This page was exported from Exam for engine [ http://blog.test4engine.com ] Export date:Mon Nov 18 3:30:30 2024 / +0000 GMT ___________________________________________________ Title: [Q37-Q61] 2024 Updated Databricks-Machine-Learning-Associate PDF for the Databricks-Machine-Learning-Associate Tests Free Updated Today! --------------------------------------------------- 2024 Updated Databricks-Machine-Learning-Associate PDF for the Databricks-Machine-Learning-Associate Tests Free Updated Today! Fully Updated Dumps PDF - Latest Databricks-Machine-Learning-Associate Exam Questions and Answers NEW QUESTION 37A 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?  3  5  6  18 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)×2 (values of Hyperparameter 2)=63 (values of Hyperparameter 1)×2 (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)×3 (folds)=186 (combinations)×3 (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.Reference:Databricks documentation on hyperparameter tuning: Hyperparameter TuningNEW QUESTION 38A 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 holdout set is not necessary when using a train-validation split  Fewer hyperparameter values need to be tested when using a train-validation split  Bias is avoidable when using a train-validation split  Reproducibility is achievable when using a train-validation split  Fewer models need to be trained when using a train-validation split NEW QUESTION 39A data scientist has a Spark DataFrame spark_df. They want to create a new Spark DataFrame that contains only the rows from spark_df where the value in column discount is less than or equal 0.Which of the following code blocks will accomplish this task?  spark_df.loc[:,spark_df[“discount”] <= 0]  spark_df[spark_df[“discount”] <= 0]  spark_df.filter (col(“discount”) <= 0)  spark_df.loc(spark_df[“discount”] <= 0, :] To filter rows in a Spark DataFrame based on a condition, the filter method is used. In this case, the condition is that the value in the “discount” column should be less than or equal to 0. The correct syntax uses the filter method along with the col function from pyspark.sql.functions.Correct code:from pyspark.sql.functions import col filtered_df = spark_df.filter(col(“discount”) <= 0) Option A and D use Pandas syntax, which is not applicable in PySpark. Option B is closer but misses the use of the col function.Reference:PySpark SQL DocumentationNEW QUESTION 40A data scientist is utilizing MLflow Autologging to automatically track their machine learning experiments. After completing a series of runs for the experiment experiment_id, the data scientist wants to identify the run_id of the run with the best root-mean-square error (RMSE).Which of the following lines of code can be used to identify the run_id of the run with the best RMSE in experiment_id?         To find the run_id of the run with the best root-mean-square error (RMSE) in an MLflow experiment, the correct line of code to use is:mlflow.search_runs( experiment_id, order_by=[“metrics.rmse”] )[“run_id”][0] This line of code searches the runs in the specified experiment, orders them by the RMSE metric in ascending order (the lower the RMSE, the better), and retrieves the run_id of the best-performing run. Option C correctly represents this logic.ReferenceMLflow documentation on tracking experiments: https://www.mlflow.org/docs/latest/python_api/mlflow.html#mlflow.search_runsNEW QUESTION 41A data scientist is using MLflow to track their machine learning experiment. As a part of each of their MLflow runs, they are performing hyperparameter tuning. The data scientist would like to have one parent run for the tuning process with a child run for each unique combination of hyperparameter values. All parent and child runs are being manually started with mlflow.start_run.Which of the following approaches can the data scientist use to accomplish this MLflow run organization?  They can turn on Databricks Autologging  They can specify nested=True when starting the child run for each unique combination of hyperparameter values  They can start each child run inside the parent run’s indented code block using mlflow.start runO  They can start each child run with the same experiment ID as the parent run  They can specify nested=True when starting the parent run for the tuning process To organize MLflow runs with one parent run for the tuning process and a child run for each unique combination of hyperparameter values, the data scientist can specify nested=True when starting the child run. This approach ensures that each child run is properly nested under the parent run, maintaining a clear hierarchical structure for the experiment. This nesting helps in tracking and comparing different hyperparameter combinations within the same tuning process.Reference:MLflow Documentation (Managing Nested Runs).NEW QUESTION 42A 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?  Change the number of compute nodes to be half or less than half of the number of evaluations.  Change the number of compute nodes and the number of evaluations to be much larger but equal.  Change the iterative optimization algorithm used to facilitate the tuning process.  Change the number of compute nodes to be double or more than double the number of evaluations. 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.Reference:Hyperparameter Optimization with HyperoptNEW QUESTION 43A machine learning engineer is using the following code block to scale the inference of a single-node model on a Spark DataFrame with one million records:Assuming the default Spark configuration is in place, which of the following is a benefit of using an Iterator?  The data will be limited to a single executor preventing the model from being loaded multiple times  The model will be limited to a single executor preventing the data from being distributed  The model only needs to be loaded once per executor rather than once per batch during the inference process  The data will be distributed across multiple executors during the inference process Using an iterator in the pandas_udf ensures that the model only needs to be loaded once per executor rather than once per batch. This approach reduces the overhead associated with repeatedly loading the model during the inference process, leading to more efficient and faster predictions. The data will be distributed across multiple executors, but each executor will load the model only once, optimizing the inference process.Reference:Databricks documentation on pandas UDFs: Pandas UDFsNEW QUESTION 44A data scientist wants to tune a set of hyperparameters for a machine learning model. They have wrapped a Spark ML model in the objective function objective_function and they have defined the search space search_space.As a result, they have the following code block:Which of the following changes do they need to make to the above code block in order to accomplish the task?  Change SparkTrials() to Trials()  Reduce num_evals to be less than 10  Change fmin() to fmax()  Remove the trials=trials argument  Remove the algo=tpe.suggest argument The SparkTrials() is used to distribute trials of hyperparameter tuning across a Spark cluster. If the environment does not support Spark or if the user prefers not to use distributed computing for this purpose, switching to Trials() would be appropriate. Trials() is the standard class for managing search trials in Hyperopt but does not distribute the computation. If the user is encountering issues with SparkTrials() possibly due to an unsupported configuration or an error in the cluster setup, using Trials() can be a suitable change for running the optimization locally or in a non-distributed manner.ReferenceHyperopt documentation: http://hyperopt.github.io/hyperopt/NEW QUESTION 45A machine learning engineer is trying to scale a machine learning pipeline by distributing its feature engineering process.Which of the following feature engineering tasks will be the least efficient to distribute?  One-hot encoding categorical features  Target encoding categorical features  Imputing missing feature values with the mean  Imputing missing feature values with the true median  Creating binary indicator features for missing values Among the options listed, calculating the true median for imputing missing feature values is the least efficient to distribute. This is because the true median requires knowledge of the entire data distribution, which can be computationally expensive in a distributed environment. Unlike mean or mode, finding the median requires sorting the data or maintaining a full distribution, which is more intensive and often requires shuffling the data across partitions.ReferenceChallenges in parallel processing and distributed computing for data aggregation like median calculation: https://www.apache.orgNEW QUESTION 46A data scientist has created a linear regression model that uses log(price) as a label variable. Using this model, they have performed inference and the predictions and actual label values are in Spark DataFrame preds_df.They are using the following code block to evaluate the model:regression_evaluator.setMetricName(“rmse”).evaluate(preds_df)Which of the following changes should the data scientist make to evaluate the RMSE in a way that is comparable with price?  They should exponentiate the computed RMSE value  They should take the log of the predictions before computing the RMSE  They should evaluate the MSE of the log predictions to compute the RMSE  They should exponentiate the predictions before computing the RMSE When evaluating the RMSE for a model that predicts log-transformed prices, the predictions need to be transformed back to the original scale to obtain an RMSE that is comparable with the actual price values. This is done by exponentiating the predictions before computing the RMSE. The RMSE should be computed on the same scale as the original data to provide a meaningful measure of error.Reference:Databricks documentation on regression evaluation: Regression EvaluationNEW QUESTION 47Which of the following machine learning algorithms typically uses bagging?  Gradient boosted trees  K-means  Random forest  Linear regression  Decision tree Random Forest is a machine learning algorithm that typically uses bagging (Bootstrap Aggregating). Bagging involves training multiple models independently on different random subsets of the data and then combining their predictions. Random Forests consist of many decision trees trained on random subsets of the training data and features, and their predictions are averaged to improve accuracy and control overfitting. This method enhances model robustness and predictive performance.Reference:Ensemble Methods in Machine Learning (Understanding Bagging and Random Forests).NEW QUESTION 48A 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 by comparing 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?  The second model is much more accurate than the first model  The data scientist failed to exponentiate the predictions in the second model prior to computing the RMSE  The data scientist failed to take the log of the predictions in the first model prior to computing the RMSE  The first model is much more accurate than the second model  The RMSE is an invalid evaluation metric for regression problems 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.Reference“Applied Predictive Modeling” by Max Kuhn and Kjell Johnson (Springer, 2013), particularly the chapters discussing model evaluation metrics.NEW QUESTION 49A data scientist is using Spark ML to engineer features for an exploratory machine learning project.They decide they want to standardize their features using the following code block:Upon code review, a colleague expressed concern with the features being standardized prior to splitting the data into a training set and a test set.Which of the following changes can the data scientist make to address the concern?  Utilize the MinMaxScaler object to standardize the training data according to global minimum and maximum values  Utilize the MinMaxScaler object to standardize the test data according to global minimum and maximum values  Utilize a cross-validation process rather than a train-test split process to remove the need for standardizing data  Utilize the Pipeline API to standardize the training data according to the test data’s summary statistics  Utilize the Pipeline API to standardize the test data according to the training data’s summary statistics To address the concern about standardizing features prior to splitting the data, the correct approach is to use the Pipeline API to ensure that only the training data’s summary statistics are used to standardize the test data. This is achieved by fitting the StandardScaler (or any scaler) on the training data and then transforming both the training and test data using the fitted scaler. This approach prevents information leakage from the test data into the model training process and ensures that the model is evaluated fairly.Reference:Best Practices in Preprocessing in Spark ML (Handling Data Splits and Feature Standardization).NEW QUESTION 50A 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?  Run each notebook interactively  Review the matrix view in the Job’s runs  Migrate the Job to a Delta Live Tables pipeline  Change each Task’s setting to use a dedicated cluster 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 is more 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.Reference:Databricks documentation on Jobs: Jobs in DatabricksNEW QUESTION 51A data scientist is wanting to explore summary statistics for Spark DataFrame spark_df. The data scientist wants to see the count, mean, standard deviation, minimum, maximum, and interquartile range (IQR) for each numerical feature.Which of the following lines of code can the data scientist run to accomplish the task?  spark_df.summary ()  spark_df.stats()  spark_df.describe().head()  spark_df.printSchema()  spark_df.toPandas() The summary() function in PySpark’s DataFrame API provides descriptive statistics which include count, mean, standard deviation, min, max, and quantiles for numeric columns. Here are the steps on how it can be used:Import PySpark: Ensure PySpark is installed and correctly configured in the Databricks environment.Load Data: Load the data into a Spark DataFrame.Apply Summary: Use spark_df.summary() to generate summary statistics.View Results: The output from the summary() function includes the statistics specified in the query (count, mean, standard deviation, min, max, and potentially quartiles which approximate the interquartile range).ReferencePySpark Documentation: https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.DataFrame.summary.htmlNEW QUESTION 52A data scientist uses 3-fold cross-validation when optimizing model hyperparameters for a regression problem. The following root-mean-squared-error values are calculated on each of the validation folds:* 10.0* 12.0* 17.0Which of the following values represents the overall cross-validation root-mean-squared error?  13.0  17.0  12.0  39.0  10.0 To calculate the overall cross-validation root-mean-squared error (RMSE), you average the RMSE values obtained from each validation fold. Given the RMSE values of 10.0, 12.0, and 17.0 for the three folds, the overall cross-validation RMSE is calculated as the average of these three values:Overall CV RMSE=10.0+12.0+17.03=39.03=13.0Overall CV RMSE=310.0+12.0+17.0=339.0=13.0 Thus, the correct answer is 13.0, which accurately represents the average RMSE across all folds.Reference:Cross-validation in Regression (Understanding Cross-Validation Metrics).NEW QUESTION 53Which of the following describes the relationship between native Spark DataFrames and pandas API on Spark DataFrames?  pandas API on Spark DataFrames are single-node versions of Spark DataFrames with additional metadata  pandas API on Spark DataFrames are more performant than Spark DataFrames  pandas API on Spark DataFrames are made up of Spark DataFrames and additional metadata  pandas API on Spark DataFrames are less mutable versions of Spark DataFrames The pandas API on Spark DataFrames are made up of Spark DataFrames with additional metadata. The pandas API on Spark aims to provide the pandas-like experience with the scalability and distributed nature of Spark. It allows users to work with pandas functions on large datasets by leveraging Spark’s underlying capabilities.Reference:Databricks documentation on pandas API on Spark: pandas API on SparkNEW QUESTION 54A machine learning engineer is trying to scale a machine learning pipeline pipeline that 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 the estimator parameter and then placing the updated cv object as the final stage of the pipeline in place of the original model.Which of the following is a negative consequence of the approach suggested by the colleague?  The model will take longer to train for each unique combination of hvperparameter values  The feature engineering stages will be computed using validation data  The cross-validation process will no longer be  The cross-validation process will no longer be reproducible  The model will be refit one more per cross-validation fold If the model object is passed to the estimator parameter of CrossValidator 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.Reference:Cross-validation and Pipeline Integration in MLlib (Avoiding Data Leakage in Pipelines).NEW QUESTION 55A data scientist has defined a Pandas UDF function predict to parallelize the inference process for a single-node model:They have written the following incomplete code block to use predict to score each record of Spark DataFrame spark_df:Which of the following lines of code can be used to complete the code block to successfully complete the task?  predict(*spark_df.columns)  mapInPandas(predict)  predict(Iterator(spark_df))  mapInPandas(predict(spark_df.columns))  predict(spark_df.columns) To apply the Pandas UDF predict to each record of a Spark DataFrame, you use the mapInPandas method. This method allows the Pandas UDF to operate on partitions of the DataFrame as pandas DataFrames, applying the specified function (predict in this case) to each partition. The correct code completion to execute this is simply mapInPandas(predict), which specifies the UDF to use without additional arguments or incorrect function calls.Reference:PySpark DataFrame documentation (Using mapInPandas with UDFs).NEW QUESTION 56The implementation of linear regression in Spark ML first attempts to solve the linear regression problem using matrix decomposition, but this method does not scale well to large datasets with a large number of variables.Which of the following approaches does Spark ML use to distribute the training of a linear regression model for large data?  Spark ML cannot distribute linear regression training  Singular value decomposition  Least-squares method  Logistic regression  Iterative optimization NEW QUESTION 57Which 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?  Keras  pandas  PvTorch  Spark ML  Scikit-learn 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.Reference:Spark MLlib documentation (Feature Engineering with Spark ML).NEW QUESTION 58A data scientist has produced three new models for a single machine learning problem. In the past, the solution used just one model. All four models have nearly the same prediction latency, but a machine learning engineer suggests that the new solution will be less time efficient during inference.In which situation will the machine learning engineer be correct?  When the new solution requires if-else logic determining which model to use to compute each prediction  When the new solution’s models have an average latency that is larger than the size of the original model  When the new solution requires the use of fewer feature variables than the original model  When the new solution requires that each model computes a prediction for every record  When the new solution’s models have an average size that is larger than the size of the original model If the new solution requires that each of the three models computes a prediction for every record, the time efficiency during inference will be reduced. This is because the inference process now involves running multiple models instead of a single model, thereby increasing the overall computation time for each record.In scenarios where inference must be done by multiple models for each record, the latency accumulates, making the process less time efficient compared to using a single model.Reference:Model Ensemble TechniquesNEW QUESTION 59Which of the following tools can be used to parallelize the hyperparameter tuning process for single-node machine learning models using a Spark cluster?  MLflow Experiment Tracking  Spark ML  Autoscaling clusters  Autoscaling clusters  Delta Lake Spark ML (part of Apache Spark’s MLlib) is designed to handle machine learning tasks across multiple nodes in a cluster, effectively parallelizing tasks like hyperparameter tuning. It supports various machine learning algorithms that can be optimized over a Spark cluster, making it suitable for parallelizing hyperparameter tuning for single-node machine learning models when they are adapted to run on Spark.ReferenceApache Spark MLlib Guide: https://spark.apache.org/docs/latest/ml-guide.html Spark ML is a library within Apache Spark designed for scalable machine learning. It provides tools to handle large-scale machine learning tasks, including parallelizing the hyperparameter tuning process for single-node machine learning models using a Spark cluster. Here’s a detailed explanation of how Spark ML can be used:Hyperparameter Tuning with CrossValidator: Spark ML includes the CrossValidator and TrainValidationSplit classes, which are used for hyperparameter tuning. These classes can evaluate multiple sets of hyperparameters in parallel using a Spark cluster.from pyspark.ml.tuning import CrossValidator, ParamGridBuilderfrom pyspark.ml.evaluation import BinaryClassificationEvaluator# Define the modelmodel = …# Create a parameter gridparamGrid = ParamGridBuilder() .addGrid(model.hyperparam1, [value1, value2]) .addGrid(model.hyperparam2, [value3, value4]) .build()# Define the evaluatorevaluator = BinaryClassificationEvaluator()# Define the CrossValidatorcrossval = CrossValidator(estimator=model,estimatorParamMaps=paramGrid,evaluator=evaluator,numFolds=3)Parallel Execution: Spark distributes the tasks of training models with different hyperparameters across the cluster’s nodes. Each node processes a subset of the parameter grid, which allows multiple models to be trained simultaneously.Scalability: Spark ML leverages the distributed computing capabilities of Spark. This allows for efficient processing of large datasets and training of models across many nodes, which speeds up the hyperparameter tuning process significantly compared to single-node computations.ReferenceApache Spark MLlib DocumentationHyperparameter Tuning in Spark MLNEW QUESTION 60Which of the following hyperparameter optimization methods automatically makes informed selections of hyperparameter values based on previous trials for each iterative model evaluation?  Random Search  Halving Random Search  Tree of Parzen Estimators  Grid Search Tree of Parzen Estimators (TPE) is a sequential model-based optimization algorithm that selects hyperparameter values based on the outcomes of previous trials. It models the probability density of good and bad hyperparameter values and makes informed decisions about which hyperparameters to try next.This approach contrasts with methods like random search and grid search, which do not use information from previous trials to guide the search process.Reference:Hyperopt and TPENEW QUESTION 61A 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 DOUBLEactual DOUBLEWhich 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?         The code block to compute the root mean-squared error (RMSE) for a linear regression model in Spark ML should use the RegressionEvaluator class with metricName set to “rmse”. Given the schema of preds_df with columns prediction and actual, the correct evaluator setup will specify predictionCol=”prediction” and labelCol=”actual”. Thus, the appropriate code block (Option C in your list) that uses RegressionEvaluator to 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.Reference:Spark ML documentation (Using RegressionEvaluator to Compute RMSE). Loading … Free Databricks-Machine-Learning-Associate Exam Questions Databricks-Machine-Learning-Associate Actual Free Exam Questions: https://www.test4engine.com/Databricks-Machine-Learning-Associate_exam-latest-braindumps.html --------------------------------------------------- Images: https://blog.test4engine.com/wp-content/plugins/watu/loading.gif https://blog.test4engine.com/wp-content/plugins/watu/loading.gif --------------------------------------------------- --------------------------------------------------- Post date: 2024-10-25 13:54:59 Post date GMT: 2024-10-25 13:54:59 Post modified date: 2024-10-25 13:54:59 Post modified date GMT: 2024-10-25 13:54:59