Databricks-ML-Assoc Practice Test 22 — 15 Questions

Question 1

A machine learning team is using Databricks Feature Store to serve features for online inference. They need to ensure that the online store remains consistent with the offline store and supports low-latency lookups. Which two practices should they follow? (Choose two.)

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

Accepted Answer

Schedule regular updates to the online store to reflect changes in the offline feature table.. Publishing feature tables to the online store and scheduling regular updates are essential to keep online and offline stores consistent. The publish_table API automates the sync, and scheduled jobs ensure freshness. Manual copying, using the online store as primary, or disabling sync would compromise consistency and performance.

Answer

Manually copy feature values from the offline store to the online store using a notebook.

Answer

Disable automatic synchronization to reduce overhead.

Answer

Use the online store as the primary source of truth for feature engineering.

Question 2

A data scientist has trained a model using scikit-learn and wants to log it to MLflow for deployment. They need to ensure that the model can be served with the correct dependencies. Which MLflow function should they use to log the model?

Accepted Answer

`mlflow.sklearn.log_model()`. `mlflow.sklearn.log_model()` is specifically designed to log scikit-learn models, capturing the model flavor, dependencies, and signature. This allows the model to be served or deployed with the correct environment. Other functions either log artifacts, metrics, or register an already logged model, none of which fulfill the logging requirement.

Answer

`mlflow.log_metric()`

Answer

`mlflow.register_model()`

Answer

`mlflow.log_artifact()`

Question 3

What is the primary function of the 'Model Signature' in the context of Databricks MLflow?

Accepted Answer

To define the schema and types for the model inputs and outputs.. The Model Signature defines the schema of the inputs, outputs, and parameters of the model. It ensures that the model is invoked with data that matches its training assumptions, preventing runtime errors. By enforcing this schema, Databricks helps developers build more robust pipelines where data mismatches are caught early, rather than causing silent failures or errors during production inference.

Answer

To encrypt the model artifacts for secure transfer.

Answer

To optimize the model for specific hardware architectures.

Answer

To store the git hash of the code used to train the model.

Question 4

A machine learning engineer is using MLflow on Databricks to track experiments for a fraud detection model. They notice that runs from two different team members are being logged into the same experiment, but the engineer wants to ensure that all runs from the current notebook session are automatically associated with a specific experiment. Which MLflow API call should the engineer use to set the active experiment for the current session?

Accepted Answer

mlflow.set_experiment("/Shared/fraud-detection"). The correct API is mlflow.set_experiment, which sets the active experiment for the current session. This ensures that all subsequent runs are logged to the specified experiment without needing to pass the experiment ID to each run. It is the standard way to organize runs by project or team in Databricks.

Answer

mlflow.start_run(experiment_id="12345")

Answer

mlflow.set_tracking_uri("databricks")

Answer

mlflow.create_experiment("/Shared/fraud-detection")

Question 5

A data scientist is using MLflow to track experiments on Databricks. They want to log a custom metric that is computed during model training and later compare it across runs using the MLflow UI. Which MLflow API call should they use?

Accepted Answer

mlflow.log_metric("custom_metric", value). The data scientist needs to track a numeric value computed during training and compare it across runs. MLflow provides specific APIs for different types of data: parameters for inputs, metrics for numeric outputs, tags for metadata, and artifacts for files. Using log_metric ensures the value is stored as a metric and can be visualized and compared in the MLflow UI. The other APIs serve different purposes and would not provide the desired functionality.

Answer

mlflow.set_tag("custom_metric", value)

Answer

mlflow.log_artifact("custom_metric", value)

Answer

mlflow.log_param("custom_metric", value)

Question 6

When designing a production-grade machine learning workflow, which THREE of the following are necessary to ensure the pipeline is observable and recoverable?

Accepted Answer

Use Delta Lake to version the training datasets.. Observability and recoverability are pillars of reliable MLOps. Comprehensive logging (via MLflow) provides the 'why' behind a model's state. Versioning datasets ensures you can recreate the input data if a model fails. Finally, robust checkpointing during training allows the job to resume from the last successful state rather than restarting from scratch, saving significant time and resources when failures occur in large, long-running training jobs.

Answer

Configure the job to automatically ignore all failures and continue.

Answer

Run all jobs manually to avoid the risk of automation errors.

Question 7

A data scientist wants to compare the performance of three hyperparameter configurations for a Spark ML model. They need a central place to view metrics like RMSE and MAE across runs, and to filter runs by parameters. Which Databricks capability should they use?

Accepted Answer

MLflow Tracking with the Databricks-hosted experiment UI. MLflow Tracking, accessed through the Databricks experiment UI, is the component that logs and visualizes run metrics, parameters, and artifacts. It enables filtering and side-by-side comparison of runs, which is exactly what the data scientist needs for evaluating hyperparameter configurations using RMSE and MAE.

Answer

Databricks Jobs with task dependencies

Answer

Databricks Feature Store

Answer

Databricks Model Registry

Question 8

What is the primary benefit of using 'AutoML' in Databricks for a machine learning project?

Accepted Answer

Automating the model training and tuning process.. Databricks AutoML automatically explores various algorithms, hyperparameters, and preprocessing techniques to identify the best-performing model for a given dataset. This significantly accelerates the prototyping phase, allowing data scientists to establish a baseline model quickly. By automating the tedious parts of the machine learning pipeline, AutoML frees up time for data scientists to focus on complex feature engineering, business logic integration, and model interpretation rather than manual trial-and-error workflows.

Answer

Automatically replacing all human data scientists.

Answer

Providing the only way to register models in Databricks.

Answer

Enabling the direct deployment of models without testing.

Question 9

A team is building a Feature Store in Databricks. What is the primary advantage of using the Feature Store for training models compared to using raw Delta tables?

Accepted Answer

It prevents training-serving skew by using consistent feature engineering.. The Feature Store provides a centralized repository for features, ensuring that the same feature engineering logic is used for both training and real-time inference. This eliminates training-serving skew, a common problem where the logic used during training differs from the logic used in production. This consistency is essential for model accuracy and reliability in dynamic production environments.

Answer

It automatically scales the compute cluster based on the number of features.

Answer

It eliminates the need for data cleaning before feature creation.

Answer

It allows models to be deployed directly to external cloud storage.

Question 10

Which THREE features are provided by the MLflow Model Registry to support model governance and deployment?

Accepted Answer

Versioning of models to track changes over time.. The Model Registry acts as the governance layer for machine learning. It supports versioning (to track history), stage transitions (to control promotion from staging to production), and tagging (for metadata management). These features are critical for regulatory compliance and operational stability, ensuring that teams can track exactly which model version is in production, who approved it, and what data it was trained on for auditability.

Answer

Automatic promotion of models to production based on accuracy.

Answer

Real-time model training based on incoming production data.

Question 11

A machine learning engineer is using MLflow to track a model training run on Databricks. They log a metric with mlflow.log_metric("accuracy", 0.95) and later want to retrieve it. They call mlflow.get_run(run_id) and access run.data.metrics. However, they find that the metrics dictionary is empty. What is the most likely reason?

Accepted Answer

The metric was logged to a different run than the one being retrieved.. An empty metrics dictionary when retrieving a run indicates that no metrics were logged to that specific run. This commonly occurs when the metric is logged while a different run is active, or when the run_id used for retrieval does not match the run where logging occurred. Verifying the active run and run_id resolves the issue.

Answer

The metric name contained uppercase letters, which are not allowed in MLflow.

Answer

The metric value was outside the allowed range, so MLflow silently dropped it.

Answer

The metric was logged with a step parameter, so it is stored in run.data.metrics as a list of values.

Question 12

A data scientist is training a machine learning model and wants to ensure that the code version, model parameters, and artifacts are all linked to a specific execution. Which Databricks component is designed specifically for this purpose?

Accepted Answer

MLflow Tracking. MLflow tracking is the component that logs the full lifecycle of a machine learning experiment. By capturing parameters, code versions (via git commit hashes), and resulting model artifacts, it provides a comprehensive audit trail. This is essential for machine learning operations as it allows teams to reproduce results, debug failures, and compare performance metrics between different training runs consistently across the platform.

Answer

Databricks Feature Store

Answer

Databricks Workflows

Answer

Unity Catalog

Question 13

A machine learning engineer is tuning a scikit-learn GradientBoostingClassifier on Databricks. They want to run 40 hyperparameter combinations, each trained on the full dataset, while keeping the driver free of model training work and collecting all results in a single MLflow parent run. Which approach should they use?

Accepted Answer

Use Hyperopt with the default SparkTrials and set max_evals to 40.. Distributing many single-node model fits requires a mechanism that schedules each trial as cluster work rather than driver-local computation. SparkTrials in Hyperopt does exactly this, and with MLflow autologging each trial becomes a nested run beneath one parent run, satisfying both the parallelism and the consolidated tracking requirements in a single, supported pattern.

Answer

Use scikit-learn's GridSearchCV with n_jobs=-1 inside a single notebook cell.

Answer

Use Hyperopt with Trials and wrap the objective function in a Pandas UDF.

Answer

Launch 40 separate notebooks with dbutils.notebook.run and merge their runs afterward.

Question 14

A data scientist is using Databricks Feature Store to create a training dataset for a model. They define a feature table with a primary key and a timestamp key. After creating the training set, they notice that some feature values are missing in the output. What is the most likely cause?

Accepted Answer

The training dataset was created using a time range that does not cover the timestamps of some feature values.. Feature Store uses point-in-time lookups based on the timestamp key to ensure training data reflects the state at the time of the label. If the training dataset's time range does not include the timestamps of certain feature values, those values will be missing. This is a common pitfall when defining the training set's time range too narrowly.

Answer

The primary key of the feature table does not match the primary key of the training dataset.

Answer

The feature table was created without specifying a timestamp key, so point-in-time lookups could not be performed.

Answer

The feature table was not refreshed after new data was added, so the latest feature values are not available.

Question 15

Why should you use a dedicated Feature Store for your ML workflows instead of just storing features as Delta tables?

Accepted Answer

It ensures consistency by linking features across training and inference.. While Delta tables provide high-performance storage, a dedicated Feature Store adds a metadata layer that tracks feature lineage, usage, and schema definitions. It ensures that the exact same feature logic is shared between training and inference pipelines. This consistency is critical for preventing training-serving skew, which is a major, often silent, cause of model performance degradation when moving from development to real-world production environments.

Answer

It makes Delta tables run significantly faster during training.

Answer

It automatically cleans missing data in the raw tables.

Answer

It is the only way to store data in the Databricks platform.