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Databricks-Machine-Learning-Associate Practice Questions

Question # 1
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 # 2
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 # 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 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 # 5
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 # 6
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 # 7
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 # 8
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

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