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

• Question 51:

  Which of the following is a benefit of using vectorized pandas UDFs instead of standard PySpark UDFs?

  A. The vectorized pandas UDFs allow for the use of type hints

  B. The vectorized pandas UDFs process data in batches rather than one row at a time

  C. The vectorized pandas UDFs allow for pandas API use inside of the function

  D. The vectorized pandas UDFs work on distributed DataFrames

  E. The vectorized pandas UDFs process data in memory rather than spilling to disk

  Correct Answer: B

  Vectorized pandas UDFs, also known as Pandas UDFs, are a powerful feature in PySpark that allows for more efficient operations than standard UDFs. They operate by processing data in batches, utilizing vectorized operations that leverage pandas to perform operations on whole batches of data at once. This approach is much moreefficient than processing data row by row as is typical with standard PySpark UDFs, which can significantly speed up the computation. References: PySpark Documentation on UDFs:https://spark.apache.org/docs/latest/api/python/user_guide/sql/arrow_panda s.html#pandas-udfs-a-k-a-vectorized-udfs

• Question 52:

  A 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 anIterator?

  A. The data will be limited to a single executor preventing the model from being loaded multiple times

  B. The model will be limited to a single executor preventing the data from being distributed

  C. The model only needs to be loaded once per executor rather than once per batch during the inference process

  D. The data will be distributed across multiple executors during the inference process

  Correct Answer: C

  Using an iterator in thepandas_udfensures 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.

  References:

  Databricks documentation on pandas UDFs: Pandas UDFs

• Question 53:

  Which statement describes a Spark ML transformer?

  A. A transformer is an algorithm which can transform one DataFrame into another DataFrame

  B. A transformer is a hyperparameter grid that can be used to train a model

  C. A transformer chains multiple algorithms together to transform an ML workflow

  D. A transformer is a learning algorithm that can use a DataFrame to train a model

  Correct Answer: A

  In Spark ML, a transformer is an algorithm that can transform one DataFrame into another DataFrame. It takes a DataFrame as input and produces a new DataFrame as output. This transformation can involve adding new columns, modifying

  existing ones, or applying feature transformations. Examples of transformers in Spark MLlib include feature transformers likeStringIndexer,VectorAssembler, andStandardScaler.

  References:

  Databricks documentation on transformers: Transformers in Spark ML

• Question 54:

  A machine learning engineer wants to parallelize the inference of group-specific models using the Pandas Function API. They have developed theapply_modelfunction that will look up and load the correct model for each group, and they want to apply it to each group of DataFramedf.

  They have written the following incomplete code block:

  

  Which piece of code can be used to fill in the above blank to complete the task?

  A. applyInPandas

  B. groupedApplyInPandas

  C. mapInPandas

  D. predict

  Correct Answer: A

  To parallelize the inference of group-specific models using the Pandas Function API in PySpark, you can use theapplyInPandasfunction. This function allows you to apply a Python function on each group of a DataFrame and return a

  DataFrame, leveraging the power of pandas UDFs (user-defined functions) for better performance.

  prediction_df = ( df.groupby("device_id") .applyInPandas(apply_model, schema=apply_return_schema) )

  In this code:

  groupby("device_id"): Groups the DataFrame by the "device_id" column. applyInPandas(apply_model, schema=apply_return_schema): Applies theapply_modelfunction to each group and specifies the schema of the return DataFrame.

  References:

  PySpark Pandas UDFs Documentation

• Question 55:

  Which of the following tools can be used to parallelize the hyperparameter tuning process for single-node machine learning models using a Spark cluster?

  A. MLflow Experiment Tracking

  B. Spark ML

  C. Autoscaling clusters

  D. Autoscaling clusters

  E. Delta Lake

  Correct Answer: B

  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.

  References:

  Apache 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 theCrossValidatorandTrainValidationSplitclasses, 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, ParamGridBuilder from pyspark.ml.evaluation import BinaryClassificationEvaluator

  # Define the model

  model = ...

  # Create a parameter grid

  paramGrid = ParamGridBuilder() \

  addGrid(model.hyperparam1, [value1, value2]) \

  addGrid(model.hyperparam2, [value3, value4]) \

  build()

  # Define the evaluator

  evaluator = BinaryClassificationEvaluator()

  # Define the CrossValidator

  crossval = 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.

  References:

  Apache Spark MLlib Documentation

  Hyperparameter Tuning in Spark ML

• Question 56:

  A data scientist has created a linear regression model that useslog(price)as a label variable. Using this model, they have performed inference and the predictions and actual label values are in Spark DataFramepreds_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 withprice?

  A. They should exponentiate the computed RMSE value

  B. They should take the log of the predictions before computing the RMSE

  C. They should evaluate the MSE of the log predictions to compute the RMSE

  D. They should exponentiate the predictions before computing the RMSE

  Correct Answer: D

  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.

  References:

  Databricks documentation on regression evaluation: Regression Evaluation

• Question 57:

  A 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?

  A. spark_df.summary ()

  B. spark_df.stats()

  C. spark_df.describe().head()

  D. spark_df.printSchema()

  E. spark_df.toPandas()

  Correct Answer: A

  Thesummary()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:Usespark_df.summary()to generate summary statistics. View Results:The output from thesummary()function includes the statistics specified in the query (count, mean, standard deviation, min, max, and potentially quartiles

  which approximate the interquartile range).

  References:

  PySpark

  Documentation:https://spark.apache.org/docs/latest/api/python/reference/api/pysp ark.sql.DataFrame.summary.html

• Question 58:

  A 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:

  1.

  10.0

  2.

  12.0

  3.

  17.0

  Which of the following values represents the overall cross-validation root-mean-squared error?

  A. 13.0

  B. 17.0

  C. 12.0

  D. 39.0

  E. 10.0

  Correct Answer: A

  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.References:

  Cross-validation in Regression (Understanding Cross-Validation Metrics).

• Question 59:

  A machine learning engineer has identified the best run from an MLflow Experiment. They have stored the run ID in the run_id variable and identified the logged model name as "model". They now want to register that model in the MLflow Model Registry with the name "best_model".

  Which lines of code can they use to register the model associated with run_id to the MLflow Model Registry?

  A. mlflow.register_model(run_id, "best_model")

  B. mlflow.register_model(f"runs:/{run_id}/model", "best_model")

  C. millow.register_model(f"runs:/{run_id)/model")

  D. mlflow.register_model(f"runs:/{run_id}/best_model", "model")

  Correct Answer: B

  To register a model that has been identified by a specific run_id in the MLflow Model Registry, the appropriate line of code is:

  mlflow.register_model(f"runs:/{run_id}/model","best_model") This code correctly specifies the path to the model within the run (runs:/{run_id}/model) and registers it under the name "best_model" in the Model Registry. This allows the model to

  be tracked, managed, and transitioned through different stages (e.g., Staging, Production) within the MLflow ecosystem.

  References:

  MLflow documentation on model registry:

  https://www.mlflow.org/docs/latest/model-registry.html#registering-a-model

• Question 60:

  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

  Correct Answer: A

  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