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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 health organization is developing a classification model to determine whether or not a patient currently has a specific type of infection. The organization's leaders want to maximize the number of positive cases identified by the model. Which of the following classification metrics should be used to evaluate the model?

A. RMSE

B. Precision

C. Area under the residual operating curve

D. Accuracy

E. Recall

Answer

E. Recall

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Question # 3

Which of the following describes the relationship between native Spark DataFrames and pandas API on Spark DataFrames?

A. pandas API on Spark DataFrames are single-node versions of Spark DataFrames with additional metadata

B. pandas API on Spark DataFrames are more performant than Spark DataFrames

C. pandas API on Spark DataFrames are made up of Spark DataFrames and additional metadata

D. pandas API on Spark DataFrames are less mutable versions of Spark DataFrames

Answer

C. pandas API on Spark DataFrames are made up of Spark DataFrames and additional metadata

Explanation:

Pandas API on Spark (previously known as Koalas) provides a pandas-like API on top of Apache Spark. It allows users to perform pandas operations on large datasets using Spark's distributed compute capabilities. Internally, it uses Spark DataFrames and adds metadata that facilitates handling operations in a pandas-like manner, ensuring compatibility and leveraging Spark's performance and scalability.

References

pandas API on Spark documentation:https://spark.apache.org/docs/latest/api/python/user_guide/pandas_on_spark/index.html

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Question # 4

Which of the Spark operations can be used to randomly split a Spark DataFrame into a training DataFrame and a test DataFrame for downstream use?

A. TrainValidationSplit

B. DataFrame.where

C. CrossValidator

D. TrainValidationSplitModel

E. DataFrame.randomSplit

Answer

E. DataFrame.randomSplit

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Question # 5

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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