ML-ASSOC practice questions 21–30

1. Question 21 · 45 in bank

   A 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?

   • AOne-hot encoding categorical features
   • BTarget encoding categorical features
   • CImputing missing feature values with the mean
   • DImputing missing feature values with the true median
   • ECreating binary indicator features for missing values

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   Answer: D

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2. Question 22 · 45 in bank

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

   • AThe vectorized pandas UDFs allow for the use of type hints
   • BThe vectorized pandas UDFs process data in batches rather than one row at a time
   • CThe vectorized pandas UDFs allow for pandas API use inside of the function
   • DThe vectorized pandas UDFs work on distributed DataFrames
   • EThe vectorized pandas UDFs process data in memory rather than spilling to disk

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   Answer: B

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3. Question 23 · 45 in bank

   A 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?

   • AChange SparkTrials() to Trials()
   • BReduce num_evals to be less than 10
   • CChange fmin() to fmax()
   • DRemove the trials=trials argument
   • ERemove the algo=tpe.suggest argument

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   Answer: A

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4. Question 24 · 45 in bank

   A machine learning engineer would like to develop a linear regression model with Spark ML to predict the price of a hotel room. They are using the Spark DataFrame train_df to train the model. The Spark DataFrame train_df has the following schema:

   

   The machine learning engineer shares the following code block:

   

   Which of the following changes does the machine learning engineer need to make to complete the task?

   • AThey need to call the transform method on train_df
   • BThey need to convert the features column to be a vector
   • CThey do not need to make any changes
   • DThey need to utilize a Pipeline to fit the model
   • EThey need to split the features column out into one column for each feature

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   Answer: B

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5. Question 25 · 45 in bank

   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?

   • AKeras
   • Bpandas
   • CPyTorch
   • DSpark ML
   • EScikit-learn

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   Answer: D

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6. Question 26 · 45 in bank

   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//IMG//
   • B//IMG//
   • C//IMG//
   • D//IMG//
   • E//IMG//

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   Answer: B

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7. Question 27 · 45 in bank

   Which of the following statements describes a Spark ML estimator?

   • AAn estimator is a hyperparameter grid that can be used to train a model
   • BAn estimator chains multiple algorithms together to specify an ML workflow
   • CAn estimator is a trained ML model which turns a DataFrame with features into a DataFrame with predictions
   • DAn estimator is an algorithm which can be fit on a DataFrame to produce a Transformer
   • EAn estimator is an evaluation tool to assess to the quality of a model

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   Answer: D

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8. Question 28 · 45 in bank

   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?

   • Aimport pyspark.pandas as ps df = ps.DataFrame(spark_df)
   • Bimport pyspark.pandas as ps df = ps.to_pandas(spark_df)
   • Cspark_df.to_sql()
   • Dimport pandas as pd df = pd.DataFrame(spark_df)
   • Espark_df.to_pandas()

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   Answer: A

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9. Question 29 · 45 in bank

   The 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?

   • ALogistic regression
   • BSpark ML cannot distribute linear regression training
   • CIterative optimization
   • DLeast-squares method
   • ESingular value decomposition

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   Answer: C

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10. Question 30 · 45 in bank

    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?

    • ASpark ML decision trees test every feature variable in the splitting algorithm
    • BSpark ML decision trees automatically prune overfit trees
    • CSpark ML decision trees test more split candidates in the splitting algorithm
    • DSpark ML decision trees test a random sample of feature variables in the splitting algorithm
    • ESpark ML decision trees test binned features values as representative split candidates

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    Answer: E

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ML-ASSOC practice, question 21. 45 questions available.