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Databricks-Machine-Learning-Associate Practice Questions

Question # 1
A machine learning engineer has created a Feature Table new_table using Feature Store Client fs. When creating the table, they specified a metadata description with key information about the Feature Table. They now want to retrieve that metadata programmatically. Which of the following lines of code will return the metadata description?
A. There is no way to return the metadata description programmatically.
B. fs.create_training_set("new_table")
C. fs.get_table("new_table").description
D. fs.get_table("new_table").load_df()
E. fs.get_table("new_table")


C. fs.get_table("new_table").description

Explanation:

To retrieve the metadata description of a feature table created using the Feature Store Client (referred here asfs), the correct method involves callingget_tableon thefsclient with the table name as an argument, followed by accessing thedescriptionattribute of the returned object. The code snippetfs.get_table("new_table").descriptioncorrectly achieves this by fetching the table object for "new_table" and then accessing its description attribute, where the metadata is stored. The other options do not correctly focus on retrieving the metadata description.

References:

Databricks Feature Store documentation (Accessing Feature Table Metadata).

Question # 2
A machine learning engineer has been notified that a new Staging version of a model registered to the MLflow Model Registry has passed all tests. As a result, the machine learning engineer wants to put this model into production by transitioning it to the Production stage in the Model Registry. From which of the following pages in Databricks Machine Learning can the machine learning engineer accomplish this task?
A. The home page of the MLflow Model Registry
B. The experiment page in the Experiments observatory
C. The model version page in the MLflow ModelRegistry
D. The model page in the MLflow Model Registry


C. The model version page in the MLflow ModelRegistry

Explanation:

The machine learning engineer can transition a model version to the Production stage in the Model Registry from the model version page. This page provides detailed information about a specific version of a model, including its metrics, parameters, and current stage. From here, the engineer can perform stage transitions, moving the model from Staging to Production after it has passed all necessary tests.

References

Databricks documentation on MLflow Model Registry:
https://docs.databricks.com/applications/mlflow/model-registry.html#model-version

Question # 3
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.


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


Question # 4
Which of the following evaluation metrics is not suitable to evaluate runs in AutoML experiments for regression problems?
A. F1
B. R-squared
C. MAE
D. MSE


A. F1

Explanation:

The code block provided by the machine learning engineer will perform the desired inference when the Feature Store feature set was logged with the model at model_uri. This ensures that all necessary feature transformations and metadata are available for the model to make predictions. The Feature Store in Databricks allows for seamless integration of features and models, ensuring that the required features are correctly used during inference.

References:

Databricks documentation on Feature Store: Feature Store in Databricks

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


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).


Question # 6
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


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

Question # 7
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.


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

Question # 8
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


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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