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Databricks Databricks-Machine-Learning-Associate Exam Sample Questions

Question # 1

In which of the following situations is it preferable to impute missing feature values with their median value over the mean value?

A. When the features are of the categorical type

B. When the features are of the boolean type

C. When the features contain a lot of extreme outliers

D. When the features contain no outliers

E. When the features contain no missingno values

Answer

C. When the features contain a lot of extreme outliers

Explanation:

Imputing missing values with the median is often preferred over the mean in scenarios where the data contains a lot of extreme outliers. The median is a more robust measure of central tendency in such cases, as it is not as heavily influenced by outliers as the mean. Using the median ensures that the imputed values are more representative of the typical data point, thus preserving the integrity of the dataset's distribution. The other options are not specifically relevant to the question of handling outliers in numerical data.

References:

Data Imputation Techniques (Dealing with Outliers).

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Question # 2

A data scientist is performing hyperparameter tuning using an iterative optimization algorithm. Each evaluation of unique hyperparameter values is being trained on a single compute node. They are performing eight total evaluations across eight total compute nodes. While the accuracy of the model does vary over the eight evaluations, they notice there is no trend of improvement in the accuracy. The data scientist believes this is due to the parallelization of the tuning process. Which change could the data scientist make to improve their model accuracy over the course of their tuning process?

A. Change the number of compute nodes to be half or less than half of the number of evaluations.

B. Change the number of compute nodes and the number of evaluations to be much larger but equal.

C. Change the iterative optimization algorithm used to facilitate the tuning process.

D. Change the number of compute nodes to be double or more than double the number of evaluations.

Answer

C. Change the iterative optimization algorithm used to facilitate the tuning process.

Explanation:

The lack of improvement in model accuracy across evaluations suggests that the optimization algorithm might not be effectively exploring the hyperparameter space. Iterative optimization algorithms like Tree-structured Parzen Estimators (TPE) or Bayesian Optimization can adapt based on previous evaluations, guiding the search towards more promising regions of the hyperparameter space.

Changing the optimization algorithm can lead to better utilization of the information gathered during each evaluation, potentially improving the overall accuracy.

References:

Hyperparameter Optimization with Hyperopt

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Question # 3

A data scientist is using Spark SQL to import their data into a machine learning pipeline. Once the data is imported, the data scientist performs machine learning tasks using Spark ML. Which of the following compute tools is best suited for this use case?

A. Single Node cluster

B. Standard cluster

C. SQL Warehouse

D. None of these compute tools support this task

Answer

B. Standard cluster

Explanation:

For a data scientist using Spark SQL to import data and then performing machine learning tasks using Spark ML, the best-suited compute tool is a Standard cluster. A Standard cluster in Databricks provides the necessary resources and scalability to handle large datasets and perform distributed computing tasks efficiently, making it ideal for running Spark SQL and Spark ML operations.

References:

Databricks documentation on clusters: Clusters in Databricks

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Question # 4

A new data scientist has started working on an existing machine learning project. The project is a scheduled Job that retrains every day. The project currently exists in a Repo in Databricks. The data scientist has been tasked with improving the feature engineering of the pipeline’s preprocessing stage. The data scientist wants to make necessary updates to the code that can be easily adopted into the project without changing what is being run each day. Which approach should the data scientist take to complete this task?

A. They can create a new branch in Databricks, commit their changes, and push those changes to the Git provider.

B. They can clone the notebooks in the repository into a Databricks Workspace folder and make the necessary changes.

C. They can create a new Git repository, import it into Databricks, and copy and paste the existing code from the original repository before making changes.

D. They can clone the notebooks in the repository into a new Databricks Repo and make the necessary changes.

Answer

A. They can create a new branch in Databricks, commit their changes, and push those changes to the Git provider.

Explanation:

The best approach for the data scientist to take in this scenario is to create a new branch in Databricks, commit their changes, and push those changes to the Git provider. This approach allows the data scientist to make updates and improvements to the feature engineering part of the preprocessing pipeline without affecting the main codebase that runs daily. By creating a new branch, they can work on their changes in isolation. Once the changes are ready and tested, they can be merged back into the main branch through a pull request, ensuring a smooth integration process and allowing for code review and collaboration with other team members.

References:

Databricks documentation on Git integration: Databricks Repos

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Question # 5

A machine learning engineer is converting a decision tree from sklearn to Spark ML. They notice that they are receiving different results despite all of their data and manually specified hyperparameter values being identical. Which of the following describes a reason that the single-node sklearn decision tree and the Spark ML decision tree can differ?

A. Spark ML decision trees test every feature variable in the splitting algorithm

B. Spark ML decision trees automatically prune overfit trees

C. Spark ML decision trees test more split candidates in the splitting algorithm

D. Spark ML decision trees test a random sample of feature variables in the splitting algorithm

E. Spark ML decision trees test binned features values as representative split candidates

Answer

E. Spark ML decision trees test binned features values as representative split candidates

Explanation:

One reason that results can differ between sklearn and Spark ML decision trees, despite identical data and hyperparameters, is that Spark ML decision trees test binned feature values as representative split candidates. Spark ML uses a method called "quantile binning" to reduce the number of potential split points by grouping continuous features into bins. This binning process can lead to different splits compared to sklearn, which tests all possible split points directly. This difference in the splitting algorithm can cause variations in the resulting trees.

References:

Spark MLlib Documentation (Decision Trees and Quantile Binning).

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