saves time for data scientists who would otherwise need to manually configure clusters. This runtime simplifies the setup process and ensures compatibility between the infrastructure and the most common data science toolkits used in production machine learning projects.

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Databricks SQL

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Delta Live Tables

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Unity Catalog

Question 16

A data scientist needs to run the same feature-engineering notebook against three different parameter sets in a Databricks Job. She wants each parameter set to execute independently and in parallel, with separate logs. Which Job feature should she use?

Accepted Answer

A for-each task that iterates over a parameter array and runs the notebook once per element.. The for-each task is designed for iterating a nested task over an input array, producing one task run per element with its own logs and status. That gives the independent, parallel executions the scientist needs while keeping everything in one workflow. Passing a JSON string, creating separate jobs, or relying on autoscaling all fail to produce per-parameter task runs with isolated logs.

Answer

A job cluster with autoscaling enabled and a single task.

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A single notebook task with the three parameter sets passed as a JSON string argument.

Answer

Three separate jobs created from the same notebook, each with its own parameter.

Question 17

A data scientist is using Databricks Feature Store to create a feature table for a recommendation model. They want to ensure that the same feature computation logic is used both during training and at inference time to avoid training-serving skew. Which Feature Store capability directly addresses this requirement?

Accepted Answer

Feature Store allows features to be computed once and reused across training and batch or online serving through a unified feature computation function.. Databricks Feature Store reduces training-serving skew by letting you define a feature computation function that is used both to create the feature table for training and to compute features at inference time. This unified logic ensures that the model sees features derived from the same transformations in both phases.

Answer

Feature Store provides a point-in-time lookup that ensures training data reflects the state of features at the time of each label event.

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Feature Store enforces schema validation on feature tables, rejecting any inference request that does not match the training schema.

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Feature Store automatically versions feature tables and tracks lineage from source data to model.

Question 18

A data scientist is building a pipeline using MLflow. Which TWO of the following tasks should be performed to ensure experiment reproducibility and auditability?

Accepted Answer

Log the git commit hash using mlflow.set_tag().. Reproducibility in machine learning requires strict tracking of code versions, dependencies, and parameters. By logging the source code version (git hash) and the specific Python environment (conda.yaml or requirements.txt), the scientist ensures that any collaborator can recreate the exact training state later. These practices are fundamental to the Databricks ML lifecycle, ensuring that models can be retrained or audited for compliance during the deployment phase.

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Manually copy the training data into the MLflow model folder.

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Disable the automatic logging feature to save memory.

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Use a global variable for all experiment parameters.

Question 19

You are tracking a deep learning model experiment using MLflow. You want to ensure that the model architecture and all hyperparameters are easily reproducible. Which approach is best practice?

Accepted Answer

Log parameters, metrics, and include the conda environment configuration with the model.. Logging model parameters and the environment configuration (like conda.yaml or requirements.txt) via MLflow ensures reproducibility. By capturing the exact package versions and architectural settings during the run, you eliminate the 'it works on my machine' problem. This practice is essential in production workflows where model drift or performance degradation requires auditing the exact conditions under which a specific model version was trained.

Answer

Only log the final model artifacts to the MLflow Model Registry to save disk space.

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Manually copy the training code into a text field in the MLflow UI for every run.

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Use a global variable in your notebook to store all hyperparameters for easy access.

Question 20

Which THREE actions are best practice when deploying a machine learning model using Databricks Model Serving?

Accepted Answer

Ensure the model signature is defined to enable input validation.. Databricks Model Serving provides a managed endpoint for low-latency inference. Adopting best practices for deployment—such as using environment variables, ensuring proper logging, and testing in staging—is essential for maintaining reliable, scalable, and secure production services. These practices ensure that the serving infrastructure is decoupled from the training environment, adheres to security policies, and provides observability into model performance in real-world conditions, effectively bridging the gap between model development and operational deployment.

Answer

Hardcode API credentials directly into the model inference script.

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Perform inference on the same cluster used for training to save costs.