Databricks-ML-Assoc Practice Test 6 — 10 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.

Answer

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.

Answer

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.

Answer

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.

Answer

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.

Answer

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.

Answer

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.

Answer

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.

Answer

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.

Answer

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.