Databricks Databricks Certifications DATABRICKS-MACHINE-LEARNING-ASSOCIATE Questions & Answers

• Question 41:

  A machine learning engineer is trying to scale a machine learning pipelinepipelinethat contains multiple feature engineering stages and a modeling stage. As part of the cross-validation process, they are using the following code block:

  

  A colleague suggests that the code block can be changed to speed up the tuning process by passing the model object to theestimatorparameter and then placing the updated cv object as the final stage of thepipelinein place of the original model.

  Which of the following is a negative consequence of the approach suggested by the colleague?

  A. The model will take longerto train for each unique combination of hvperparameter values

  B. The feature engineering stages will be computed using validation data

  C. The cross-validation process will no longer be

  D. The cross-validation process will no longer be reproducible

  E. The model will be refit one more per cross-validation fold

  Correct Answer: B

  If the model object is passed to the estimator parameter ofCrossValidator and the cross-validation object itself is placed as a stage in the pipeline, the feature engineering stages within the pipeline would be applied separately to each training

  and validation fold during cross-validation. This leads to a significant issue: the feature engineering stages would be computed using validation data, thereby leaking information from the validation set into the training process. This would

  potentially invalidate the cross-validation results by giving an overly optimistic performance estimate.References:

  Cross-validation and Pipeline Integration in MLlib (Avoiding Data Leakage in Pipelines).

• Question 42:

  A data scientist has developed a linear regression model using Spark ML and computed the predictions in a Spark DataFrame preds_df with the following schema:

  prediction DOUBLE actual DOUBLE Which of the following code blocks can be used to compute the root mean-squared-error of the model according to the data in preds_df and assign it to the rmse variable?

  

  A. Option A

  B. Option B

  C. Option C

  D. Option D

  E. Option E

  Correct Answer: C

  The code block to compute the root mean-squared error (RMSE) for a linear regression model in Spark ML should use theRegressionEvaluatorclass withmetricNameset to "rmse". Given the schema ofpreds_dfwith

  columnspredictionandactual, the correct evaluator setup will specifypredictionCol="prediction"andlabelCol="actual". Thus, the appropriate code block (Option C in your list) that usesRegressionEvaluatorto compute the RMSE is the correct

  choice. This setup correctly measures the performance of the regression model using the predictions and actual outcomes from the DataFrame.

  References:

  Spark ML documentation (Using RegressionEvaluator to Compute RMSE).

• Question 43:

  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

  Correct Answer: B

  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 44:

  A data scientist wants to use Spark ML to impute missing values in their PySpark DataFrame features_df. They want to replace missing values in all numeric columns in features_df with each respective numeric column's median value.

  They have developed the following code block to accomplish this task:

  

  The code block is not accomplishing the task.

  Which reasons describes why the code block is not accomplishing the imputation task?

  A. It does not impute both the training and test data sets.

  B. The inputCols and outputCols need to be exactly the same.

  C. The fit method needs to be called instead of transform.

  D. It does not fit the imputer on the data to create an ImputerModel.

  Correct Answer: D

  In the provided code block, theImputerobject is created but not fitted on the data to generate anImputerModel. Thetransformmethod is being called directly on the Imputerobject, which does not yet contain the fitted median values needed for imputation. The correct approach is to fit the imputer on the dataset first. Corrected code: imputer = Imputer( strategy="median", inputCols=input_columns, outputCols=output_columns ) imputer_model = imputer.fit(features_df)# Fit the imputer to the dataimputed_features_df = imputer_model.transform(features_df)# Transform the data using the fitted imputer References: PySpark ML Documentation

• Question 45:

  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.

  Correct Answer: C

  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 46:

  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")

  Correct Answer: C

  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

  thedescription attribute of the returned object. The code snippetfs.get_table("new_table").description correctly 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 47:

  A data scientist has been given an incomplete notebook from the data engineering team. The notebook uses a Spark DataFrame spark_df on which the data scientist needs to perform further feature engineering. Unfortunately, the data scientist has not yet learned the PySpark DataFrame API.

  Which of the following blocks of code can the data scientist run to be able to use the pandas API on Spark?

  A. import pyspark.pandas as ps df = ps.DataFrame(spark_df)

  B. import pyspark.pandas as ps df = ps.to_pandas(spark_df)

  C. spark_df.to_sql()

  D. import pandas as pd df = pd.DataFrame(spark_df)

  E. spark_df.to_pandas()

  Correct Answer: A

  To use the pandas API on Spark, which is designed to bridge the gap between the simplicity of pandas and the scalability of Spark, the correct approach involves importing the pyspark.pandas (recently renamed topandas_api_on_spark)

  module and converting a Spark DataFrame to a pandas-on-Spark DataFrame using this API. The provided syntax correctly initializes a pandas-on-Spark DataFrame, allowing the data scientist to work with the familiar pandas-like API on large

  datasets managed by Spark.

  References:

  Pandas API on Spark

  Documentation:https://spark.apache.org/docs/latest/api/python/user_guide/pandas _on_spark/index.html

• Question 48:

  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

  E. pandas API on Spark DataFrames are unrelated to Spark DataFrames

  Correct Answer: C

  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

• Question 49:

  A data scientist learned during their training to always use 5-fold cross-validation in their model development workflow. A colleague suggests that there are cases where a train-validation split could be preferred over k-fold cross-validation

  when k > 2.

  Which of the following describes a potential benefit of using a train-validation split over k-fold cross-validation in this scenario?

  A. A holdout set is not necessary when using a train-validation split

  B. Reproducibility is achievable when using a train-validation split

  C. Fewer hyperparameter values need to be tested when usinga train-validation split

  D. Bias is avoidable when using a train-validation split

  E. Fewer models need to be trained when using a train-validation split

  Correct Answer: E

  A train-validation split is often preferred over k-fold cross-validation (with k >

  2) when computational efficiency is a concern. With a train-validation split, only two models (one on the training set and one on the validation set) are trained, whereas k-fold cross-validation requires training k models (one for each fold). This

  reduction in the number of models trained can save significant computational resources and time, especially when dealing with large datasets or complex models.

  References:

  Model Evaluation with Train-Test Split

• Question 50:

  A data scientist uses 3-fold cross-validation and the following hyperparameter grid when optimizing model hyperparameters via grid search for a classification problem:

  Hyperparameter 1: [2, 5, 10] Hyperparameter 2: [50, 100]

  Which of the following represents the number of machine learning models that can be trained in parallel during this process?

  A. 3

  B. 5

  C. 6

  D. 18

  Correct Answer: D

  To determine the number of machine learning models that can be trained in parallel, we need to calculate the total number of combinations of hyperparameters. The given hyperparameter grid includes:

  Hyperparameter 1: [2, 5, 10] (3 values)

  Hyperparameter 2: [50, 100] (2 values)

  The total number of combinations is the product of the number of values for each hyperparameter:3 (values of Hyperparameter 1)? (values of Hyperparameter 2)=63 (value s of Hyperparameter 1)? (values of Hyperparameter 2)=6 With 3-fold

  cross-validation, each combination of hyperparameters will be evaluated 3 times. Thus, the total number of models trained will

  be:6 (combinations)? (folds)=186 (combinations)? (folds)=18 However, the number of models that can be trained in parallel is equal to the number of hyperparameter combinations, not the total number of models considering cross-validation.

  Therefore, 6 models can be trained in parallel.

  References:

  Databricks documentation on hyperparameter tuning: Hyperparameter Tuning