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Databricks-Machine-Learning-Associate Practice Questions

Question # 1
A machine learning engineering team has a Job with three successive tasks. Each task runs a single notebook. The team has been alerted that the Job has failed in its latest run. Which of the following approaches can the team use to identify which task is the cause of the failure?
A. Run each notebook interactively
B. Review the matrix view in the Job's runs
C. Migrate the Job to a Delta Live Tables pipeline
D. Change each Task’s setting to use a dedicated cluster


B. Review the matrix view in the Job's runs

Explanation:

To identify which task is causing the failure in the job, the team should review the matrix view in the Job's runs. The matrix view provides a clear and detailed overview of each task's status, allowing the team to quickly identify which task failed. This approach ismore efficient than running each notebook interactively, as it provides immediate insights into the job's execution flow and any issues that occurred during the run.

References:

Databricks documentation on Jobs: Jobs in Databricks


Question # 2
A data scientist is wanting to explore the Spark DataFrame spark_df. The data scientist wants visual histograms displaying the distribution of numeric features to be included in the exploration. Which of the following lines of code can the data scientist run to accomplish the task?
A. spark_df.describe()
B. dbutils.data(spark_df).summarize()
C. This task cannot be accomplished in a single line of code.
D. spark_df.summary()
E. dbutils.data.summarize (spark_df)


E. dbutils.data.summarize (spark_df)

Explanation:

To display visual histograms and summaries of the numeric features in a Spark DataFrame, the Databricks utility functiondbutils.data.summarizecan be used. This function provides a comprehensive summary, including visual histograms.

Correct code:

dbutils.data.summarize(spark_df)

Other options likespark_df.describe()andspark_df.summary()provide textual statistical summaries but do not include visual histograms.

References:

Databricks Utilities Documentation

Question # 3
A data scientist has developed a machine learning pipeline with a static input data set using Spark ML, but the pipeline is taking too long to process. They increase the number of workers in the cluster to get the pipeline to run more efficiently. They notice that the number of rows in the training set after reconfiguring the cluster is different from the number of rows in the training set prior to reconfiguring the cluster. Which of the following approaches will guarantee a reproducible training and test set for each model?
A. Manually configure the cluster
B. Write out the split data sets to persistent storage
C. Set a speed in the data splitting operation
D. Manually partition the input data


B. Write out the split data sets to persistent storage

Explanation:

To ensure reproducible training and test sets, writing the split data sets to persistent storage is a reliable approach. This allows you to consistently load the same training and test data for each model run, regardless of cluster reconfiguration or other changes in the environment.

Correct approach:

Split the data.

Write the split data to persistent storage (e.g., HDFS, S3).

Load the data from storage for each model training session.

train_df, test_df = spark_df.randomSplit([0.8,0.2], seed=42)

train_df.write.parquet("path/to/train_df.parquet") test_df.write.parquet("path/to/test_df.parquet")# Later, load the datatrain_df = spark.read.parquet("path/to/train_df.parquet") test_df = spark.read.parquet("path/to/test_df.parquet")

References:

Spark DataFrameWriter Documentation

Question # 4
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 # 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.


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 # 6
Which of the following machine learning algorithms typically uses bagging?
A. IGradient boosted trees
B. K-means
C. Random forest
D. Decision tree


C. Random forest

Explanation:

Random Forest is a machine learning algorithm that typically uses bagging (Bootstrap Aggregating). Bagging is a technique that involves training multiple base models (such as decision trees) on different subsets of the data and then combining their predictions to improve overall model performance. Each subset is created by randomly sampling with replacement from the original dataset. The Random Forest algorithm builds multiple decision trees and merges them to get a more accurate and stable prediction.

References:

Databricks documentation on Random Forest: Random Forest in Spark ML


Question # 7
Which of the following tools can be used to distribute large-scale feature engineering without the use of a UDF or pandas Function API for machine learning pipelines?
A. Keras
B. pandas
C. PvTorch
D. Spark ML
E. Scikit-learn


D. Spark ML

Explanation:

Spark ML (Machine Learning Library) is designed specifically for handling large-scale data processing and machine learning tasks directly within Apache Spark. It provides tools and APIs for large-scale feature engineering without the need to rely on user-defined functions (UDFs) or pandas Function API, allowing for more scalable and efficient data transformations directly distributed across a Spark cluster. Unlike Keras, pandas, PyTorch, and scikit-learn, Spark ML operates natively in a distributed environment suitable for big data scenarios.

References:

Spark MLlib documentation (Feature Engineering with Spark ML).


Question # 8
A data scientist has created two linear regression models. The first model uses price as a label variable and the second model uses log(price) as a label variable. When evaluating the RMSE of each model bycomparing the label predictions to the actual price values, the data scientist notices that the RMSE for the second model is much larger than the RMSE of the first model. Which of the following possible explanations for this difference is invalid?
A. The second model is much more accurate than the first model
B. The data scientist failed to exponentiate the predictions in the second model prior tocomputingthe RMSE
C. The datascientist failed to take the logof the predictions in the first model prior to computingthe RMSE
D. The first model is much more accurate than the second model
E. The RMSE is an invalid evaluation metric for regression problems


E. The RMSE is an invalid evaluation metric for regression problems

Explanation:

The Root Mean Squared Error (RMSE) is a standard and widely used metric for evaluating the accuracy of regression models. The statement that it is invalid is incorrect. Here’s a breakdown of why the other statements are or are not valid:

Transformations and RMSE Calculation:If the model predictions were transformed (e.g., using log), they should be converted back to their original scale before calculating RMSE to ensure accuracy in the evaluation. Missteps in this conversion process can lead to misleading RMSE values.

Accuracy of Models:Without additional information, we can't definitively say which model is more accurate without considering their RMSE values properly scaled back to the original price scale. Appropriateness of RMSE:RMSE is entirely valid for regression problems as it provides a measure of how accurately a model predicts the outcome, expressed in the same units as the dependent variable.

References

"Applied Predictive Modeling" by Max Kuhn and Kjell Johnson (Springer, 2013), particularly the chapters discussing model evaluation metrics.


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