Databricks-ML-Assoc Databricks Machine Learning Practice Test 6 — 15 Questions

Question 1

A machine learning engineer is training a model using Databricks AutoML. They notice that the generated notebook includes a step that uses Hyperopt for hyperparameter tuning, but the tuning process is taking too long. They want to reduce the search space without sacrificing model performance significantly. Which Hyperopt configuration change should they make?

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

Accepted Answer

Use a narrower search space by specifying more constrained ranges for hyperparameters, such as reducing the maximum depth of trees or limiting the number of leaves.. Narrowing the hyperparameter search space by constraining ranges reduces the number of possible configurations Hyperopt must evaluate. This directly cuts tuning time and often maintains model performance because it focuses on sensible values. Switching algorithms or increasing trials does not address the root cause of a large search space, and may even increase runtime.

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Switch from the default 'TPE' algorithm to 'Random' search.

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Change the search algorithm to 'Annealing' and set a high initial temperature.

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Increase the max_evals parameter to allow more trials.

Question 2

A data scientist has registered a model in the Databricks Model Registry and wants to deploy it as a REST API endpoint for real-time inference. Which Databricks feature should they use?

Accepted Answer

MLflow Model Serving. For real-time inference of registered models, Databricks offers MLflow Model Serving, which creates a managed REST endpoint. It integrates with the Model Registry, allowing you to serve specific model versions with automatic scaling. Other options are for analytics, tracking, or batch processing, not for serving models as APIs.

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Databricks Jobs

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MLflow Tracking

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Databricks SQL Analytics

Question 3

A data scientist is training a scikit-learn model on Databricks and wants to automatically log parameters, metrics, and models without writing explicit MLflow logging code. Which approach should they use?

Accepted Answer

Enable MLflow autologging by calling mlflow.sklearn.autolog() before training.. MLflow autologging for scikit-learn is enabled by calling mlflow.sklearn.autolog() before training. This automatically logs parameters, metrics, and the model without explicit logging calls. It is the standard way to achieve automatic tracking in Databricks. Other options either require manual code, are for different purposes, or are not valid configurations.

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Use the Databricks AutoML feature to train the model.

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Manually log parameters and metrics using mlflow.log_param and mlflow.log_metric, and log the model with mlflow.sklearn.log_model.

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Configure the cluster to use MLflow integration by setting the environment variable MLFLOW_AUTOLOGGING=true.

Question 4

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 5

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 6

A machine learning engineer is using MLflow Tracking on Databricks to compare multiple runs. They want to programmatically retrieve the best run based on a metric called 'rmse' from an experiment. Which MLflow API call should they use?

Accepted Answer

`mlflow.search_runs()` with `order_by=['metrics.rmse ASC']`. `mlflow.search_runs()` is designed to query runs within an experiment and can order results by metrics. By specifying `order_by=['metrics.rmse ASC']`, the run with the lowest RMSE appears first. This is efficient and programmatic, unlike manually iterating runs or using functions that retrieve single runs or experiment metadata.

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`mlflow.get_experiment_by_name()` and then filter runs by metric

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`mlflow.get_run()` with the run ID of the best run

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`mlflow.list_run_infos()` and manually iterate to find the minimum 'rmse'

Question 7

A machine learning engineer registers a model in the Databricks Model Registry and wants to serve it with low-latency online inference. The model's Python dependencies include a custom private library that is not publicly available. Which deployment approach should the engineer use to ensure the private library is available at inference time?

Accepted Answer

Package the private library as a wheel file, include it in the model's conda environment or requirements, and log the model with that dependency so the serving endpoint installs it.. To serve a model with a private dependency, the dependency must be included in the model's environment specification. Packaging the private library as a wheel file and referencing it in the conda environment or requirements ensures the serving endpoint installs it during environment construction, so the model can import it at inference time.

Answer

Deploy the model to a Databricks model serving endpoint and specify the private library as an environment variable in the endpoint configuration.

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Register the model with mlflow.pyfunc.log_model and set the pip_requirements parameter to the name of the private library as published on PyPI.

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Upload the private library to DBFS and reference its path in the model's Python code using dbutils.fs, then rely on the serving endpoint to mount DBFS automatically.

Question 8

A machine learning engineer is training a model using Databricks AutoML. They notice that the generated notebook includes a step that uses Hyperopt for hyperparameter tuning, but the tuning process is taking too long. They want to reduce the search space without sacrificing model performance significantly. Which Hyperopt configuration change should they make?

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

Accepted Answer

It logs hyperparameters, training metrics, the model signature, and the fitted model artifact automatically.. Scikit-learn autologging hooks into the training call and records hyperparameters, metrics, the inferred signature, and the fitted model artifact. Because the integration operates at the estimator level, the engineer does not need to add explicit logging calls to capture the model or its evaluation results, which directly fulfills the requirement of automatic capture during training.

Answer

It logs the environment dependencies only, allowing reproduction but not artifact capture.

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It logs only the hyperparameters passed to the estimator constructor and nothing else.

Answer

It logs metrics and artifacts but requires a separate call to register the model in Unity Catalog.

Question 9

A machine learning engineer is using Databricks Feature Store to create a training dataset. They want to ensure that the features used during training are exactly the same as those served at inference time. Which Feature Store capability should they rely on?

Accepted Answer

Point-in-time lookups when creating the training set. Databricks Feature Store provides point-in-time lookups to create training datasets that reflect the state of features at the time of each label. This prevents leakage and ensures that the same feature values would be available at inference. Other options are either not Feature Store capabilities or do not address training-serving skew.

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Feature freshness monitoring

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Automatic feature scaling during training

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Online store for low-latency feature retrieval

Question 10

A data scientist has registered a model in the Databricks Model Registry. They want to transition the model from 'Staging' to 'Production' but need to ensure that only specific users can perform this transition. Which Databricks feature should they use to enforce this access control?

Accepted Answer

Model Registry permissions with stage-level access control.. The Model Registry provides granular permissions at the model level, including the ability to restrict stage transitions. By configuring stage-level access control, administrators can ensure that only authorized users can move a model to Production. Other features like cluster ACLs, secret scopes, and volume permissions do not govern model registry operations.

Answer

Cluster access control lists (ACLs).

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Unity Catalog volume permissions.

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Workspace-level secret scopes.

Question 11

A data scientist has trained a scikit-learn model and wants to log it to MLflow with a custom signature that includes input and output schema. Which MLflow method should they use to log the model along with the signature?

Accepted Answer

mlflow.sklearn.log_model(model, "model", signature=signature). The data scientist needs to log a scikit-learn model with a signature. MLflow provides flavor-specific functions like mlflow.sklearn.log_model that accept a signature parameter. This logs the model with the schema, enabling proper validation and serving. The generic mlflow.log_model is not directly used for scikit-learn, mlflow.log_artifact lacks model metadata, and mlflow.register_model is for registration, not logging. Therefore, mlflow.sklearn.log_model is correct.

Answer

mlflow.register_model(model, "model")

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mlflow.log_artifact(model, "model")

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mlflow.log_model(model, "model", signature=signature)

Question 12

A data scientist is using MLflow to train a scikit-learn model on Databricks. They call mlflow.sklearn.autolog() before fitting the model. After the run completes, they need to retrieve the automatically logged model and load it for batch inference in a separate notebook. Which approach correctly retrieves the logged model for loading?

Accepted Answer

Use the MLflow run ID to construct the artifact URI 'runs:/<run_id>/model' and pass it to mlflow.sklearn.load_model().. With MLflow autologging, the trained model is saved as an artifact in the run's artifact directory under the name 'model'. To load it later, you need the run ID and the artifact path. The 'runs:/' URI scheme provides a portable reference to the run's artifacts, and mlflow.sklearn.load_model() correctly interprets that URI to reconstruct the model.

Answer

Access the model directly from the DBFS path '/dbfs/FileStore/models/<run_id>' and load it using joblib.load().

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Read the model from the Delta table that MLflow automatically creates in the default database when autologging is enabled.

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Query the MLflow tracking server's REST API endpoint '/api/2.0/mlflow/runs/get' and extract the model binary from the response.

Question 13

A data scientist is using Databricks AutoML to train a classification model on a dataset with a highly imbalanced target variable. They want to ensure the model evaluation focuses on the minority class. Which evaluation metric should they prioritize when interpreting AutoML results?

Accepted Answer

F1 score. F1 score balances precision and recall, making it suitable for imbalanced classification where the minority class is of interest. AUC and accuracy can be misleading, and MSE is for regression. Prioritizing F1 ensures the model captures the minority class effectively.

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Mean squared error (MSE)

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Accuracy

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Area under the ROC curve (AUC)

Question 14

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 15

A data engineer is working with a large Delta table and wants to optimize it for machine learning feature engineering. They frequently filter data by a column named 'event_date' and join on a column named 'user_id'. Which Delta Lake feature should they use to improve query performance?

Accepted Answer

Z-ORDER BY on 'user_id' and 'event_date'.. Z-ORDER BY clusters data on the specified columns, enabling efficient data skipping for filters and joins. Since the engineer filters by 'event_date' and joins on 'user_id', Z-ORDER on both columns will significantly improve performance. Partitioning on high-cardinality 'user_id' is inefficient, and CDF or simple compaction do not address the need for co-locating related data.

Answer

Enabling change data feed (CDF).

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Using OPTIMIZE with file compaction.

Answer

Partitioning by 'user_id'.